Patent Publication Number: US-2023145084-A1

Title: Artificial immunohistochemical image systems and methods

Description:
CROSS-REFERENCE TO RELATED APPLICATIONS 
     This application is a continuation of U.S. application Ser. No. 17/301,975, filed Apr. 20, 2021, which claims the benefit of U.S. Application 63/012,885, filed Apr. 20, 2020. Each application is incorporated herein by reference in its entirety. 
    
    
     BACKGROUND 
     Patient-derived tumor organoid (TO) technologies have been used to create cellular models of diverse cancer types, including colon, breast, pancreatic, liver, lung, endometrial, prostate, and esophagogastric, among others. In addition to advancing fundamental research, TOs have recently been employed for drug development and precision medicine studies. 
     Tumor organoids can be used to model cancer growth and estimate the effectiveness of different therapies in stopping cancer growth. To monitor the growth of tumor organoids before, during, and after exposure to various anti-cancer therapies, the tumor organoids can be imaged to detect cell death and/or viable cells in a cell culture plate. 
     Some methods for detecting dead cells or viable cells can include the use of a fluorescent signal, which can be detected by fluorescent microscopy. Fluorescent dyes can be applied to the tumor organoids in order to highlight certain characteristics in the cells and/or make the characteristics easier to detect. The cells can then be imaged using a technique such as fluorescent microscopy. However, fluorescent microscopy can be very time consuming, and the fluorescent dyes used can be toxic to cells, which can artificially inflate the amount of observed cell death that may be falsely attributed to the anti-cancer therapy being tested. 
     Accordingly, there is a need in the art to automatically analyze tumor organoids and other cellular compositions without the use of fluorescent dyes and/or fluorescent microscopy. 
     SUMMARY OF DISCLOSURE 
     Disclosed herein are systems, methods, and mechanisms useful for automatically analyzing tumor organoid and other cellular composition images. In particular, the disclosure provides systems, methods, and mechanisms for generating images of cellular compositions, such as tumor organoids, that approximate fluorescent staining techniques using only raw brightfield images of tumor organoids. 
     In accordance with some embodiments of the disclosed subject matter, a method of generating an artificial fluorescent image of cells is provided. The method includes receiving a brightfield image generated by a brightfield microscopy imaging modality of at least a portion of cells included in a specimen, applying, to the brightfield image, at least one trained model, the trained model being trained to generate the artificial fluorescent image based on the brightfield image, receiving the artificial fluorescent image from the trained model. 
     In accordance with some embodiments of the disclosed subject matter, an organoid analysis system including at least one processor and at least one memory is provided. The system is configured to receive a brightfield image generated by a brightfield microscopy imaging modality from at least a portion of cells included in a specimen, apply, to the brightfield image, at least one model trained to generate an artificial fluorescent image based on the brightfield image, the artificial fluorescent image being indicative of whether the cells included in the tumor organoids are alive or dead, and output the artificial fluorescent image to at least one of a memory or a display. 
     In accordance with some embodiments of the disclosed subject matter, a method of generating an artificial fluorescent image without a fluorescent stain is provided. The method includes receiving a brightfield image generated by a brightfield microscopy imaging modality from at least a portion of cells included in a specimen, applying, to the brightfield image, at least one model trained to generate an artificial fluorescent image based on the brightfield image, the artificial fluorescent image being indicative of whether the cells included in the tumor organoids are alive or dead, and generating a report based on the artificial fluorescent image. 
    
    
     
       BRIEF DESCRIPTION OF DRAWINGS 
       Petition for color: N/A 
         FIG.  1    shows an example of a system for automatically analyzing tumor organoid images. 
         FIG.  2    shows an example of hardware that can be used in some embodiments of the system. 
         FIG.  3    shows an exemplary flow that can generate brightfield images and/or fluorescent images, as well as live/dead assays readouts, using patient derived organoids grown from tumor specimens. 
         FIG.  4    shows an exemplary flow for training a generator to generate an artificial fluorescent image based on an input brightfield image of organoid cells. 
         FIG.  5    shows an exemplary flow for generating an artificial fluorescent image. 
         FIG.  6    shows an exemplary neural network. 
         FIG.  7    shows an exemplary discriminator. 
         FIG.  8    shows an exemplary process that can train a model to generate an artificial fluorescent stain image of one or more organoids based on an input brightfield image. 
         FIG.  9    shows an exemplary process that can generate an artificial fluorescent image of one or more organoids based on a brightfield image. 
         FIG.  10    shows exemplary raw images before preprocessing and after preprocessing. 
         FIG.  11    shows an exemplary flow for culturing tumor organoids. Culture of patient derived tumor organoids. 
         FIG.  12    shows an exemplary flow for conducting drug screens in accordance with systems and methods described herein. 
         FIG.  13    shows an exemplary process that can generate artificial fluorescent images at multiple time points for at least one organoid. 
         FIG.  14    shows a table representing an exemplary assay or well plate arrangement. 
         FIG.  15    shows an example of images generated using a single neural network model and a three neural network model. 
         FIG.  16    shows a flow for generating an artificial fluorescent image using a first trained model and a second trained model. 
         FIG.  17    shows a process for generating fluorescent images of tumor organoids. 
         FIG.  18    shows a flow for predicting a viability based on a brightfield image. 
         FIG.  19    shows an exemplary generator and an exemplary discriminator. 
         FIG.  20    shows a discriminator that can generate a viability prediction based on a brightfield image and an artificial fluorescent image. 
         FIG.  21    shows a process for generating a viability value. 
     
    
    
     DETAILED DESCRIPTION 
     The various aspects of the subject disclosure are now described with reference to the drawings. It should be understood, however, that the drawings and detailed description hereafter relating thereto are not intended to limit the claimed subject matter to the particular form disclosed. Rather, the intention is to cover all modifications, equivalents, and alternatives falling within the spirit and scope of the claimed subject matter. 
     In the following detailed description, reference is made to the accompanying drawings which form a part hereof, and in which is shown by way of illustration, specific embodiments in which the disclosure may be practiced. These embodiments are described in sufficient detail to enable those of ordinary skill in the art to practice the disclosure. It should be understood, however, that the detailed description and the specific examples, while indicating examples of embodiments of the disclosure, are given by way of illustration only and not by way of limitation. From this disclosure, various substitutions, modifications, additions, rearrangements, or combinations thereof within the scope of the disclosure may be made and will become apparent to those of ordinary skill in the art. 
     In accordance with common practice, the various features illustrated in the drawings may not be drawn to scale. The illustrations presented herein are not meant to be actual views of any particular method, device, or system, but are merely idealized representations that are employed to describe various embodiments of the disclosure. Accordingly, the dimensions of the various features may be arbitrarily expanded or reduced for clarity. In addition, some of the drawings may be simplified for clarity. Thus, the drawings may not depict all of the components of a given apparatus (e.g., device) or method. In addition, like reference numerals may be used to denote like features throughout the specification and figures. 
     Information and signals described herein may be represented using any of a variety of different technologies and techniques. For example, data, instructions, commands, information, signals, bits, symbols, and chips that may be referenced throughout the above description may be represented by voltages, currents, electromagnetic waves, magnetic fields or particles, optical fields or particles, or any combination thereof. Some drawings may illustrate signals as a single signal for clarity of presentation and description. It will be understood by a person of ordinary skill in the art that the signal may represent a bus of signals, wherein the bus may have a variety of bit widths and the disclosure may be implemented on any number of data signals including a single data signal. 
     The various illustrative logical blocks, modules, circuits, and algorithm acts described in connection with embodiments disclosed herein may be implemented as electronic hardware, computer software, or combinations of both. To clearly illustrate this interchangeability of hardware and software, various illustrative components, blocks, modules, circuits, and acts are described generally in terms of their functionality. Whether such functionality is implemented as hardware or software depends upon the particular application and design constraints imposed on the overall system. Skilled artisans may implement the described functionality in varying ways for each particular application, but such implementation decisions should not be interpreted as causing a departure from the scope of the embodiments of the disclosure described herein. 
     In addition, it is noted that the embodiments may be described in terms of a process that is depicted as a flowchart, a flow diagram, a structure diagram, or a block diagram. Although a flowchart may describe operational acts as a sequential process, many of these acts can be performed in another sequence, in parallel, or substantially concurrently. In addition, the order of the acts may be re-arranged. A process may correspond to a method, a function, a procedure, a subroutine, a subprogram, etc. Furthermore, the methods disclosed herein may be implemented in hardware, software, or both. If implemented in software, the functions may be stored or transmitted as one or more instructions or code on a computer-readable medium. Computer-readable media includes both computer storage media and communication media including any medium that facilitates transfer of a computer program from one place to another. 
     It should be understood that any reference to an element herein using a designation such as “first,” “second,” and so forth does not limit the quantity or order of those elements, unless such limitation is explicitly stated. Rather, these designations may be used herein as a convenient method of distinguishing between two or more elements or instances of an element. Thus, a reference to first and second elements does not mean that only two elements may be employed there or that the first element must precede the second element in some manner. Also, unless stated otherwise a set of elements may comprise one or more elements. 
     As used herein, the terms “component,” “system” and the like are intended to refer to a computer-related entity, either hardware, a combination of hardware and software, software, or software in execution. For example, a component may be, but is not limited to being, a process running on a processor, a processor, an object, an executable, a thread of execution, a program, and/or a computer. By way of illustration, both an application running on a computer and the computer can be a component. One or more components may reside within a process and/or thread of execution and a component may be localized on one computer and/or distributed between two or more computers or processors. 
     The word “exemplary” is used herein to mean serving as an example, instance, or illustration. Any aspect or design described herein as “exemplary” is not necessarily to be construed as preferred or advantageous over other aspects or designs. 
     Furthermore, the disclosed subject matter may be implemented as a system, method, apparatus, or article of manufacture using standard programming and/or engineering techniques to produce software, firmware, hardware, or any combination thereof to control a computer or processor based device to implement aspects detailed herein. The term “article of manufacture” (or alternatively, “computer program product”) as used herein is intended to encompass a computer program accessible from any computer-readable device, carrier, or media. For example, computer readable media can include but are not limited to magnetic storage devices (e.g., hard disk, floppy disk, magnetic strips, etc.), optical disks (e.g., compact disk (CD), digital versatile disk (DVD), etc.), smart cards, and flash memory devices (e.g., card, stick). 
     Additionally, it should be appreciated that a carrier wave can be employed to carry computer-readable electronic data such as those used in transmitting and receiving electronic mail or in accessing a network such as the Internet or a local area network (LAN). Of course, those skilled in the art will recognize many modifications may be made to this configuration without departing from the scope or spirit of the claimed subject matter. 
     As used herein the terms “biological specimen,” “patient sample,” and “sample” refer to a specimen collected from a patient. Such samples include, without limitation, tumors, biopsies, tumor organoids, other tissues, and bodily fluids. Suitable bodily fluids include, for example, blood, serum, plasma, sputum, lavage fluid, cerebrospinal fluid, urine, semen, sweat, tears, saliva, and the like. Samples may be collected, for example, via a biopsy, swab, or smear. 
     The terms “extracted”, “recovered,” “isolated,” and “separated,” refer to a compound, (e.g., a protein, cell, nucleic acid or amino acid) that has been removed from at least one component with which it is naturally associated and found in nature. 
     The terms “enriched” or “enrichment” herein refer to the process of amplifying nucleic acids contained in a sample. Enrichment can be sequence specific or nonspecific (i.e., involving any of the nucleic acids present in a sample). 
     As used herein, “cancer” shall be taken to mean any one or more of a wide range of benign or malignant tumors, including those that are capable of invasive growth and metastases through a human or animal body or a part thereof, such as, for example, via the lymphatic system and/or the blood stream. As used herein, the term “tumor” includes both benign and malignant tumors and solid growths. Typical cancers include but are not limited to carcinomas, lymphomas, or sarcomas, such as, for example, ovarian cancer, colon cancer, breast cancer, pancreatic cancer, lung cancer, prostate cancer, urinary tract cancer, uterine cancer, acute lymphatic leukemia, Hodgkin&#39;s disease, small cell carcinoma of the lung, melanoma, neuroblastoma, glioma, and soft tissue sarcoma of humans. 
     Fluorescence microscopy is commonly used to detect the presence of specific molecules in a sample. For example, in cell biology, fluorescence microscopy can be used to highlight a specific cellular component (e.g., an organelle) or detect a molecular marker that is indicative of a particular cellular state (e.g., apoptosis, differentiation, or activation of a cell signaling pathway). However, there are several drawbacks that limit the use of fluorescence microscopy. First, use of this technique requires additional time, labor, and reagents (e.g., stains) as compared to transmitted light microscopy, making it a costly bottleneck in high-throughput screening processes. Second, some fluorescent dyes are toxic to cells and can bias the results of certain experiments (e.g., quantification of cell death). Further, cells that were damaged by these dyes can no longer be used in an ongoing experiment, so a greater quantity of cells is required for experiments that involve assaying cells at multiple time points. Third, the time over which a sample can be observed using fluorescence microscopy is limited by photobleaching, a process in which fluorophores lose their ability to fluoresce as they are illuminated. 
     Fortunately, methods based on transmitted light microscopy largely avoid these problems, as they are relatively fast and inexpensive to use and can capture multiple images of the same living samples at several time points. The term “transmitted light microscopy” is used to refer to any type of microscopy where the light passes from the source to the opposite side of the lens. The simplest of these methods is brightfield microscopy, in which samples are illuminated from below with white light and the transmitted light is observed from above. Use of a standard brightfield microscope is somewhat limited for biological samples that have low contrast. For instance, without the use of stains, the membrane and nucleus are the only features of a mammalian cell that are discernable in a brightfield image. Fortunately, adding optical accessories to a standard brightfield microscope can dramatically enhance image contrast, eliminating the need to kill, fix, and stain samples. One very simple contrast-enhancing method is dark-field microscopy, which works by illuminating the sample with light that will not be collected by the objective lens. For applications in which greater detail is required, phase-contrast microscopy and differential interference contrast microscopy may be employed. These complementary techniques produce high-contrast images of transparent biological samples by using optical systems to convert variations in density or thickness within the sample to differences in contrast in the final image. Importantly, these techniques can be used to reveal small cellular structures, such as nuclei, ribosomes, mitochondria, membranes, spindles, mitotic apparatus, nucleolus, chromosomes, Golgi apparatus, vacuoles, pinocytotic vesicles, lipid droplets, and cytoplasmic granules. Brightfield microscopy can also be augmented with polarized light, which creates contrast in samples comprising materials with different refractive indices (i.e., birefringent samples). Whereas dark-field, phase-contrast, and differential interference contrast microscopy are well suited for imaging live, unstained biological samples, polarized light microscopy is well suited for studying the structure and composition of rocks, minerals, and metals. Notably, any of these contrast-enhancing methods can be combined with optical sectioning techniques, such as confocal microscopy and light sheet microscopy, which produce clear images of focal planes deep within thicker samples (e.g., thick tissues, small organisms), reducing or eliminating the need to physically section samples (e.g., using a microtome). 
     In the present application, the inventors demonstrate that certain cellular states that are commonly detected using fluorescence microscopy also manifest as subtle morphological features in images produced by transmitted light microscopy. While such features may be difficult or impossible to discern in these images using only the human eye, the inventors show their identification can be automated using a trained model. In the Examples, a trained model is used to predict the percentages of live and dead cells in a sample (i.e., values which would typically be determined using fluorescent stains, such as Caspase-3/7 and TO-PRO-3 stain), using only a brightfield image as input. These visualization methods may then be used in a high-throughput screen for drugs that kill cancer cells within tumor organoids generated from patient tumor samples. 
     The methods and systems disclosed herein are not limited to this single application, however. The ability to associate subtle morphological features that are present in a transmitted light microscopy image with cellular states of interest is useful in countless applications spanning many diverse fields of study. Several exemplary, non-limiting applications are discussed below. 
     The systems and methods disclosed herein have utility in the field of biology, as the disclosed systems and methods can be used to characterize samples ranging from individual cells (e.g., plant cells, animal cells), to tissue slices (e.g., biopsies), to small organisms (e.g., protozoa, bacteria, fungi, embryos, nematodes, insects). Importantly, by avoiding the use of cytotoxic stains, the disclosed systems and methods allow the same samples to be imaged repeatedly over a multi-day or even multi-week time course. Images of the samples are captured using transmitted light microscopy, and a trained system utilizes morphological characteristics (e.g., cell volume, diameter, shape, and topography) to identify cells that possess a certain cellular state. For instance, one can estimate the numbers or concentrations of particular cell types present in a sample by training a system to distinguish cells by type, or assess cell viability by training a system to distinguish between live and dead cells. Trained systems may also be used to characterize cells based on behaviors such as proliferation, differentiation, apoptosis, necrosis, motility, migration, cytoskeletal dynamics, cell-cell and cell-matrix adhesion, signaling, polarity, and vesicle trafficking. For example, the systems and methods disclosed herein may be used to differentiate between different modes of cell death based on their unique morphologies (e.g., shrinkage in apoptosis versus swelling in necrosis). These systems and methods may also be used to monitor the response of cells to any experimental manipulation, ranging from a culture condition to the effect of the outer space environment on biological processes. Thus, the disclosed systems and methods provide a means to investigate myriad aspects of biology using a highly efficient platform. While the cells, tissues, or organisms may be left untreated (e.g., not subject to staining, fixing, etc.), the systems and methods disclosed herein are also useful to image stained, fixed, or otherwise treated samples. By way of example but not by way of limitation, tissues or cells that are immunohistochemically stained, hematoxylin and eosin stained, etc. may be imaged pursuant to the systems and methods disclosed herein. 
     The systems and methods of the present disclosure are useful in the development of novel and improved therapeutics. For instance, the disclosed systems and methods may be used to monitor the response of cells to potential drugs in high-throughput drug screens, as described in the Examples. Additionally, the disclosed systems and methods may be used to monitor the differentiation status of cells, both in the context of development and in the directed differentiation of stem cells. Stem cells may be used to repair tissues damaged by disease or injury, either by directly injecting them into a patient or by differentiating them into replacement cells ex vivo. For example, stem cells may be differentiated into a particular blood cell type for use in donor-free blood transfusions. Other promising stem cell-based therapies include the replacement of: bone marrow cells in blood cancer patients; neurons damaged by spinal cord injuries, stroke, Alzheimer&#39;s disease, or Parkinson&#39;s disease; cartilage damaged by arthritis; skin damaged by serious burns; and islet cells destroyed by type  1  diabetes. Stem cells can also be used to generate specific cell types, tissues, 3D tissue models, or organoids for use in drug screening. Use of a trained system capable of monitoring cell differentiation status would allow for more efficient production of any of these stem cell-based products. 
     The systems and methods of the present disclosure are useful in the diagnosis of medical conditions. For example, the systems and methods disclosed herein can be used to quickly, efficiently, and accurately detect the presence of particular cell types in a patient sample that are indicative of a disease or condition, e.g., tumor cells, blood in urine or stool, clue cells in vaginal discharge, or inflammatory cell infiltration. For example, a system trained on images of tissue samples (e.g., biopsies) will detect morphological features that can be used to distinguish between benign, non-invasive, and invasive cancer cells. Additionally, such systems may be used to identify microbes and parasites in a patient samples, enabling the diagnosis of a wide range of infectious diseases including those caused by bacteria (e.g., tuberculosis, urinary tract infection, tetanus, Lyme disease, gonorrhea, syphilis), fungi (e.g., thrush, yeast infections, ringworm), and parasites (e.g., malaria, sleeping sickness, hookworm disease, scabies). Such methods may be particularly useful for identifying the responsible pathogen in cases where a condition may be caused by a variety of microbes (e.g., infectious keratitis of the cornea). 
     The systems and methods disclosed herein can be used to identify organisms in environmental samples, such as soil, crops, and water. This application could be utilized in both the environmental sciences, e.g., for assessing the health of an ecosystem based on the number and diversity of organisms, and in epidemiology, e.g., for tracing the spread of contaminants that pose a health risk. 
     The systems and methods disclosed herein can be used to evaluate of a wide variety materials, such as clays, fats, oils, soaps, paints, pigments, foods, drugs, glass, latex, polymer blends, textiles and other fibers, chemical compounds, crystals, rocks and minerals. Applications in which such materials are analyzed using microscopy are found across diverse fields. In industry, the systems and methods disclosed herein can be used in failure analysis, design validation, and quality control of commercial products and building materials. For example, the systems and methods disclosed herein can be used to detect defects or fractures in parts of machinery that require a high degree of precision, such as watches and aircraft engines. In computer science, the systems and methods disclosed herein can be used to examine integrated circuits and semiconductors. In both archeology and forensics, the systems and methods disclosed herein can be used to identify unknown materials and examine wear patterns on artifacts/evidence. In geology, the systems and methods disclosed herein can be used to determine the composition of rocks and minerals, and to uncover evidence as to how they were formed. In agriculture, the systems and methods disclosed herein can be used to detect microbial indicators of soil health and to inspect seeds and grains to assess their purity, quality, and germination capacity. In food science, the systems and methods disclosed herein can be used to produce in vitro cultured meat from animal cells. 
     The present application provides a non-limiting exemplary system that uses brightfield images as input in a screen for cancer drugs. Typically, drug response is measured via cell viability assays using live/dead fluorescent stains, which have multiple drawbacks, which are discussed above. While the use of brightfield microscopy largely avoids these issues, visualizing and quantifying live/dead cells from brightfield images alone is not easily accessible and is a significant obstacle towards more cost-efficient high-throughput screening of tumor organoids. Certain systems and methods described herein provide artificial fluorescent images that can be generated using only brightfield images. 
     In some embodiments, a method of generating an artificial image of a cellular composition such as a cell or a group of cells (e.g., cells in culture), is provided. In some embodiments, the generated image is indicative of whether the cell comprises one or more characteristics indicative of a particular cell state or cell identity (e.g., death, disease, differentiation, strain of bacteria, etc.). In some embodiments, the methods include receiving a brightfield image; providing the brightfield image to a trained model; receiving the artificial fluorescent image from the trained model; and outputting the artificial fluorescent image to at least one of a memory or a display. In some embodiments, the cell is a mammalian cell, a plant cell, a eukaryotic cell, or a bacterial cell. In some embodiments, the characteristic(s) indicative of a cell state or cell identity comprise one or more distinguishing physical, structural features of the cell, wherein the features are identifiable by brightfield microscopy. Exemplary, non-limiting features include size, morphology, structures within the cell, staining values, structures on the cell surface, etc. 
     Drug Screening 
     Analysis of drug response data by target may identify important pathways/mutations. For example, drugs can be applied to organoids and/or specimens, and the results of the drug application can be analyzed. For drugs that cause cell death in organoids, the targets of those drugs may be important. Thus, it is desirable to discover and/or develop additional drugs that modulate these targets. The cellular pathways and/or mutations that are important may be specific to the cancer type of the organoid. For example, if CDK inhibitors specifically kill colorectal cancer (CRC) tumor organoid cells, CDK may be especially important in CRC. 
       FIG.  1    shows an example of a system  100  for automatically analyzing tumor organoid images. In some embodiments, the system  100  can include a computing device  104 , a secondary computing device  108 , and/or a display  116 . In some embodiments, the system  100  can include an organoid image database  120 , a training data database  124 , and/or a trained models database  128 . In some embodiments, the trained models database  128  can include one or more trained machine learning models such as artificial neural networks. In some embodiments, the computing device  104  can be in communication with the secondary computing device  108 , the display  116 , the organoid image database  120 , the training data database  124 , and/or the trained models database  128  over a communication network  112 . As shown in  FIG.  1   , the computing device  104  can receive tumor organoid images, such as brightfield images of tumor organoids, and generate artificial fluorescent stain images of the tumor organoids. In some embodiments, the computing device  104  can execute at least a portion of an organoid image analysis application  132  to automatically generate the artificial fluorescent stain images. 
     The organoid image analysis application  132  can be included in the secondary computing device  108  that can be included in the system  100  and/or on the computing device  104 . The computing device  104  can be in communication with the secondary computing device  108 . The computing device  104  and/or the secondary computing device  108  may also be in communication with a display  116  that can be included in the system  100  over the communication network  112 . 
     The communication network  112  can facilitate communication between the computing device  104  and the secondary computing device  108 . In some embodiments, communication network  112  can be any suitable communication network or combination of communication networks. For example, communication network  112  can include a Wi-Fi network (which can include one or more wireless routers, one or more switches, etc.), a peer-to-peer network (e.g., a Bluetooth network), a cellular network (e.g., a 3G network, a 4G network, a 5G network, etc., complying with any suitable standard, such as CDMA, GSM, LTE, LTE Advanced, WiMAX, etc.), a wired network, etc. In some embodiments, communication network  112  can be a local area network, a wide area network, a public network (e.g., the Internet), a private or semi-private network (e.g., a corporate or university intranet), any other suitable type of network, or any suitable combination of networks. Communications links shown in  FIG.  1    can each be any suitable communications link or combination of communications links, such as wired links, fiber optic links, Wi-Fi links, Bluetooth links, cellular links, etc. 
     The organoid image database  120  can include a number of raw tumor organoid images, such as brightfield images. In some embodiments, the brightfield images can be generated using a brightfield microscopy imaging modality. Exemplary brightfield images are described below. In some embodiments, the organoid image database  120  can include artificial fluorescent stain images generated by the organoid image analysis application  132 . 
     The training data database  124  can include a number of images for training a model to generate artificial fluorescent stain images. In some embodiments, the training data image database  124  can include raw brightfield images and corresponding three channel fluorescent stain images. The trained models database  128  can include a number of trained models that can receive raw brightfield images of tumor organoids and output artificial fluorescent stain images. In some embodiments, trained models  136  can be stored in the computing device  104 . In some embodiments, each pair of the raw brightfield images and the corresponding three channel fluorescent stain images can be include a common field of view of the same slide captured by different microscope settings for the brightfield images and the corresponding three channel fluorescent stain images, respectively (e.g., brightfield settings and fluorescent settings). 
     In some embodiments, the training data database  124  can include paired (corresponding) histopathology slide images, where each image depicts a tissue slice from a biological specimen or a blood smear from a blood draw. In some embodiments, if two images correspond it can indicate that the tissue slice(s) or blood smear(s) associated with the two images are obtained from the same biological specimen. For example, the two images may be obtained from the same tumor biopsy or the same blood draw. In some embodiments, the images can depict tissue slices that may have been approximately adjacent in the specimen and/or the same tissue slice may have been used to generate both images. In some embodiments, the corresponding images can depict corresponding cellular and/or tissue structures. For example, both images can depict common structures (e.g., different sections of the same biological cell, same organ, same tissue, etc.). In one example, one of the images can include hematoxylin and eosin (H&amp;E) staining and the other image can include immunohistochemistry (IHC) staining. The IHC staining may be multiplex IHC staining. An example of corresponding images can be found in U.S. patent application Ser. No. 16/830,186, filed Mar. 25, 2020 and titled “Determining Biomarkers From Histopathology Slide Images,” which is incorporated herein by reference in its entirety. In various embodiments, one advantage of simulating IHC slides is to facilitate the detection of various biomarkers without the cost of IHC staining. 
       FIG.  2    shows an example 200 of hardware that can be used in some embodiments of the system  100 . The computing device  104  can include a processor  204 , a display  208 , an input  212 , a communication system  216 , and a memory  220 . The processor  204  can be any suitable hardware processor or combination of processors, such as a central processing unit (“CPU”), a graphics processing unit (“GPU”), etc., which can execute a program, which can include the processes described below. 
     In some embodiments, the display  208  can present a graphical user interface. In some embodiments, the display  208  can be implemented using any suitable display devices, such as a computer monitor, a touchscreen, a television, etc. In some embodiments, the inputs  212  of the computing device  104  can include indicators, sensors, actuatable buttons, a keyboard, a mouse, a graphical user interface, a touch-screen display, etc. 
     In some embodiments, the communication system  216  can include any suitable hardware, firmware, and/or software for communicating with the other systems, over any suitable communication networks. For example, the communication system  216  can include one or more transceivers, one or more communication chips and/or chip sets, etc. In a more particular example, communication system  216  can include hardware, firmware, and/or software that can be used to establish a coaxial connection, a fiber optic connection, an Ethernet connection, a USB connection, a Wi-Fi connection, a Bluetooth connection, a cellular connection, etc. In some embodiments, the communication system  216  allows the computing device  104  to communicate with the secondary computing device  108 . 
     In some embodiments, the memory  220  can include any suitable storage device or devices that can be used to store instructions, values, etc., that can be used, for example, by the processor  204  to present content using display  208 , to communicate with the secondary computing device  108  via communications system(s)  216 , etc. The memory  220  can include any suitable volatile memory, non-volatile memory, storage, or any suitable combination thereof. For example, the memory  220  can include RAM, ROM, EEPROM, one or more flash drives, one or more hard disks, one or more solid state drives, one or more optical drives, etc. In some embodiments, the memory  220  can have encoded thereon a computer program for controlling operation of computing device  104  (or secondary computing device  108 ). In such embodiments, the processor  204  can execute at least a portion of the computer program to present content (e.g., user interfaces, images, graphics, tables, reports, etc.), receive content from the secondary computing device  108 , transmit information to the secondary computing device  108 , etc. 
     The secondary computing device  108  can include a processor  224 , a display  228 , an input  232 , a communication system  236 , and a memory  240 . The processor  224  can be any suitable hardware processor or combination of processors, such as a central processing unit (“CPU”), a graphics processing unit (“GPU”), etc., which can execute a program, which can include the processes described below. 
     In some embodiments, the display  228  can present a graphical user interface. In some embodiments, the display  228  can be implemented using any suitable display devices, such as a computer monitor, a touchscreen, a television, etc. In some embodiments, the inputs  232  of the secondary computing device  108  can include indicators, sensors, actuatable buttons, a keyboard, a mouse, a graphical user interface, a touch-screen display, etc. 
     In some embodiments, the communication system  236  can include any suitable hardware, firmware, and/or software for communicating with the other systems, over any suitable communication networks. For example, the communication system  236  can include one or more transceivers, one or more communication chips and/or chip sets, etc. In a more particular example, communication system  236  can include hardware, firmware, and/or software that can be used to establish a coaxial connection, a fiber optic connection, an Ethernet connection, a USB connection, a Wi-Fi connection, a Bluetooth connection, a cellular connection, etc. In some embodiments, the communication system  236  allows the secondary computing device  108  to communicate with the computing device  104 . 
     In some embodiments, the memory  240  can include any suitable storage device or devices that can be used to store instructions, values, etc., that can be used, for example, by the processor  224  to present content using display  228 , to communicate with the computing device  104  via communications system(s)  236 , etc. The memory  240  can include any suitable volatile memory, non-volatile memory, storage, or any suitable combination thereof. For example, the memory  240  can include RAM, ROM, EEPROM, one or more flash drives, one or more hard disks, one or more solid state drives, one or more optical drives, etc. In some embodiments, the memory  240  can have encoded thereon a computer program for controlling operation of secondary computing device  108  (or computing device  104 ). In such embodiments, the processor  224  can execute at least a portion of the computer program to present content (e.g., user interfaces, images, graphics, tables, reports, etc.), receive content from the computing device  104 , transmit information to the computing device  104 , etc. 
     The display  116  can be a computer display, a television monitor, a projector, or other suitable displays. 
       FIG.  3    shows an exemplary flow  300  that can generate brightfield images and/or fluorescent images, as well as live/dead assays readouts, using patient derived organoids grown from tumor specimens. In some embodiments, the live/dead assays readouts can be produced using brightfield and multiplexed fluorescence imaging. Drug response can be measured via cell viability assays using live/dead fluorescent stains. In some embodiments, the flow  300  can be included in a high throughput drug screening system. An example of a high throughput drug screening can be found in U.S. Prov. patent application Ser. No. 17/114,386, titled “Large Scale Organoid Analysis” and filed Dec. 7, 2020, which is incorporated herein by reference in its entirety. In some examples, biological therapies, such as antibodies or allogenic therapies, may be used as one or more of the drugs in the drug screening. 
     The flow  300  can include harvesting a tumor specimen  308  from a human patient  304 , culturing organoids  312  using the tumor specimen  308 , drug screening  316  the organoids, imaging the organoids  320 , and outputting brightfield and fluorescence images  324  of the organoids. After the organoids are cultured, cells from the organoids can be plated into an assay plate (e.g. a 96-well assay plate, a 384-well assay plate, etc.). The assay plate may also be referred to as a plate. The drug screening  316  can include plating the cells and treating the cells with a number of different drugs and/or concentrations. For example, a 384-well plate can include fourteen drugs at seven different concentrations. As another example, a 96-well plate can include six drugs at five different concentrations. The imaging  320  can include brightfield imaging the treated cells, as well as applying fluorescent stains to at least a portion of the cells and fluorescent imaging the cells. In some embodiments, the fluorescent imaging can include producing three channels of data for each cell. The three channels of data can include a blue/all nuclei channel, a green/apoptotic channel, and a red/pink/dead channel. Each channel can be used to form a fluorescent image. Additionally, the imaging  320  can produce combined 3-channel fluorescent images that include the blue/all nuclei channel, the green/apoptotic channel, and the red/pink/dead channel. In some embodiments, the imaging  320  can include generating brightfield images of the cells using a bright-field microscope and generating fluorescent images of the cells using a confocal microscope such as a confocal laser scanning microscope. In some embodiments, instead of using traditional fluorescent staining to generate the fluorescent images, the imaging  320  can include generating brightfield images for at least a portion of the cells and generating artificial brightfield images for the portion of the cells based on the brightfield images using a process described below (e.g., the process of  FIG.  9   ). 
     By way of example but not by way of limitation, in some embodiments, brightfield images (for example a 2D brightfield projection) depicting a cell culture well during a drug screening assay can be generated using a brightfield modality, such as a brightfield microscope. In some embodiments, a brightfield image is generated using a 10×objective on a microscope. A different objective can be used if higher or lower magnification is desired. In some embodiments, the microscope can be an ImageXPRESS microscope available from Molecular Devices. Other microscope brands capable of brightfield imaging are commercially available and can also be employed in the disclosed methods. In some embodiments, the cells can be cancer cell lines or cancer tumor organoids derived from patient specimens. 
       FIG.  4    shows an exemplary flow  400  for training a generator  408  to generate an artificial fluorescent image  412  based on an input brightfield image  404  of a cellular composition, such as organoid cells. In some embodiments, the generator  408  can include a U-Net convolutional neural network. In some embodiments, the generator  408  can include a pix2pix model. In some embodiments, the generator  408  can be a generative adversarial network (GAN). An exemplary neural network that can be included in the generator  408  is described below in conjunction with  FIG.  6   . In some embodiments, the generator can include a neural network that can receive the brightfield image  404  and output a single three-channel fluorescent image (e.g., a 256×256×3 image). In some embodiments, the generator can include three neural networks that can each receive the brightfield image  404  and output a one-channel fluorescent image (e.g., a 256×256×1 image). Generators that include three neural networks that can each receive the brightfield image  404  and output a one-channel fluorescent image may be referred to as three-model generators. Each of the neural networks can be trained to output a specific channel of fluorescence. For example, a first neural network can output a blue/all nuclei channel image, a second neural network can output a green/apoptotic channel image, and a third neural network can output a red/dead channel image. The flow  400  can include combining the blue/all nuclei channel image, the green/apoptotic channel image, and the red/dead channel image into a single three-channel fluorescent image (e.g., a 256×256×3 image, a 1024×1024×3 image, etc.). 
     The flow can include providing the brightfield image  404 , the artificial fluorescent image  412 , and a ground truth fluorescent image  424  associated with brightfield image to a discriminator  416  that can predict whether or not an image is real or generated by the generator  408  (e.g., the artificial fluorescent image  412 ). In some embodiments, the generator  408  can receive an image and output a label ranging from 0 to 1, with 0 indicating that image is generated by the generator  408  and 1 indicating that the image is real (e.g., the ground truth fluorescent image  424  associated with the brightfield image  404 ). In some embodiments, the discriminator  416  can be a PatchGAN discriminator, such as a 1×1 PatchGAN discriminator. An exemplary discriminator is described below in conjunction with  FIG.  7   . 
     The flow  400  can include an objective function value calculation  420 . The objective function value calculation  420  can include calculating an objective function value based on labels output by the discriminator  416  and/or by other metrics calculated based on the brightfield image  404 , the artificial fluorescent image  412 , and the ground truth fluorescent image  424 . The objective function value can capture multiple loss functions (e.g., a weighted sum of multiple loss functions). In this way, the objective function value can act as a total loss value for the generator  408  and the discriminator  416 . The flow  400  can include transmitting the objective function value and/or other information from the discriminator  416  to the generator  408  and the discriminator  416  in order to update both the generator  408  and the discriminator  416 . A number of different suitable objective functions can be used to calculate the objective function value. However, in testing an embodiment of the generator  408 , a sum of GANLoss+0.83SSIM+0.17L1 was shown to outperform other tested loss functions such as GANLoss+L1 as used by the generator  408 . GANLoss can be used to determine whether an image is real or generated. The L1 loss can be used as an additional objective to be minimized to ensure that the generated and real image have the least mean absolute error in addition to GANLoss. Structural Similarity Index (SSIM) can be used to improve performance across multiple performance metrics as well as reduce artifacts. The objective function value calculation  420  will be described below. 
     The flow  400  can include receiving a number of pairs of a brightfield image and a corresponding ground truth fluorescence image, and iteratively training the generator  408  using each pair of images. 
     In some embodiments, the flow  400  can include receiving a number of pairs of a H&amp;E image and a corresponding ground truth IHC image, and iteratively training the generator  408  using each pair of images to generate artificial IHC images. 
     In some embodiments, the flow  400  can include receiving a number of pairs of an IHC image and a corresponding ground truth H&amp;E image, and iteratively training the generator  408  using each pair of images to generate artificial H&amp;E images. 
     In some embodiments, the flow  400  can include receiving a number of pairs of an IHC image and/or a multiplex IHC images and a corresponding ground truth H&amp;E image, and iteratively training the generator  408  using each pair of images to generate artificial H&amp;E images. 
     In some embodiments, the flow  400  can include pre-processing the brightfield image  404  and the ground truth fluorescent image  424 . Raw brightfield and fluorescent images may have minimal contrast and require enhancement before being used to train the generator  408 . For example, in testing, the pixel intensities for the individual channels of the fluorescent image were generally skewed to zero, which may have been because most of the image is black (i.e., background), except for regions containing organoids and/or cells. 
     In some embodiments, the artificial fluorescent image  412  can be used to provide a count of live/dead cells. In order to enhance the contrast of the artificial fluorescent image  412  and improve the ability to count live/dead cells from the artificial fluorescent image  412 , both the brightfield image  404  and the corresponding ground truth image  424  can undergo contrast enhancement to brighten and sharpen organoids/cells. 
     In some embodiments, multiple brightfield images and multiple ground truth fluorescent images can be generated per well. For example, for a 96-well plate, there can be about 9-16 sites per well that get imaged. 
     In some embodiments, the raw brightfield and ground truth fluorescent images can have pixel intensities ranging from [0, 2 16 ]. First, a contrast enhancement process, which can be included in the organoid image analysis application  132 , can convert each image to an unsigned byte format, with values ranging from [0, 255]. Next, the contrast enhancement process can stretch and clip each pixel intensity to a desired output range. 
     In some embodiments, the desired intensity range of an input image to be stretched can be decided on a per image basis as follows: For the three pixel intensities corresponding to the three fluorophores used to generate the fluorescent image, the input range can be re-scaled using the mode of the pixel intensity distribution as the lower bound value and 1/10th the maximum pixel intensity as the upper bound. The contrast enhancement process can choose the upper bound in order to avoid oversaturated pixels and focus on cell signal. The contrast enhancement process can normalize each pixel intensity based on the lower bound and the upper bound, which function as a min/max range, using a min-max norm, and then each pixel can be multiplied by the output range [0,255]. For the brightfield image  404 , the contrast enhancement process can determine an input range by uniformly stretching the 2nd and 98th percentile of pixel intensities to the output range [0,255]. 
     For images with low signal, background noise may be included in the output range. To minimize any remaining back-ground noise, the contrast enhancement process can clip the minimum pixel value by two integer values for the red and green channels, and by three integer values for the blue channel, where the intensity range is wider on average. The maximum pixel values can be increased accordingly to preserve intensity range per image. 
     In some embodiments, the ground truth image  424  can be a 1024×1024×3 RGB image including a blue channel corresponding to nuclei (Hoecsht), a green channel corresponding to apoptotic cells (Caspase), and a red channel corresponding to dead cells (TO-PRO-3) In some embodiments, the flow  400  can include enhancing the ground truth image  424 . In some embodiments, the enhancing can include contrast enhancing the blue channel, the green channel, and the red channel to brighten and sharpen organoids and/or cells in the ground truth image  424 . In some embodiments, the flow can down convert pixel intensities in the ground truth image  424  (e.g., converting sixteen bit pixel intensities to eight bit intensities). After converting pixel intensities, the flow  400  can include rescaling pixel intensities to 1/10th of a maximum pixel intensity as the upper bound, as well as to the mode of the pixel intensity+two integer values as the lower bound for the red channel and the green channel, and to the mode of the pixel intensity+three integer values for the blue channel as the lower bound. 
     In some embodiments, the discriminator  416  can output a predicted label (e.g., a “0” or a “1”) to the objective function calculation  420 . The predicted label can indicate if the artificial fluorescent image  412  is fake or real. In some embodiments, the objective function can be calculated as a weighted sum of GANLoss, SSIM, and L1. In some embodiments, the GANLoss can be calculated based on the predicted label output by the discriminator. The GANLoss can be used to determine whether the artificial fluorescent image  412  is real or generated. In some embodiments, the L1 loss can be calculated based on the artificial fluorescent image  412  and the corresponding ground truth image. The L1 loss can be used as an additional objective to be minimized to ensure that the artificial fluorescent image  412  and the corresponding ground truth image have the least mean absolute error in addition to GANLoss. 
     Certain machine learning models, such as the pix2pix model, may only use GANLoss and L1 loss in training a generator. As mentioned above, the objective function calculation  420  can include an SSIM metric in addition to the GANLoss and the L1 loss, which can improve the performance of the generator  408  in comparison to a generator trained using only GANLoss and L1 loss. 
     In some embodiments, the objective function implemented in the objective function calculation can be defined as: 
     
       
         
           
             
               
                 
                   
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     where λ+β=1, L L1  is the mean absolute error loss, and 1−L SSIM (G) is the structural similarity index loss between the generated image G (e.g., the fluorescent image  412 ) and the corresponding ground truth image. In some embodiments, λ can be 0.17 and β can be 0.83. In some embodiments, λ can be selected from 0.1 to 0.3, and β can be selected from 0.7 to 0.9. 
     In some embodiments, SSIM can take into account the luminance (l), contrast (c), and structure (s) of two images and computes a metric between 0 and 1, where 1 indicates a perfect match between the two images: 
     
       
         
           
             
               
                 
                   
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     C 1 , C 2  and C 3  are small constants defined by: 
         C   1 =( K   1   L ) 2   ,C   2 =( K   2   L ) 2  and  C   3   =C   2 /2  (5)
 
     where K 1 , K 2  are two scalar constants whose values are less than 1, and L is the dynamic range of the pixel intensities (i.e. 256). SSIM can then be calculated as: 
     
       
         
           
             
               
                 
                   
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     where l, c, and s are computed using the mean, variance and covariance respectively of two images of the same size using a fixed window size. α, β, and γ are constants set to 1. In addition to structural similarity, we also evaluated model prediction using root mean square error, which is the sum of the squared difference of pixel intensities. 
     In some embodiments, the Proposed Loss function can be as follows: 
       DiscriminatorLoss=MSELoss{Real Prediction,1}+MSELoss{Generated Prediction,0}+MSELoss{Predicted Viability,Viability} 
       GeneratorLoss=MSELoss{Generated Prediction,1}+MAE{Generated Fluorescent,Real Fluorescent}+SSIM{Generated Fluorescent,Real Fluorescent}; 
     where MSE refers to Mean Squared Error Loss, MAE is the mean absolute error, and SSIM is the Structural similarity index. The RCA Model was trained for thirty epochs with a learning rate of 2e-3 and Adam optimizer. The images of resolution 1024×1024 were imaged at 10× magnification. They were randomly flipped as a data augmentation step. 
     In some embodiments, once a dye is added to a cell culture well, the cells in that well cannot continue to be used for the experiment, such that it is difficult or impossible to measure cell death in that well at a subsequent point in time. In some embodiments, the flow  400  can include generating artificial fluorescent images, which can reduce time requirements for imaging by a factor of ten in comparison to utilizing dyes to generate the fluorescent images. Standard fluorescent imaging may take up to an hour to perform. In some embodiments, the flow  400  can be used in conjunction with a drug screening platform that uniquely interprets tumor organoids (TOs) which have limited biomass and intra-tumoral clonal heterogeneity by incorporating Patient Derived Tumor Organoids. The platform couples high content fluorescent confocal imaging analysis with a robust statistical analytical approach to measure hundreds of discrete data points of TO viability from as few as 10{circumflex over ( )}3 cells. 
     In some embodiments, a flow similar to flow  400  may be used to train a generator (e.g., the generator  408 ) to receive H&amp;E images and generate artificial IHC images and/or multiplex IHC images. In some embodiments, the flow  400  can include receiving an H&amp;E image from training data database  124  and providing the H&amp;E image to the generator  408  (in place of the brightfield image  404 ). In these embodiments, the discriminator  424  can receive an artificial H&amp;E image generated by the generator and a ground truth IHC image (in place of the ground truth fluorescent image  424 ) corresponding to an H&amp;E image from training data database  124 . Thus, the flow  400  can be used to train a generator to generate artificial IHC images and/or multiplex IHC images based on H&amp;E images. 
     Referring to  FIG.  4    as well as  FIG.  5   , an exemplary flow  500  for generating an artificial fluorescent image  512  is shown. The flow  500  can include providing an input brightfield image  504  of plated cells to a trained model  508 . The trained model  508  can include the generator  408 , which can be trained using the flow  400 . The trained model  508  can output an artificial fluorescent image  512 . The fluorescent image  512  can be used to generate a live/dead assays readout and/or analyze the effectiveness of different drugs and/or dosages on cancer cells in tissue organoids. 
     Notably, the flow  500  can produce the fluorescent image  512  without the use of fluorescent dyes, which provides several advantages over traditional fluorescent imaging processes that require the use of fluorescent dyes. Some dyes have cytotoxicity and must be added a certain amount of time before imaging. Additionally, once certain dyes are added to a cell culture well, the cells in that well cannot continue to be used for reimaging because of the difficulty in measuring cell death in that well at a subsequent point in time. Thus, the flow  500  can improve the ease of generating the fluorescent images because the flow  500  may only require brightfield imaging, which is not time-dependent like the traditional fluorescent imaging. Additionally, the flow  500  can increase the speed at which the fluorescent images are obtained, because fluorescent dyes do not need to be applied to the cells, and because the flow  500  does not have to wait for the fluorescent dyes to diffuse before imaging the cells. As another example, the flow  500  can allow multiple fluorescent images to be generated for each cell well at a number of different time points. The fluorescent dyes used in traditional fluorescent imaging can damage the cells enough to prevent reimaging. In contrast, the flow  500  can be used to produce multiple fluorescent images over a time period of days, weeks, months, etc. Thus, the flow  500  can provide more data points per cell well than traditional fluorescent imaging. 
     The training data used to train a trained model may be selected based on the aspects of the cellular compositions to be imaged post-training. In some embodiments, a single trained model (e.g., trained model  508 ) can be trained on training data including a set of brightfield images and corresponding fluorescent images associated with one of six or more organoid lines each having a distinct cancer type, such that each organoid line is represented in the training data. In some embodiments, a single trained model (e.g., trained model  508 ) can be a pan-cancer model trained to generate artificial fluorescent stain images from a brightfield image associated with any cancer type. In some embodiments, a trained model can be trained on training data only including images associated with one organoid line (for example, one cancer type). 
       FIG.  6    shows an exemplary neural network  600 . The neural network  600  can be trained to receive an input image  604  and generate an artificial fluorescent image  608  based on the input image  604 . In some embodiments, the input image  604  can be a raw brightfield image that has been processed to enhance contrast and/or modify other characteristics in order to enhance the raw brightfield image and potentially produce a better artificial fluorescent image (e.g., the fluorescent image  608 ). 
     In some embodiments, the neural network  600  can include a Unet architecture. In some embodiments, the Unet architecture can be sized to receive a 256×256×3 input image. The 256×256×3 input image can be a brightfield image. In some embodiments, the input image can be a 256×256×1 image. In some embodiments, the generator  408  in  FIG.  4    and/or the trained model  508  in  FIG.  5    can include the neural network  600 . 
       FIG.  7    shows an exemplary discriminator  700 . In some embodiments, the discriminator  700  in  FIG.  7    can be included as the discriminator  416  in the flow  400  shown in  FIG.  4   . In some embodiments, the discriminator  700  can be a 1×1 PatchGAN. In some embodiments, the discriminator  700  can receive a brightfield image  704  and a fluorescent image  708 . The fluorescent image can be an artificial fluorescent image (e.g., the fluorescent image  608  in  FIG.  6   ) or a ground truth fluorescent image. In some embodiments, each of the brightfield image  704  and the fluorescent image  708  can be 256×256×3 input images. In some embodiments, the brightfield image  704  and the fluorescent image  708  can be concatenated. In some embodiments, the concatenated image can be a 256×256×6 input image. 
     In some embodiments, the discriminator  700  can receive the brightfield image  704  and a fluorescent image  708  and generate a predicted label  712  indicative of whether or not the fluorescent image  708  is real or fake. In some embodiments, the predicted label  712  can be a “0” to indicate the fluorescent image  708  is fake, and “1” to indicate the fluorescent image  708  is real. In some embodiments, the discriminator  700  can include a neural network 
     Referring to  FIGS.  4 - 7   , in some embodiments, the flow  400 , the flow  500 , the neural network  600 , and the discriminator  700  can be implemented using Pytorch version 1.0.0. In some embodiments, the flow  400  can be used to train the generator  408  to generate artificial fluorescent images for a colon cancer organoid line. In some embodiments, the flow  400  can be used to train the generator  408  to generate artificial fluorescent images for a gastric cancer organoid line. 
       FIG.  8    shows an exemplary process  800  that can train a model to generate an artificial fluorescent stain image of one or more organoids based on an input brightfield image. In some embodiments, the model can be the generator  408  in  FIG.  4   , and/or the neural network  600 . In some embodiments, the model can include a neural network that can receive the input brightfield image and output a single three-channel fluorescent image (e.g., a 256×256×3 image). In some embodiments, the model can include three neural networks that can each receive the brightfield image and output a one-channel fluorescent image (e.g., a 256×256×1 image). The one-channel images can then be combined into a single three-channel fluorescent image. 
     In some embodiments, the process  800  can be used to train a model to output artificial fluorescent images of objects other than tumor organoids using a number of non-fluorescent images (e.g., brightfield images) and fluorescent stain images (which may have more or less than three channels) as training data. 
     The process  800  can be implemented as computer readable instructions on one or more memories or other non-transitory computer readable media, and executed by one or more processors in communication with the one or more memories or other media. In some embodiments, the process  800  can be implemented as computer readable instructions on the memory  220  and/or the memory  240  and executed by the processor  204  and/or the processor  224 . 
     At  804 , the process  800  can receive training data. In some embodiments, the training data can include a number of brightfield images and a number of associated real fluorescent images of organoids. In some embodiments, the organoids can be from a single tumor organoid line. In some embodiments, the brightfield images and the real fluorescent images can be preprocessed in order to enhance contrast as described above. In some embodiments, the brightfield images and the real fluorescent images can be raw images that have not undergone any preprocessing such as contrast enhancement. 
     At  808 , if the training data includes raw brightfield images and/or raw real fluorescent images (i.e., “YES” at  808 ), the process  800  can proceed to  812 . If the training data does not include any raw brightfield images or raw real fluorescent images (i.e., “NO” at  808 ), the process  800  can proceed to  816 . 
     At  812 , the process  800  can preprocess at least a portion of the brightfield images and/or real fluorescent images. In some embodiments, at  812 , the process  800  can enhance the contrast of any raw brightfield images and/or real fluorescent images included in the training data. In some embodiments, the raw brightfield and ground truth fluorescent images can have pixel intensities ranging from [0,2 16 ]. In some embodiments, the process  800  can convert each image to an unsigned byte format, with values ranging from [0, 255]. The process  800  can then stretch and clip each pixel intensity to a desired output range. 
     In some embodiments, the process  800  can stretch the desired intensity range of the input on a per image basis. For the three pixel intensities corresponding to the three fluorophores used to generate a real fluorescent image, the process  800  can re-scale the input range using the mode of the pixel intensity distribution as the lower bound value and 1/10th the maximum pixel intensity as the upper bound. The process  800  can determine the upper bound in order to avoid oversaturated pixels and focus on cell signal. The process  800  can normalize each pixel intensity based on the lower bound and the upper bound, which function as a min/max range, using a min-max norm, and then each pixel can be multiplied by the output range [0,255]. For each brightfield image included in the training data, the process  800  can determine an input range by uniformly stretching the 2nd and 98th percentile of pixel intensities to the output range [0,255]. 
     For images with low signal, background noise may be included in the output range. In some embodiments, to minimize any remaining background noise, the process  800  can clip the minimum pixel value by two integer values for the red and green channels, and by three integer values for the blue channel, where the intensity range is wider on average. In some embodiments, the process  800  can increase maximum pixel values accordingly to preserve intensity range per image. 
     At  816 , the process  800  can provide a brightfield image to the model. As described above, in some embodiments, the model can be the generator  408  in  FIG.  4    and/or the neural network  600  in  FIG.  6   . In some embodiments, the model can include three neural networks, and each neural network can receive a copy of the brightfield image and output a different channel (e.g., red, green, or blue) of an artificial fluorescent image. 
     At  820 , the process  800  can receive an artificial fluorescent image from the model. The model can generate the artificial fluorescent image (e.g., the artificial fluorescent image  412 ) based on the brightfield image (e.g., the brightfield image  404 ) provided to the model. In some embodiments, the process  800  can receive three one-channel images from three neural networks included in the model and combine the one-channel images into a single three-channel artificial fluorescent image. 
     At  824 , the process  800  can calculate an objective function value based on the brightfield image, the real fluorescent image associated with the brightfield image, and the artificial fluorescent image. In some embodiments, the process  800  can determine a predicted label indicative of whether or not the artificial fluorescent image is real or not by providing the artificial fluorescent image and the real fluorescent image to a discriminator (e.g., the discriminator  416 ). In some embodiments, the objective function value can be calculated using equation (1) above, where λ is 0.17 and β is 0.83. In some embodiments, λ can be selected from 0.1 to 0.3, and β can be selected from 0.7 to 0.9. In some embodiments, the learning rate can fixed at 0.0002 for a first number of epochs (e.g., fifteen epochs) of training, and then linearly decayed to zero over a second number of epochs (e.g., ten epochs). 
     At  828 , the process  800  can update the model (e.g., the generator  408 ) and the discriminator (e.g., the discriminator  416 ) based on the objective function value. In some embodiments, the model and the discriminator can each include a neural network. In some embodiments, the process  800  can update weights of layers included in neural networks included in the model and the discriminator based on the objective function value. 
     At  832 , the process  800  can determine whether or not there is a brightfield image included in the training data that has not been provided to the model. If there is a brightfield image included in the training data that has not been provided to the model (e.g., “YES” at  832 ), the process can proceed to  816  in order to provide the brightfield image to the model. If there are no brightfield images included in the training data that has not been provided to the model (e.g., “NO” at  832 ), the process can proceed to  836 . 
     At  836 , the process  800  can cause the model to be output. At  836 , the model has been trained, and can be referred to as a trained model. In some embodiments, the process  800  can cause the trained model to be output to at least one of a memory (e.g., the memory  220  and/or the memory  240 ) and/or a database (e.g., the trained models database  128 ). The trained model may be accessed and used in certain processes, such as the processes in  FIGS.  9  and  13   . The process  800  can then end. 
       FIG.  9    shows an exemplary process  900  that can generate an artificial fluorescent image of one or more organoids based on a brightfield image. More specifically, the process  900  can generate the artificial fluorescent image using a trained model. In some embodiments, the model can be the generator  408  in  FIG.  4   , the trained model  508 , and/or the neural network  600  in  FIG.  6    trained using the process  800 . In some embodiments, the model can include a neural network that can receive the input brightfield image and output a single three-channel fluorescent image (e.g., a 256×256×3 image). In some embodiments, the model can include three neural networks that can each receive the brightfield image and output a one-channel fluorescent image (e.g., a 256×256×1 image). The one-channel images can then be combined into a single three-channel fluorescent image. 
     In some embodiments, the process  900  can be used to generate artificial fluorescent images (which can have one channel, two channels, three channels, etc.) of objects other than tumor organoids using a non-fluorescent image (e.g., a brightfield image). In this way, objects other than tumor organoids that require fluorescent staining to be properly imaged can be artificially generated without the use of and/or drawbacks of fluorescent dyes. 
     The process  900  can be implemented as computer readable instructions on one or more memories or other non-transitory computer readable media, and executed by one or more processors in communication with the one or more memories or other media. In some embodiments, the process  900  can be implemented as computer readable instructions on the memory  220  and/or the memory  240  and executed by the processor  204  and/or the processor  224 . In some embodiments, the process  900  can be executed by an imaging system. In some embodiments, a brightfield microscopy imaging system can be configured to execute the process  900 . In some embodiments, the brightfield microscopy imaging system can include one or more memories or other non-transitory computer readable media including the process  900  implemented as computer readable instructions on the one or more memories or other non-transitory computer readable media, and one or more processors in communication with the one or more memories or other media configured to execute the computer readable instructions to execute the process  900 . 
     At  904 , the process  900  can receive a brightfield image (e.g., the brightfield image  404  in  FIG.  4    and/or the brightfield image  504  in  FIG.  5   ) of one or more organoids. In some embodiments, the brightfield image can be preprocessed in order to enhance contrast as described above. In some embodiments, the brightfield image can be a raw image that has not undergone any preprocessing such as contrast enhancement. 
     At  908 , the process  900  can determine if the brightfield image is unprocessed (i.e., raw). If the brightfield image is unprocessed (i.e., “YES” at  908 ), the process  900  can proceed to  912 . If the brightfield image is not unprocessed (i.e., “NO” at  908 ), the process  900  can proceed to  916 . 
     At  912 , the process  900  can preprocess the brightfield image. In some embodiments, the brightfield image can have pixel intensities ranging from [0, 2 16 ]. In some embodiments, the process  900  can convert the brightfield image to an unsigned byte format, with values ranging from [0, 255]. In some embodiments, the process  900  can convert the brightfield image to another format with less bits than the original pixel intensity. The process  900  can then stretch and clip each pixel intensity to a desired output range. In some embodiments, the process  900  can determine an input range for the brightfield image by uniformly stretching the 2nd and 98th percentile of pixel intensities in the brightfield image to an output range [0,255]. 
     At  916 , the process  900  can provide the brightfield image to a trained model. In some embodiments, the model can include the generator  408  in  FIG.  4    trained using the process  800  in  FIG.  8   , the trained model  508 , and/or the neural network  600  trained using the process  800  in  FIG.  8   . In some embodiments, the trained model can include three neural networks, and each neural network can receive a copy of the brightfield image and output a different channel (e.g., red, green, or blue) of an artificial fluorescent image. In some embodiments, the process  900  can apply the trained model to the brightfield image to generate an artificial fluorescent image. 
     At  920 , the process  900  can receive an artificial fluorescent image from the trained model. In some embodiments, the process  900  can receive three one-channel images from three neural networks included in the trained model and combine the one-channel images into a single three-channel artificial fluorescent image. The artificial fluorescent image can indicate whether cells included in the tumor organoids are alive or dead. 
     At  924 , the process  900  can cause the artificial fluorescent image to be output. In some embodiments, the process  900  can cause the artificial fluorescent image to be output to at least one of a memory (e.g., the memory  220  and/or the memory  240 ) and/or a display (e.g., the display  116 , the display  208 , and/or the display  228 ). The artificial fluorescent image can be used to provide a live/dead count of cells in the organoids. In some embodiments, the process  900  can cause the artificial fluorescent image to be output to an automatic cell counting process in order to receive an accurate live/dead count of cells, a percentage of cells that are viable (e.g., alive) or dead, and/or a cell count report in the artificial fluorescent image. For example, the process  900  can cause the artificial fluorescent image to be output to the CellProfiler available at https://cellprofiler.org. In some embodiments, the process  900  can cause one or more channels of the artificial fluorescent image to be output to an automatic cell counting process in order to receive a cell count report, a percentage of cells that are viable (e.g., alive) or dead, and/or accurate live/dead count of cells in the artificial fluorescent image. In some embodiments, the process  900  can cause the brightfield image to be output to a trained model in order to receive a cell count report, a percentage of cells that are viable (e.g., alive) or dead, and/or accurate live/dead count of cells in the artificial fluorescent image. In some embodiments, the process  900  can cause a combination (e.g., image embeddings combined by concatenation) of the brightfield image and one, two, or three channels of the artificial fluorescent image to be output to an automatic cell counting process in order to receive a cell count report, a percentage of cells that are viable (e.g., alive) or dead, and/or an accurate live/dead count of cells in the artificial fluorescent image. 
     In some embodiments, at  924 , the process  900  can identify cells in the artificial fluorescent image by converting each of the channels to grayscale, enhancing and suppressing certain features such as speckles, ring shapes, neurites, dark holes, identifying primary objects belonging to the all cell channel where the typical diameters of these objects (in pixel units) is set anywhere between 2 and 20 with a minimum cross entropy thresholding method at a smoothing scale of 1.3488, and identifying primary objects again belonging to the dead cells channel where typical diameter is anywhere between 5 and 20 in pixel units. In this way, the process  900  can generate a cell count report. In some embodiments, the process  924  can determine if a drug and/or dosage is effective in killing tumor organoid cells based on the live/dead count of cells. In some embodiments, at  924 , the process  900  can extrapolate dose response from a distribution of organoid viability at a single concentration. 
     In some embodiments, the cell count report may be analyzed to quantify the efficacy of the drug in killing a particular line of tumor organoid cells. For example, if a concentration of a drug causes a lower number of live cells and/or greater number of dead cells, the drug may be rated as more effective in killing a particular line of tumor organoid cells. For each line of tumor organoid cells, characteristics of the tumor organoid cells (for example, molecular data including detected mutations, RNA expression profiles measured in the tumor organoid cells etc., other biomarkers, and/or clinical data associated with the patient from which the tumor organoid was derived) and the results (including the drug efficacy rating) of each drug dose may be saved in a database of drug assay results. These results may be used to match therapies to patients. For example, if a patient has a cancer with characteristics similar to a tumor organoid cell line, drugs rated as effective in killing those tumor organoid cells may be matched to the patient. 
     In some embodiments, the process  900  can analyze nucleic acid data associated with the one or more tumor organoids. Each tumor organoid included in the one or more tumor organoids can be associated with a specimen (e.g., the specimen the tumor organoid was harvested from). In some embodiments, each specimen can be associated with a patient. The patient can be associated with patient data that can include nucleic acid data. In some embodiments, the nucleic acid data can include whole exome data, transcriptome data, DNA data, and/or RNA data. The nucleic acid data may be used to further analyze the patient. In some embodiments, the process  900  can associate the artificial fluorescent image with information about the specimen (e.g., the nucleic acid data). In some embodiments, the process  900  can provide the artificial fluorescent image and the associated information about the specimen to a database. In some embodiments, the database can include at least seven hundred and fifty artificial fluorescent images. 
     In some embodiments, the process  900  can generate a report based on the cell count, the cell count report, the nucleic acid data, and/or the artificial fluorescent image. In some embodiments, the process  900  can cause the report to be output to at least one of a memory (e.g., the memory  220  and/or the memory  240 ) and/or a display (e.g., the display  116 , the display  208 , and/or the display  228 ). The process  900  can then end. 
       FIG.  10    shows exemplary raw images before preprocessing and after preprocessing. The raw images before preprocessing include a brightfield image  1004 , a blue/all nuclei channel fluorescent image  1008 , a green/apoptotic channel fluorescent image  1012 , red/pink/dead channel fluorescent image  1016 , and a combined 3-channel fluorescent image  1020 . The preprocessed images include a brightfield image  1024 , a blue/all nuclei channel fluorescent image  1028 , a green/apoptotic channel fluorescent image  1032 , red/pink/dead channel fluorescent image  1036 , and a combined 3-channel fluorescent image  1040 . The organoids and cells are brighter and sharper in the preprocessed images. In some embodiments, the preprocessed images  1024 - 1040  can be generated at  812  in the process  800  in  FIG.  8   . 
       FIG.  11    shows an exemplary flow  1100  for culturing tumor organoids. Culture of patient derived tumor organoids. The flow  100  can include obtaining tumor tissue from a same-day surgery, disassociating cells from the tumor tissue, and culturing the tumor organoids from the cells. An example of systems and methods for culturing tumor organoids may be found in U.S. patent application Ser. No. 16/693,117, titled “Tumor Organoid Culture Compositions, Systems, and Methods” and filed Nov. 22, 2019, which is incorporated by reference herein in its entirety. Tumor tissue sent from hospitals is cultured to form tumor organoids. 
       FIG.  12    shows an exemplary flow  1200  for conducting drug screens in accordance with systems and methods described herein. In some embodiments, the flow  1200  can include disassociating tumor organoids into single cells, plating the cells (e.g., in a well plate such as a 96-well plate and/or a 384-well plate), growing the cells into organoids over a predetermined time period (e.g., seventy-two hours), treating the organoids with at least one therapeutic technique, and imaging the tumor organoids a predetermined amount of time (e.g., seventy-two hours) after the tumor organoids are treated. In some embodiments, only brightfield imaging may be performed on the tumor organoids, and any brightfield images generated can be used to generate artificial fluorescent images using the process  900  in  FIG.  9   . A live/dead count can then be generated based on the artificial fluorescent images. One example of systems and methods for using tumor organoids for drug screens may be found in U.S. Patent Prov. App. No. 62/924,621, titled “Systems and Methods for Predicting Therapeutic Sensitivity” and filed Oct. 22, 2019 (and PCT/US20/56930, filed Oct. 22, 2020), which are incorporated by reference herein in their entireties. 
       FIG.  13    shows an exemplary process  1300  that can generate artificial fluorescent images at multiple time points for at least one organoid. Notably, the process  1300  can provide an advantage over standard fluorescent imaging techniques. As mentioned above, fluorescent dyes used to generate standard fluorescent images can damage the cells (e.g., killing the cells) in the organoids, and do not permit fluorescent images to be generated at different time points (e.g., every twelve hours, every twenty-four hours, every seventy-two hours, every week, etc.). In contrast, the process  1300  permits repeated fluorescent imaging of organoids because the process  1300  may only require brightfield images (which do not damage the organoids), and can generate artificial fluorescent images based on the brightfield images. 
     The process  1300  can be implemented as computer readable instructions on one or more memories or other non-transitory computer readable media, and executed by one or more processors in communication with the one or more memories or other media. In some embodiments, the process  1300  can be implemented as computer readable instructions on the memory  220  and/or the memory  240  and executed by the processor  204  and/or the processor  224 . In some embodiments, the process  1300  can be executed by an imaging system. In some embodiments, a brightfield microscopy imaging system can be configured to execute the process  1300 . In some embodiments, the brightfield microscopy imaging system can include one or more memories or other non-transitory computer readable media including the process  1300  implemented as computer readable instructions on the one or more memories or other non-transitory computer readable media, and one or more processors in communication with the one or more memories or other media configured to execute the computer readable instructions to execute the process  1300 . 
     At  1304 , the process  1300  can receive an indication to analyze treated organoids at multiple time points. In some embodiments, the organoids can be plated (e.g., in a well plate such as a 96-well plate and/or a 384-well plate). In some embodiments, the organoids can be plated on multiple well plates. In some embodiments, the organoids can be plated on one or more petri dishes. In some embodiments, the organoids can be treated using a variety of different treatments, which can vary in drug type, drug concentration, and/or other parameters. In some embodiments, each well in a well plate can be associated with a different treatment. 
     In some embodiments, the multiple time points can represent a time after the organoids have been treated. For example, a twelve hour time point can be twelve hours after the time at which the organoids were treated. In some embodiments, the multiple time points can be spaced at regular intervals. For example, the multiple time points can occur every twelve hours, every twenty-four hours, every seventy-two hours, every week, etc. In some embodiments, the multiple time points can be irregularly spaced. For example, the time points can include a first time point at six hours, a second time point at twenty four-hours, a third time point at three days, a fourth time point at one week, and a fifth time point at twenty-eight days. 
     At  1308 , the process  1300  can wait until the next time point included in the multiple time points. For example, if six hours has passed since the organoids have been treated, and the next time point is at twelve hours, the process  1300  can wait for six hours. 
     At  1312 , the process  1300  can cause at least one brightfield image of the treated organoids to be generated. In some embodiments, process  1300  can generate the brightfield images of the treated organoids using a bright-field microscope and generating fluorescent images of the cells using a confocal microscope such as a confocal laser scanning microscope. In some embodiments, the process  1300  can preprocess the at least one brightfield image. For example, the process  1300  can, for each brightfield image, perform at least a portion of  912  in the process  900  in  FIG.  9   . In some embodiments, multiple brightfield images can be generated for each well. For example, for a 96-well plate, there can be about 9-16 sites per well that get imaged. 
     At  1316 , the process  1300  can cause at least one artificial fluorescent image to be generated based on the at least one brightfield image. In some embodiments, the process  1300  can provide each brightfield image to a trained model, and receive an artificial fluorescent image associated with the brightfield image from the trained model. In some embodiments, the trained model can include the generator  408  in  FIG.  4    trained using the process  800  in  FIG.  8   , the trained model  508 , and/or the neural network  600  trained using the process  800  in  FIG.  8   . In some embodiments, the trained model can include a neural network that can receive the input brightfield image and output a single three-channel fluorescent image (e.g., a 256×256×3 image). In some embodiments, the trained model can include three neural networks that can each receive the brightfield image and output a one-channel fluorescent image (e.g., a 256×256×1 image). The one-channel images can then be combined into a single three-channel fluorescent image. The at least one artificial fluorescent image can indicate whether cells included in the tumor organoids are alive or dead. In some embodiments, the process  1300  can apply the trained model to the at least one brightfield image to generate the at least one artificial fluorescent image. 
     At  1320 , the process  1300  can cause the at least one fluorescent image to be output. In some embodiments, the process  1300  can cause the at least one artificial fluorescent image to be output to at least one of a memory (e.g., the memory  220  and/or the memory  240 ) and/or a display (e.g., the display  116 , the display  208 , and/or the display  228 ). The at least one artificial fluorescent image can be used to provide a live/dead count of cells in the organoids. In some embodiments, the process  900  can cause the artificial fluorescent image to be output to an automatic cell counting process in order to get an accurate live/dead count of cells in the artificial fluorescent image. For example, the process  900  can cause the artificial fluorescent image to be output to the CellProfiler available at https://cellprofiler.org. In this way, the process  1300  can automatically generate live/dead counts for multiple wells at multiple time points, which can make drug treatment experiments run faster and gather more data with the same number of wells as compared to standard fluorescent dye imaging techniques that kill cells. 
     In some embodiments, at  1320 , the process  1300  can identify cells in the artificial fluorescent image by converting each of the channels to grayscale, enhancing and suppressing certain features such as speckles, ring shapes, neurites, dark holes, identifying primary objects belonging to the all cell channel where the typical diameters of these objects (in pixel units) is set anywhere between 2 and 20 with a minimum cross entropy thresholding method at a smoothing scale of 1.3488, and identifying primary objects again belonging to the dead cells channel where typical diameter is anywhere between 5 and 20 in pixel units. In this way, the process  1300  can generate a cell count report. 
     In some embodiments, the process  1300  can analyze nucleic acid data associated with the one or more tumor organoids. Each tumor organoid included in the one or more tumor organoids can be associated with a specimen (e.g., the specimen the tumor organoid was harvested from). In some embodiments, each specimen can be associated with a patient. The patient can be associated with patient data that can include nucleic acid data. In some embodiments, the nucleic acid data can include whole exome data, transcriptome data, DNA data, and/or RNA data. The nucleic acid data may be used to further analyze the patient. In some embodiments, the process  1300  can associate the artificial fluorescent image with information about the specimen (e.g., the nucleic acid data). In some embodiments, the process  1300  can provide the artificial fluorescent image and the associated information about the specimen to a database. In some embodiments, the database can include at least seven hundred and fifty artificial fluorescent images. 
     In some embodiments, the process  1300  can generate a report based on the cell count, the cell count report, the nucleic acid data, and/or the artificial fluorescent image. In some embodiments, the process  1300  can cause the report to be output to at least one of a memory (e.g., the memory  220  and/or the memory  240 ) and/or a display (e.g., the display  116 , the display  208 , and/or the display  228 ). The process  1300  can then end. 
     In some embodiments, the process  800  in  FIG.  8   , the process  900  in  FIG.  9   , and/or the process  1300  in  FIG.  13    can be included in the organoids image analysis application  132  in  FIG.  1   . 
       FIG.  14    shows a table representing an exemplary assay or well plate arrangement. More specifically, the table shows an arrangement of treatment therapies by well in a 24×16 well plate. 
     In some embodiments, to populate the well plate with tumor organoids, single cell suspensions of tumor organoid cells can be generated using a predetermined protocol. In some embodiments, to populate a 24×16 well plate, a 24-well plate culture can be dissociated into single cells and seeded in 384-well plates in a mix of 30% Matrigel and 70% media. This setup can allow tumor organoids to form from individual cells for the assay, maintaining tumor organoid heterogeneity in each well. About 2000 cells can be seeded per well allowing enough tumor organoids (TO&#39;s) to form while not overcrowding the plate. 
     The number of usable wells in each 384-well plate can be 330 wells. There can be two sites in each well which get imaged. For a 96-well plate, there can be about 9-16 sites per well that get imaged. In some embodiments, each row in the well plate can receive a different drug. In some embodiments, a control (e.g., Staurosporine) can be fixed in row A. The vehicle can be column  2  where the first half is given DMSO and the second half Staurosporine. In some embodiments, each row can receive drug concentration in technical triplicate. 
     Example 1 
     In this example, Tumor Organoids in each well were stained using three fluorophores for high content fluorescent confocal imaging analysis. In order to obtain the fluorescent readouts a high content imager (ImageXpress Confocal, Molecular Devices) was utilized for data acquisition. Images were acquired at 10× magnification with a 50 micron slit spinning disk aperture. Four channels were acquired using incandescent brightfield, and LED light sources using manufacturer&#39;s default settings for 4′,6-diamidino-2-phenylindole (DAPI), Fluorescein isothiocyanate (FITC), and Cyanine 5 (CY5) to acquire data from Hoechst 33342 (Thermo), Caspase-3/7 reagent (Essen Bioscience), or TO-PRO-3 (Thermo) respectively. 
     In this example, the experimental setup used a 384 well plate, with  330  usable wells within each plate. Since each well has two sites that get imaged, each plate has a total of 660 paired brightfield and fluorescence images. At a magnification of 10×, two images are taken per well at 2 sites with a stack of images in the Z plane ranging from 1-100 heights with increments as high as 15 microns per z plane. The Z stack images are projected to 2D for analysis. The three fluorophores for each brightfield image visualizes all nuclei (Hoechst 33342, blue), apoptotic cells (Caspase-3/7 Apoptosis Assay Reagent, green), and dead cells (TO-PRO-3, red). 
     The final dataset contained registered fluorophores from two patient lines. Patient line A with colon cancer consisted of 9900 paired brightfield and fluorescent images. Patient line B with gastric cancer consisted of 10557 paired brightfield and fluorescent images. 
     A model in accordance with the generator  408  and the discriminator  416  was implemented in Pytorch version 1.0.0. The colon cancer organoid patient line A was selected to evaluate performance for all models analyzed. The organoid line contained 8415 paired brightfield and fluorescent images across 15 experimental plates, which was subjected to an 80-10-10 split for training, test and validation, resulting in 6930 images for training,  742  in validation and  743  for test. Each image was loaded one at a time. Fine tuning of parameters was achieved using only validation set. The learning rate was fixed at 0.0002 for the first 15 epochs of training and consequently linearly decayed to 0 for the next 10 epochs yielding a total of 25 epochs for training. 
     A fixed set of 743 brightfield and corresponding fluorescent images randomly sampled from 15 experimental plates was chosen as a test set. Evaluation for all experiments was performed on the fixed test set. First, the effect of training three separate models (three-model) was evaluated for each fluorophore channel versus training one single model (one-model) on the combined fluorescent readout. For the three-model, predictions for each fluorophore were combined at the end and evaluated. Performance was evaluated both quantitatively using structural similarity index and root mean squared error as well as qualitatively visually using heatmaps. 
     No significant improvement in fluorescent stain prediction was observed when using the three-model, which trained a separate generator for each channel. Table 1 reports the average SSIM and root mean squared error across each channel&#39;s predictions for all 743 test images, and  FIG.  15    shows example images and organoids. Furthermore, because the three-model required three times as many computing resources with only limited RMSE improvement, it was reasoned that a one-model implementation could sufficiently and efficiently perform image-to-image translation from brightfield image to a combined fluorescent readout. In Table 1, lower RMSE and higher SSIM indicates better performance. 
     
       
         
           
               
               
               
               
             
               
                   
                 TABLE 1 
               
               
                   
                   
               
               
                   
                 Experiment 
                 Avg. RMSE 
                 Avg. SSIM 
               
               
                   
                   
               
             
            
               
                   
                 three-model 
                 1.3655 
                 0.92299 
               
               
                   
                 one-model 
                 1.39952 
                 0.92383 
               
               
                   
                   
               
            
           
         
       
     
       FIG.  15    shows an example of images generated using a single neural network model (one-model) and a three neural network model (three-model). A first image  1504  is an example of a ground truth fluorescence image. A second image  1508  is an example of an artificial fluorescent image generated using a one-model with a single neural network that can receive an input brightfield image and output a single three-channel fluorescent image (e.g., a 256×256×3 image). A third image  1512  is an example of an artificial fluorescent image generated using a three-model with three neural networks that can each receive the brightfield image and output a one-channel fluorescent image (e.g., a 256×256×1 image). A fourth image  1516  is an example of a greyscale error map between the second image  1508  and the ground truth image  1504 . A fifth image  1520  is an example of a greyscale error map between the third image  1512  and the ground truth image  1504 . A sixth image  1524 , a seventh image  1528 , an eighth image  1532 , a ninth image  1536 , and a tenth image  1540  are examples of a zoomed-in organoid in the first image  1504 , the second image  1508 , the third image  1512 , the fourth image  1516 , and the fifth image  1520 , respectively. 
     Next, the effect of adding SSIM loss to the 1-model objective function was elaborated. The objective function in Equation 1 is a weighted combination of L1 and SSIM loss (λL1+βSSIM). The influence of SSIM was tested by uniformly evaluating β={0; 0.25; 0.5; 0.75; 1}. Table 2 highlights the performance on the held-out test set using different β. A combination of β=0.75 (and λ=0.25), shows the best performance of the trained model (e.g., the trained model  508 ) in terms of both SSIM and RMSE. 
     
       
         
           
               
               
               
               
             
               
                   
                 TABLE 2 
               
               
                   
                   
               
               
                   
                 Experiment 
                 Avg. RMSE 
                 Avg. SSIM 
               
               
                   
                   
               
             
            
               
                   
                 β = 0 
                 1.39952 
                 0.92383 
               
               
                   
                 β = 1 
                 1.49570 
                 0.91110 
               
               
                   
                 β = 0.25 
                 1.37369 
                 0.92691 
               
               
                   
                 β = 0.5 
                 1.36477 
                 0.92781 
               
               
                   
                 β = 0.75 
                 1.35165 
                 0.92829 
               
               
                   
                   
               
            
           
         
       
     
     To determine if the accuracy of the trained model (β=0.75) was driven by specific improvement in the prediction of a single fluorophore, such as prediction of DAPI (all cells) or FITC (dying/apoptotic cells), the average RMSE and SSIM across each channel were examined. The results are shown in Table 3. 
     
       
         
           
               
               
               
               
             
               
                   
                 TABLE 3 
               
               
                   
                   
               
               
                   
                 Channel 
                 Avg. RMSE 
                 Avg. SSIM 
               
               
                   
                   
               
             
            
               
                   
                 Dead Cells 
                 1.6311 
                 0.92306 
               
               
                   
                 Dead/Dying Cells 
                 1.1015 
                 0.92759 
               
               
                   
                 All Cells 
                 1.7083 
                 0.91807 
               
               
                   
                   
               
            
           
         
       
     
     The model trained with β=0.75 demonstrated consistent RMSE and SSIM scores across all channels. The performance of how the model trained with β=0.75 performed on two new patient colorectral cancer organoid lines (organoids lines B and C) was evaluated. Each new line had a total of 648 brightfield and corresponding fluorescent readouts across different plates. Table 4 demonstrates that a model trained on β=0.75 trained on a single organoid can transfer to other organoid lines. However, the difference between the two lines suggests some limitations. The difference between the two lines suggests that different colorectal cancer organoid lines may present different morphological features that may limit model transfer. In that event, retraining the current best model with some data from organoid line C or employing domain adaptation techniques can facilitate better generalizability to organoid line C. 
     
       
         
           
               
               
               
               
             
               
                   
                 TABLE 4 
               
               
                   
                   
               
               
                   
                 Experiment 
                 Avg. RMSE 
                 Avg. SSIM 
               
               
                   
                   
               
             
            
               
                   
                 Organoid line B 
                 1.42222 
                 0.91062 
               
               
                   
                 Organoid line C 
                 2.03384 
                 0.78431 
               
               
                   
                   
               
            
           
         
       
     
     Example 2 
     Experiments were performed to try and improve model performance. Candidate models included a GANLoss+SSIM model, a GANLoss+SSIM+L1 model trained using a GANLoss+0.17L1+0.83 SSIM model, a GANLoss+MS-SSIM model, and a GANLoss+0.83MS-SSIM+0.17L1 model. 
     Initially, three separate Pix2Pix models were employed to train the individual fluorescent channels. The Avg SSIM and RMSE results over the same 743 blind test images as described in Example 1 are shown below. Tables 5-8 show results of the candidate models implemented in three-model fashion. Table 5 shows results of the GANLoss+SSIM model. Table 6 shows results of the GANLoss+0.83SSIM+0.17L1 model. Table 7 shows results of the GANLoss+MS-SSIM model. Table 8 shows results of the GANLoss+0.83 MS-SSIM+0.17 L1 model. 
     
       
         
           
               
               
               
               
             
               
                   
                 TABLE 5 
               
               
                   
                   
               
               
                   
                 Experiment 
                 Avg RMSE 
                 Avg SSIM 
               
               
                   
                   
               
             
            
               
                   
                 Dead Cells 
                 1.65133 
                 0.91736 
               
               
                   
                 Dead/Dying Cells 
                 1.12827 
                 0.91784 
               
               
                   
                 All cells 
                 1.73630 
                 0.91097 
               
               
                   
                   
               
            
           
         
       
     
     
       
         
           
               
               
               
               
             
               
                   
                 TABLE 6 
               
               
                   
                   
               
               
                   
                 Experiment 
                 Avg RMSE 
                 Avg SSIM 
               
               
                   
                   
               
             
            
               
                   
                 Dead cells 
                 1.56781 
                 0.92950 
               
               
                   
                 Dead/Dying Cells 
                 1.10735 
                 0.92782 
               
               
                   
                 All cells 
                 1.70604 
                 0.92024 
               
               
                   
                   
               
            
           
         
       
     
     
       
         
           
               
               
               
               
             
               
                   
                 TABLE 7 
               
               
                   
                   
               
               
                   
                 Experiment 
                 Avg RMSE 
                 Avg SSIM 
               
               
                   
                   
               
             
            
               
                   
                 Dead cells 
                 1.64130 
                 0.92027 
               
               
                   
                 Dead/Dying Cells 
                 1.13677 
                 0.91956 
               
               
                   
                 All cells 
                 1.72046 
                 0.91725 
               
               
                   
                   
               
            
           
         
       
     
     
       
         
           
               
               
               
               
             
               
                   
                 TABLE 8 
               
               
                   
                   
               
               
                   
                 Experiment 
                 Avg RMSE 
                 Avg SSIM 
               
               
                   
                   
               
             
            
               
                   
                 Dead cells 
                 1.61705 
                 0.92530 
               
               
                   
                 Dead/Dying Cells 
                 1.12485 
                 0.92856 
               
               
                   
                 All cells 
                 1.72099 
                 0.91723 
               
               
                   
                   
               
            
           
         
       
     
     Fluorescent Combined 3 Channel Image Results 
     The results in Table 9 below take the 3 channel pix2pix models per experiment and combine them to form their 3 channel IF counterpart. The individual channels were trained separately and combined by stacking RGB. 
                                     TABLE 9                       Experiment   Avg RMSE   Avg SSIM                          GANLoss + L1   1.36550   0.92299           GANLoss + SSIM   1.38915   0.91558           GANLoss + SSIM + L1   1.33759   0.92586           GANLoss + MS-SSIM   1.39169   0.91875           GANLoss + MS-SSIM + L1   1.37136   0.92372                        
It was observed that GANLoss+SSIM or GANLoss+MS-SSIM standalone do not perform as well as other models. A combination of GANLoss+0.83 SSIM+0.17L1 seems to perform the best. It was also found that GANLoss+L1 and GANLoss+SSIM do not do a good job with detecting blurry bad quality images. The GANLoss+SSIM+L1 model was able to accurately detect blurry artifacts. The GANLoss+SSIM+L1 model recognized artifacts and blurs better than other models and avoided prediction altogether when blurs/artifacts are present in the brightfield image.
 
     Example 3 
     In Example 2, the process of training 3 separate pix2pix models for multiple different objective functions proved to require several GPU&#39;s (3 per model) and extra effort in data curation. A similar performance analysis was done to check if similar/better RMSE and SSIM values were observed by directly training from brightfield to 3 channel fluorescence using a single Pix2Pix model in an attempt to reduce GPU usage. 
     Table 10 below shows the results of directly training to transfer style to IF image for the same set of objective functions on the same test set of 743 images belonging to 10245. The number of GPU&#39;s was reduced from 15 GPU&#39;s to 5 GPU&#39;s and the performance although not too significant, is marginally better. Thus, it may be preferable to use a one-model to generate artificial fluorescent images because performance can be at least as good as a three-model, with one third of the computational requirements. In particular, a one-model trained using an objective function of GANLoss+0.83 MS-SSIM+0.17 L1 model may outperform other one-models and/or three-models trained on the same training data. 
     
       
         
           
               
               
               
               
             
               
                   
                 TABLE 10 
               
               
                   
                   
               
               
                   
                 Experiment 
                 Avg RMSE 
                 Avg SSIM 
               
               
                   
                   
               
             
            
               
                   
                 GANLoss + L1 
                 1.39952 
                 0.92383 
               
               
                   
                 GANLoss + SSIM 
                 1.49570 
                 0.91110 
               
               
                   
                 GANLoss + SSIM + L1 
                 1.35567 
                 0.92890 
               
               
                   
                 GANLoss + MS-SSIM 
                 1.44965 
                 0.91841 
               
               
                   
                 GANLoss + MS-SSIM + L1 
                 1.39880 
                 0.92577 
               
               
                   
                   
               
            
           
         
       
     
     Table 11 below shows results of a number of one-models trained using different objective functions. GANLoss+0.75 SSIM+0.25 L1 had the best RMSE, while GANLoss+0.83 SSIM+17 L1 had the best SSIM performance. 
     
       
         
           
               
               
               
             
               
                 TABLE 11 
               
               
                   
               
               
                 Experiment 
                 Avg RMSE 
                 Avg SSIM 
               
               
                   
               
             
            
               
                 GANLoss + 0.5 SSIM + 0.5 L1 
                 1.36478 
                 0.92781 
               
               
                 GANLoss + 0.5 MSSSIM + 0.5 L1 
                 1.40834 
                 0.92434 
               
               
                 GANLoss + 0.17 SSIM + 0.83 L1 
                 1.34783 
                 0.92641 
               
               
                 GANLoss + 0.17 MSSSIM + 0.83 L1 
                 1.37889 
                 0.92560 
               
               
                 GANLoss + 0.25 SSIM + 0.75 L1 
                 1.37369 
                 0.92691 
               
               
                 GANLoss + 0.25 MSSSIM + 0.75 L1 
                 1.37788 
                 0.92547 
               
               
                 GANLoss + 0.75 SSIM + 0.25 L1 
                 1.35166 
                 0.92830 
               
               
                 GANLoss + 0.75 MSSSIM + 0.25 L1 
                 1.39788 
                 0.92541 
               
               
                 GANLoss + 0.83 SSIM + 0.17 L1 
                 1.35567 
                 0.92890 
               
               
                 GANLoss + 0.83 MSSSIM + 0.17 L1 
                 1.39880 
                 0.92577 
               
               
                   
               
            
           
         
       
     
     Example 4 
     This example details an exemplary cell profiler readout. The cell profiler readout includes all cell counts and dead cell counts of real fluorescent images and corresponding artificial fluorescent images. In Table 12, each row indicates a particular site in a well within an experimental plate and whether it is an artificial or a real image imaged from an ImageXpress Micro microscope. 
     
       
         
           
               
               
               
               
             
               
                 TABLE 12 
               
               
                   
               
               
                 Count_ 
                 Count_PrimaryDead 
                   
                   
               
               
                 Cells 
                 Cells 
                 FileName_Native 
                 ImageNumber 
               
               
                   
               
             
            
               
                 281.0 
                 170.0 
                 Assay10B_10245- 
                 1 
               
               
                   
                   
                 10301_A03_s1_ 
                   
               
               
                   
                   
                 fake B.png 
                   
               
               
                 295.0 
                 107.0 
                 Assay10B_10245- 
                 2 
               
               
                   
                   
                 10301_A03_s1_ 
                   
               
               
                   
                   
                 real_B.png 
                   
               
               
                 269.0 
                 211.0 
                 Assay10B_10245- 
                 3 
               
               
                   
                   
                 10301_A20_s2_ 
                   
               
               
                   
                   
                 fake_B.png 
                   
               
               
                 270.0 
                 210.0 
                 Assay10B_10245- 
                 4 
               
               
                   
                   
                 10301_A20_s2_ 
                   
               
               
                   
                   
                 real_B.png 
                   
               
               
                 549.0 
                 162.0 
                 Assay10B_10245- 
                 5 
               
               
                   
                   
                 10301_C20_s1_ 
                   
               
               
                   
                   
                 fake_B.png 
               
               
                   
               
            
           
         
       
     
     Table 13 below shows plate and well information for each image along with the SSIM/RMSE values. 
     
       
         
           
               
               
               
               
               
               
               
               
               
               
             
               
                 TABLE 13 
               
               
                   
               
               
                   
                 Count_Primary 
                   
                 file 
                   
                   
                   
                   
                   
                   
               
               
                 Count_Cells 
                 DeadCells 
                 file 
                 Name 
                 type 
                 plate 
                 well 
                 welltype 
                 SSIM 
                 RMSE 
               
               
                   
               
             
            
               
                 281.0 
                 170.0 
                 [Assay10B, 10245- 
                 Assay10B_10245- 
                 fake 
                 Assay10B 
                 A03 
                 A 
                 0.947745 
                 1.165460 
               
               
                   
                   
                 10301, A03, s1, fake, B] 
                 10301_A03_s1 
               
               
                 295.0 
                 107.0 
                 [Assay10B,10245- 
                 Assay10B_10245- 
                 real 
                 Assay10B 
                 A03 
                 A 
                 0.947745 
                 1.165460 
               
               
                   
                   
                 10301, A03, s1, real, B] 
                 10301_A03_s1 
               
               
                 269.0 
                 211.0 
                 [Assay10B, 10245- 
                 Assay10B_10245- 
                 fake 
                 Assay10B 
                 A20 
                 A 
                 0.956855 
                 1.011834 
               
               
                   
                   
                 10301, A20, s2, fake, B] 
                 10301_A20_s2 
               
               
                 270.0 
                 210.0 
                 [Assay10B,10245- 
                 Assay10B_10245 
                 real 
                 Assay10B 
                 A20 
                 A 
                 0.956855 
                 1.011834 
               
               
                   
                   
                 10301, A20, s2, real, B] 
                 10301_A20_s2 
               
               
                 549.0 
                 162.0 
                 [Assay10B, 10245- 
                 Assay10B_10245- 
                 fake 
                 Assay10B 
                 C20 
                 C 
                 0.940647 
                 1.194703 
               
               
                   
                   
                 10301, C20, s1, fake, B] 
                 10301_C20_s1 
               
               
                   
               
            
           
         
       
     
     Table 13 shows that the fluorescent images can produce similar cell counts as compared to the corresponding real fluorescent image. 
     Example 5 
     In some embodiments, a large scale drug assay in tumor organoids can increase throughput of the assay. This high throughput screening can be used for validation or testing of drug efficacy or for discovery of novel therapeutics. In some embodiments, 3D TOs may be more similar to a tumor from which they are derived than a 2-dimensional clonal established cell line derived from that tumor. 
     In this example, tumor tissue removed by a biopsy is dissociated into single cells and grown into a 3-dimensional (3D) tumor organoid (TO) culture including TOs. TOs are then dissociated into single cells and grown in a 384-well tissue culture plate for 72 hours. Each well receives either no treatment (or a mock treatment) or a dose (concentration) of a small molecule inhibitor or chemotherapy drugs and the effect of the treatment on the cells in the TO is measured. In one example, over 1,000 drugs may be tested. In another example, several concentrations of 140 drugs may be tested. 
     In one example, the treatment is one of three hundred and fifty-one small molecule inhibitors and seven doses are tested for each treatment on two different organoid types (two organoid cell lines), each derived from a separate patient sample. In this example, one organoid type is a gastric cancer organoid line and the other is a colorectal cancer organoid line. In one example, the effect of the treatment may be measured by counting the number of dead cells and/or viable cells in a well after exposure to a treatment. In this example of fluorescent staining, cell nuclei are stained blue with Hoechst 33342, dying (apoptotic) cells are stained green with Caspase-3/7 Apoptosis Assay Reagent, and dead cells are stained red with TO-PRO-3. 
     In this example, the gastric cancer organoid line has an amplification of the HER2 gene. Afatinib (a drug that targets HER2, among other molecules) and two other drugs that target HER2 kill this gastric cancer organoid line effectively. 
     In some embodiments, the methods and systems described above may be utilized in combination with or as part of a digital and laboratory health care platform that is generally targeted to medical care and research. It should be understood that many uses of the methods and systems described above, in combination with such a platform, are possible. One example of such a platform is described in U.S. patent application Ser. No. 16/657,804, titled “Data Based Cancer Research and Treatment Systems and Methods”, and filed Oct. 18, 2019, which is incorporated herein by reference and in its entirety for all purposes. 
     For example, in some embodiments of the methods and systems described above may include microservices constituting a digital and laboratory health care platform supporting artificial fluorescent image generation and analysis. Some embodiments may include a single microservice for executing and delivering artificial fluorescent image generation or may include a plurality of microservices each having a particular role which together implement one or more of the embodiments above. In one example, a first microservice may execute training data generation in order to deliver training data to a second microservice for training a model. Similarly, the second microservice may execute model training to deliver a trained model according to at least some embodiments. A third microservice may receive a trained model from a second microservice and may execute artificial fluorescent image generation. 
     Some embodiments above can be executed in one or more microservices with or as part of a digital and laboratory health care platform, one or more of such micro-services may be part of an order management system that orchestrates the sequence of events as needed at the appropriate time and in the appropriate order necessary to instantiate embodiments above. A micro-services based order management system is disclosed, for example, in U.S. Prov. Patent Application No. 62/873,693, titled “Adaptive Order Fulfillment and Tracking Methods and Systems”, filed Jul. 12, 2019, which is incorporated herein by reference and in its entirety for all purposes. 
     For example, continuing with the above first and second microservices, an order management system may notify the first microservice that an order for training a model has been received and is ready for processing. The first microservice may execute and notify the order management system once the delivery of training data is ready for the second microservice. Furthermore, the order management system may identify that execution parameters (prerequisites) for the second microservice are satisfied, including that the first microservice has completed, and notify the second microservice that it may continue processing the order to generate a trained model according to some embodiments. 
     When the digital and laboratory health care platform further includes a report generation engine, the methods and systems described above may be utilized to create a summary report of a patient&#39;s genetic profile and the results of one or more insight engines for presentation to a physician. For instance, the report may provide to the physician information about the extent to which a specimen that was used to harvest organoids. For example, the report may provide a genetic profile for each of the tissue types, tumors, or organs in the specimen. The genetic profile may represent genetic sequences present in the tissue type, tumor, or organ and may include variants, expression levels, information about gene products, or other information that could be derived from genetic analysis of a tissue, tumor, or organ. The report may include therapies and/or clinical trials matched based on a portion or all of the genetic profile or insight engine findings and summaries. For example, the therapies may be matched according to the systems and methods disclosed in U.S. Prov. Patent Application No. 62/804,724, titled “Therapeutic Suggestion Improvements Gained Through Genomic Biomarker Matching Plus Clinical History”, filed Feb. 12, 2019, which is incorporated herein by reference and in its entirety for all purposes. For example, the clinical trials may be matched according to the systems and methods disclosed in U.S. Prov. Patent Application No. 62/855,913, titled “Systems and Methods of Clinical Trial Evaluation”, filed May 31, 2019, which is incorporated herein by reference and in its entirety for all purposes. 
     The report may include a comparison of the results to a database of results from many specimens. An example of methods and systems for comparing results to a database of results are disclosed in U.S. Prov. Patent Application No. 62/786,739, titled “A Method and Process for Predicting and Analyzing Patient Cohort Response, Progression and Survival”, and filed Dec. 31, 2018, which is incorporated herein by reference and in its entirety for all purposes. The information may be used, sometimes in conjunction with similar information from additional specimens and/or clinical response information, to discover biomarkers or design a clinical trial. 
     When the digital and laboratory health care platform further includes application of one or more of the embodiments herein to organoids developed in connection with the platform, the methods and systems may be used to further evaluate genetic sequencing data derived from an organoid to provide information about the extent to which the organoid that was sequenced contained a first cell type, a second cell type, a third cell type, and so forth. For example, the report may provide a genetic profile for each of the cell types in the specimen. The genetic profile may represent genetic sequences present in a given cell type and may include variants, expression levels, information about gene products, or other information that could be derived from genetic analysis of a cell. The report may include therapies matched based on a portion or all of the deconvoluted information. These therapies may be tested on the organoid, derivatives of that organoid, and/or similar organoids to determine an organoid&#39;s sensitivity to those therapies. For example, organoids may be cultured and tested according to the systems and methods disclosed in U.S. patent application Ser. No. 16/693,117, titled “Tumor Organoid Culture Compositions, Systems, and Methods”, filed Nov. 22, 2019; and U.S. Prov. Patent Application No. 62/924,621, which are incorporated herein by reference and in their entirety for all purposes. 
     When the digital and laboratory health care platform further includes application of one or more of the above in combination with or as part of a medical device or a laboratory developed test that is generally targeted to medical care and research, such laboratory developed test or medical device results may be enhanced and personalized through the use of artificial intelligence. An example of laboratory developed tests, especially those that may be enhanced by artificial intelligence, is disclosed, for example, in U.S. Provisional Patent Application No. 62/924,515, titled “Artificial Intelligence Assisted Precision Medicine Enhancements to Standardized Laboratory Diagnostic Testing”, and filed Oct. 22, 2019, which is incorporated herein by reference and in its entirety for all purposes. 
     It should be understood that the examples given above are illustrative and do not limit the uses of the systems and methods described herein in combination with a digital and laboratory health care platform. 
     The systems and methods disclosed herein can reduce (1) the time required for imaging the plates (2) the need for toxic dyes that could cause cell death and skew results and (3) the amount of manual labor to add those dyes, allowing larger numbers of drugs or concentrations of drugs to be tested (for example, by a factor of ten). The systems and methods reduce the number of images generated to analyze each plate (for example, by a factor of four or 5, or from 10,000 images to about 2,000-2,500 images) by receiving a brightfield image and predicting the corresponding fluorescent readout and allowing label-free (dye-free) estimation of cell viability (for example, the percentage of cells in a well or in an image that are alive at a given time) at multiple time points. 
     The systems and methods described herein may be used to make hundreds or more measurements in each cell culture well to assess heterogeneity of the drug response (for example, surviving or dying after treatment) of the organoids, which may be done on a per organoid basis (for example, analyzing the cell death or percent of viable cells in each organoid). Multiple measurements include fluorescence intensity, cell growth, cell death, cells per organoid, cells per well, dose response (for example, graphing % cell viability vs. drug dose, calculating a best fit curve or drug dose response curve, and measuring the area above the curve and below the 100% viability intercept), etc. These measurements facilitate determination of cellular mechanisms of a drug response and/or the detection of drug-resistant subpopulations of organoids within an organoid line or within a cell culture well. Drug efficacy (for example, dose response) and specificity may be measured. Drug efficacy of all drugs for one or more organoid lines may be plotted. In one example, the x-axis shows drug efficacy for a first organoid line and the y-axis shows drug efficacy for a second organoid line. In this plot, drugs near the upper right corner were effective against both organoid lines. Drugs near the upper left or lower right corners were effective against one of the organoid lines. 
     Drugs that kill an organoid line may also be categorized and quantified according to their drug target. For example, the thirty most effective drugs that kill an organoid line may be organized by target in a bar graph, showing the number of effective drugs per target. 
     Referring now to  FIG.  16   , a flow for generating an artificial fluorescent image  1616  using a first trained model  1604  and a second trained model  1612  is shown. The first trained model  1604  can generate one or more individual organoids  1608  (e.g., organoid segmentations) based on a brightfield image  1600 . The brightfield image  1600  may contain one or more organoids, and the trained first model  1604  can identify each organoid by segmenting the organoids  1608  from the brightfield image  1600 . In some embodiments, the first trained model can include an artificial neural network. In some embodiments, the artificial neural network can include a Mask-RCNN network. 
     In some embodiments, the first trained model  1604  and the second trained model can be used to predict drug response and other characteristics of an organoid line based on viable cells and/or morphology associated with each individual tumor organoid (TO) in the brightfield image  1600 . 
     Assessing each organoid individually may provide better information about treatments than if the organoids are assessed in place in the brightfield image  1600 . Each TO may represent a different tumor clone present in the specimen. Each TO may exhibit a different therapeutic response to the different drugs at different dosage levels. Instead of assessing viabilities of the TOs by aggregating the viabilities across the entire field-of-view in an image, understanding the distribution of the viabilities at a per-organoid level (for example, how many cells in each organoid are viable) and possibly aggregating the viabilities of the TOs belonging to the same tumor clone may offer a better understanding of the response of organoids to the drugs, and by extension, a better understanding of the response of a patient to the drugs. 
     In some embodiments, the first trained model  1604  can be trained to segment organoids out from the brightfield image  1600  using a training set of brightfield images annotated with bounding boxes around the individual organoids. In some embodiments, the first trained model  1604  can generate masks and bounding boxes around every organoid in the brightfield image. In some embodiments, the first trained model  1604  can generate model embeddings that can be used to generate features based on the organoids in order to assess viability and morphology. 
     In some embodiments, the second trained model  1612  can include the generator  408  trained to generate an artificial fluorescent image based on an input brightfield organoid. The second trained model  1612  can be trained on a training set of individual brightfield organoids and individual fluorescent organoids. Each individual fluorescent organoid can be used to generate a viability. The viabilities for all organoids can be aggregated. A distribution of viabilities per organoid can be generated and/or visualized. In some embodiments, the distribution of live/dead cells per organoid can be calculated to get a prediction or extrapolation of dose response from the distribution of Organoid viability at a single drug concentration. In some embodiments, a process can aggregate the viabilities of different tumor clones among the organoids if side information is available to determine which cropped out TO belongs to which tumor clone. 
     In some embodiments, the morphologies of every organoid can be visualized. In some embodiments, the morphologies of the tumor organoids can be visualized, either by using handcrafted features or model embeddings, and clustering in a supervised or unsupervised setting. In some embodiments, the morphological clusters can be associated with cluster viabilities, and by extension, drug response. In some embodiments, the TO morphology can be used to predict drug response. 
     Referring now to  FIG.  16    as well as  FIG.  17   , a process  1700  for generating fluorescent images of tumor organoids is shown. The process  1700  can be implemented as computer readable instructions on one or more memories or other non-transitory computer readable media, and executed by one or more processors in communication with the one or more memories or other media. In some embodiments, the process  1700  can be implemented as computer readable instructions on the memory  220  and/or the memory  240  and executed by the processor  204  and/or the processor  224 . In some embodiments, the process  1700  can be executed by an imaging system. In some embodiments, a brightfield microscopy imaging system can be configured to execute the process  1700 . In some embodiments, the brightfield microscopy imaging system can include one or more memories or other non-transitory computer readable media including the process  1700  implemented as computer readable instructions on the one or more memories or other non-transitory computer readable media, and one or more processors in communication with the one or more memories or other media configured to execute the computer readable instructions to execute the process  1700 . 
     At  1704 , the process  1700  can receive a brightfield image (e.g., the brightfield image  1600  in  FIG.  16   ) of one or more organoids. In some embodiments, the brightfield image can be preprocessed in order to enhance contrast as described above. In some embodiments, the brightfield image can be a raw image that has not undergone any preprocessing such as contrast enhancement. 
     At  1708 , the process  1700  can determine if the brightfield image is unprocessed (i.e., raw). If the brightfield image is unprocessed (i.e., “YES” at  1708 ), the process  1700  can proceed to  1712 . If the brightfield image is not unprocessed (i.e., “NO” at  1708 ), the process  1700  can proceed to  1716 . 
     At  1712 , the process  1700  can preprocess the brightfield image. In some embodiments, the brightfield image can have pixel intensities ranging from [0, 2 16 ]. In some embodiments, the process  1700  can convert the brightfield image to an unsigned byte format, with values ranging from [0, 255]. In some embodiments, the process  1700  can convert the brightfield image to another format with less bits than the original pixel intensity. The process  1700  can then stretch and clip each pixel intensity to a desired output range. In some embodiments, the process  1700  can determine an input range for the brightfield image by uniformly stretching the 2nd and 98th percentile of pixel intensities in the brightfield image to an output range [0,255]. 
     At  1716 , the process  1700  can provide the brightfield image to a first trained model. In some embodiments, the first trained model can be the first trained model  1604  in  FIG.  16   . In some embodiments, the trained model can a neural network. In some embodiments, the neural network can include a Mask-RCNN model. 
     At  1720 , the process  1700  can receive at least one individual tumor organoid from the first trained model (for example, in a 64×64×1 or 32×32×1 image). Each individual tumor organoid can be a portion of the brightfield image. 
     At  1724 , the process  1700  can provide the at least one individual tumor organoid to a second trained model. In some embodiments, the second trained model can include the second trained model  1612  in  FIG.  16   . In some embodiments, the process  1700  can sequentially provide each individual tumor organoid to the second trained model. In some embodiments, the process  1700  can apply the second trained model to the at least one individual tumor organoid to generate at least one artificial fluorescent image. 
     At  1728 , the process  1700  can receive at least one artificial fluorescent image from the second trained model. Each artificial fluorescent image can be generated based on an individual tumor organoid. The artificial fluorescent image can indicate whether cells included in the tumor organoids are alive or dead. 
     At  1732 , the process  1700  can cause the at least one artificial fluorescent image to be output. In some embodiments, the process  1700  can cause the at least one artificial fluorescent image to be output to at least one of a memory (e.g., the memory  220  and/or the memory  240 ) and/or a display (e.g., the display  116 , the display  208 , and/or the display  228 ). The at least one artificial fluorescent image can be used to provide a live/dead count of cells in each individual organoid. In some embodiments, the process  1700  can cause the at least one artificial fluorescent image to be output to an automatic cell counting process in order to receive an accurate live/dead count of cells, percentage of cells that are viable, and/or a cell count report for each organoid. For example, the process  1700  can cause the at least one artificial fluorescent image to be output to the CellProfiler available at https://cellprofiler.org. In some embodiments, the process  1700  can cause one or more channels of the at least one artificial fluorescent image to be output to an automatic cell counting process in order to receive a cell count report, percentage of cells that are viable, and/or accurate live/dead count of cells in each organoid. In some embodiments, the process  1700  can cause the artificial fluorescent image to be output to a trained model in order to receive a cell count report, percentage of cells that are viable, and/or accurate live/dead count of cells in the artificial fluorescent image. In some embodiments, the process  1700  can cause a combination (e.g., image embeddings combined by concatenation) of the brightfield image and one, two, or three channels of the artificial fluorescent image to be output to an automatic cell counting process in order to receive a cell count report, percentage of cells that are viable, and/or an accurate live/dead count of cells in the artificial fluorescent image. 
     In some embodiments, at  1732 , the process  1700  can identify cells in the artificial fluorescent image by converting each of the channels to grayscale, enhancing and suppressing certain features such as speckles, ring shapes, neurites, dark holes, identifying primary objects belonging to the all cell channel where the typical diameters of these objects (in pixel units) is set anywhere between 2 and 20 with a minimum cross entropy thresholding method at a smoothing scale of 1.3488, and identifying primary objects again belonging to the dead cells channel where typical diameter is anywhere between five and twenty in pixel units. In this way, the process  1700  can generate a cell count report. In some embodiments, the process  1732  can determine if a drug and/or dosage is effective in killing tumor organoid cells based on the live/dead count of cells or percentage of cells that are viable for each organoid. In some embodiments, at  1732 , the process  1700  can extrapolate dose response from a distribution of organoid viability at a single concentration. 
     In some embodiments, the process  1700  can analyze nucleic acid data associated with the one or more tumor organoids. Each tumor organoid included in the one or more tumor organoids can be associated with a specimen (e.g., the specimen the tumor organoid was harvested from). In some embodiments, each specimen can be associated with a patient. The patient can be associated with patient data that can include nucleic acid data. In some embodiments, the nucleic acid data can include whole exome data, transcriptome data, DNA data, and/or RNA data. The nucleic acid data may be used to further analyze the patient. In some embodiments, the process  1700  can associate the artificial fluorescent image with information about the specimen (e.g., the nucleic acid data). In some embodiments, the process  1700  can provide the artificial fluorescent image and the associated information about the specimen to a database. In some embodiments, the database can include at least seven hundred and fifty artificial fluorescent images. 
     In some embodiments, the process  1700  can generate a report based on the cell count, the cell count report, the nucleic acid data and/or the artificial fluorescent image. In some embodiments, the process  1700  can cause the report to be output to at least one of a memory (e.g., the memory  220  and/or the memory  240 ) and/or a display (e.g., the display  116 , the display  208 , and/or the display  228 ). The process  1700  can then end. 
     Example 6—Neural Network-Based Model for Predicting TO Drug Response, and Response Prediction from Brightfield Images in the Absence of Fluorescent Labels 
     In some embodiments, a process can cause a brightfield image and one, two, or three channels of the artificial fluorescent image to be output to an automatic cell counting process (for example, a viability estimation process) in order to receive a percentage of cells in the image that are viable (alive). 
       FIG.  18    illustrates a flow  1800  for predicting a viability  1820  based on a brightfield image  1804 . The brightfield image  1804  can be a three-channel brightfield image of tumor organoids and/or and cells. The flow  1800  can include providing the brightfield image  1804  to a generator  1808 . In some embodiments, the generator  1808  can generate an artificial fluorescent image  1812  based on the brightfield image  1804 . The flow  1800  can include providing the brightfield image  1804  and the artificial fluorescent image  1812  to a discriminator  1816 . The discriminator can generate the viability  1820  based on the brightfield image  1804  and the artificial fluorescent image  1812 . 
     Referring now to  FIG.  18    as well as  FIG.  19   , an exemplary generator  1900  and an exemplary discriminator  1902  are shown. In some embodiments, the discriminator  1902  can be used to train the generator  1900 . In some embodiments, the generator  1900  and the discriminator  1902  can be included in a regularized conditional adversarial (RCA) network. 
     In some embodiments, the generator  1900  can include an encoder-decoder U-Net network. In some embodiments, the U-Net can include skip connections. In some embodiments, the generator  1900  can receive a two-dimensional brightfield image (e.g., a 1024×1024 brightfield image). In some embodiments, the generator  1900  can generate a normalized, three-channel, high-resolution 1024×1024×3 output fluorescent image based on the brightfield image, where the three channels correspond to Hoechst 33342 all nuclei stained readout, Caspase-3/7 apoptotic stained readout, and TOPRO-3 dead cell stained readout, respectively. 
     Referring now to  FIGS.  18  and  19    as well as  FIG.  20   , a discriminator  1904  can generate a viability prediction  1924  based on a brightfield image and an artificial fluorescent image. The discriminator  1904  can include an encoder branch  1908  and a fully-connected branch  1912 . In some embodiments, the encoder branch  1908  can include a 70×70 patchGAN. In some embodiments, the encoder branch  1908  can receive a concatenated brightfield image and a fluorescent image  1916  of size 1024×1024×6. In some embodiments, the encoder branch  1908  can generate an output prediction map  1920  (e.g., an output prediction map of size 126×126×1). The fully-connected branch  1912  can then generate a viability prediction  1924  based on the output prediction map  1920 . In some embodiments, the fully-connected branch  1912  can include a number of fully-connected layers (e.g., two fully-connected layers) and a sigmoid activation layer that outputs the viability prediction  1924 . The viability prediction  1924  can indicate viability. In some embodiments, the viability prediction  1924  can be and/or range from zero (indicative of no viability) to one (indicative of high viability). 
     Training 
     In testing, the generator  1900  and the discriminator  1902  were trained on eight thousand four hundred and fifteen paired brightfield and 3-channel fluorescence images from colon adenocarcinoma TO screening experiments, each with associated calculated drug responses based on TO-PRO-3 viability. In some embodiments, an objective function (for example, loss function used for training) can include an additional mean squared error loss in a discriminator objective to regress against the branch of the discriminator that computes overall viability per brightfield image. Exemplary loss functions for the discriminator  1902  and the generator  1900  are given below: 
         D   Loss =MSE Loss {Real Prediction,1}+MSE Loss {Fake Prediction,0}+MSE Loss {Predicted Viability,Viability} 
         G   Loss =MSE Loss {Fake Prediction,1}+MAE Loss {Fake Fluorescent,Real Fluorescent}+SSIM{Fake Fluorescent,Real Fluorescent} 
     Weights for the discriminator  1902  can be updated by minimizing D Loss , and weights for the generator  1900  can be updated by maximizing G LOSS . 
     Validation 
     In validation, representative images of real versus generated fluorescence demonstrated nearly indistinguishable visual matching. These results were confirmed using two quantitative metrics: the structural similarity index (SSIM) as well as the root mean squared error (RMSE). The reported average SSIM and RMSE values across 1,526 samples of the colon adenocarcinoma TO used in the screening experiment were 0.90 and 0.13924 respectively. For the gastric TO line, the reported average SSIM and RMSE values across 9200 samples were 0.898 and 0.136, respectively. TO description, image analysis, and generation of images for training data 
     TOs were dissociated into single cells and resuspended in a 30:70% mix of GFR Matrigel:growth media at a concentration of 100 cells/μl. The solution was added to 384-well assay plates (Corning) at 20 μl per well for a final concentration of 2,000 cells per well. Assay plates were covered with a Breathe-Easy sealing membrane (Sigma Aldrich) to prevent evaporation. TOs were grown for 72 hours before drug addition. Drugs were prepared in growth media with 2.5 μM Caspase-3/7 Green Apoptosis Assay Reagent (Essen Bioscience). Serial dilutions of each molecule were prepared in 384-well polystyrene plates (Nunc). Seven 10-fold dilutions were made for each compound with the high dose being 10 μM. Select compounds were limited to a high dose of 1 μM by maximum solubility. Diluted drug was added to the assay plate using an Integra Viaflo pipette (Integra) mounted on an Integra Assist Plus Pipetting Robot (Integra). Assay plates were again covered with a Breathe-Easy sealing membrane and TOs were exposed to drugs for another 72 hours before imaging. 
     Prior to imaging, TOs were incubated with 4 μM Hoechst 33342 (Fisher Scientific) and 300 nM TO-PRO-3 Iodide (642/661) (Invitrogen) for 1.5-2 hours. Assay plates were imaged using an ImageXpress Micro Confocal (Molecular Devices) at 10× magnification so that ˜100-200 TOs were imaged per well. The multiplexed fluorescence images were 1024×1024×3 RGB images, where red corresponds to dead cells (TO-PRO-3), green to apoptotic cells (Caspase-3/7), and blue to nuclei (Hoechst 33342). All wavelength channels underwent a simple intensity rescaling contrast enhancement technique to brighten and sharpen the TOs/cells as well as remove background noise. 
     Images were acquired as 4×15 μm Z-stacks and the 2D projections were analyzed to assess cell viability. Confocal images were analyzed using the MetaXpress software (Molecular Devices) custom module editor feature to design an analysis module that identified TOs by clusters of Hoechst 33342 staining, individual cells by Hoechst 33342 staining, and dead/dying cells by either TO-PRO-3 or Caspase-3/7 staining. The result of this analysis module is a spreadsheet detailing the number of live and dead cells for every individual organoid. Each viability value was equal to or greater than 0 (0% of cells viable) and equal to or less than 1 (100% of cells viable). 
     Viability calculation=sum total of all live cells in the site/sum total of all cells in the site (gives a proportion of live cells per site). More effective drugs will have lower viabilities (more cells die) at higher doses compared to less effective drugs, which have higher viabilities. 
     The mean viability for all organoids per site (for example, per image) was obtained from the MetaXpress software readout. For each image added to a training data set used to train the viability discriminator, the image was stored with the mean viability associated with that image as a label or metadata. The images had a resolution of 1024×1024 and were randomly flipped as a data augmentation step before being used as training data. 
     The training data set in this example included seven thousand images representing  15  culture plates. 
     The percentage of viable cells per TO was calculated based on the image analysis described above. TOs with fewer than three cells, TOs larger than the top one percent by size, and wells with fewer than 20 TOs detected were excluded from analysis. 
     In another example, an AUC may be used as metadata or a label to generate training data. The mean viability for all TOs at a given drug concentration was used in dose-response curves to calculate AUC. AUC was calculated using the computeAUC function using settings for “actual” AUC of the R Package PharmacoGx (v1.17.1). Heatmaps of AUC values were generated using the Pheatmap package (v1.0.12) in R. Scatterplots of AUC values were generated using the ggplot2 package (v3.3.0) in R. 
     Referring now to  FIGS.  18 - 20    as well as  FIG.  21   , a process  2100  for generating a viability value is shown. The process  2100  can be implemented as computer readable instructions on one or more memories or other non-transitory computer readable media, and executed by one or more processors in communication with the one or more memories or other media. In some embodiments, the process  2100  can be implemented as computer readable instructions on the memory  220  and/or the memory  240  and executed by the processor  204  and/or the processor  224 . In some embodiments, the process  2100  can be executed by an imaging system. In some embodiments, a brightfield microscopy imaging system can be configured to execute the process  2100 . In some embodiments, the brightfield microscopy imaging system can include one or more memories or other non-transitory computer readable media including the process  2100  implemented as computer readable instructions on the one or more memories or other non-transitory computer readable media, and one or more processors in communication with the one or more memories or other media configured to execute the computer readable instructions to execute the process  2100 . 
     At  2104 , the process  2100  can receive a brightfield image (e.g., the brightfield image  1804  in  FIG.  18   ) of one or more organoids. In some embodiments, the brightfield image can be preprocessed in order to enhance contrast as described above. In some embodiments, the brightfield image can be a raw image that has not undergone any preprocessing such as contrast enhancement. 
     At  2108 , the process  2100  can determine if the brightfield image is unprocessed (i.e., raw). If the brightfield image is unprocessed (i.e., “YES” at  2108 ), the process  2100  can proceed to  2112 . If the brightfield image is not unprocessed (i.e., “NO” at  2108 ), the process  2100  can proceed to  2116 . 
     At  2112 , the process  2100  can preprocess the brightfield image. In some embodiments, the brightfield image can have pixel intensities ranging from [0,2{circumflex over ( )}16]. In some embodiments, the process  2100  can convert the brightfield image to an unsigned byte format, with values ranging from [0, 255]. In some embodiments, the process  2100  can convert the brightfield image to another format with less bits than the original pixel intensity. The process  2100  can then stretch and clip each pixel intensity to a desired output range. In some embodiments, the process  2100  can determine an input range for the brightfield image by uniformly stretching the 2nd and 98th percentile of pixel intensities in the brightfield image to an output range [0,255]. 
     At  2116 , the process  2100  can provide the brightfield image to a trained model. In some embodiments, the trained model can include a generator (e.g., the generator  1808  and/or the generator  1900 ) and the discriminator (e.g., the discriminator  1816  and/or the discriminator  1904 ). In some embodiments, the process  2100  can include providing the brightfield image to the generator, receiving an artificial fluorescent image from the process, concatenating the brightfield image with the artificial fluorescent image to generate a concatenated image, and providing the concatenated image to the discriminator. In some embodiments, the process  2100  can include applying the trained model to the brightfield image to generate the fluorescent image. 
     At  2120 , the process  2100  can receive a viability (e.g., a viability value) from the trained model. In some embodiments, the process  2100  can receive the viability from the discriminator  1904 . In some embodiments, the viability can be the viability  1820  and/or the viability prediction  1924 . 
     At  2124 , the process  2100  can cause the viability to be output. In some embodiments, the process  2100  can cause viability to be output to at least one of a memory (e.g., the memory  220  and/or the memory  240 ) and/or a display (e.g., the display  116 , the display  208 , and/or the display  228 ). In some embodiments, the process  2100  can generate a report based on the viability. In some embodiments, the process  2100  can analyze nucleic acid data associated with the one or more organoids. Each organoid included in the one or more organoids can be associated with a specimen (e.g., the specimen the organoid was harvested from). In some embodiments, each specimen can be associated with a patient. The patient can be associated with patient data that can include nucleic acid data. In some embodiments, the nucleic acid data can include whole exome data, transcriptome data, DNA data, and/or RNA data. The nucleic acid data may be used to further analyze the patient. In some embodiments, the process  2100  can associate the artificial fluorescent image with information about the specimen (e.g., the nucleic acid data). In some embodiments, the process  2100  can provide the artificial fluorescent image, the associated information about the specimen, and/or the viability to a database. In some embodiments, the database can include at least seven hundred and fifty artificial fluorescent images. 
     In some embodiments, the process  2100  can cause the report to be output to at least one of a memory (e.g., the memory  220  and/or the memory  240 ) and/or a display (e.g., the display  116 , the display  208 , and/or the display  228 ). The process  2100  can then end. 
     The present disclosure has described one or more preferred embodiments, and it should be appreciated that many equivalents, alternatives, variations, and modifications, aside from those expressly stated, are possible and within the scope of the invention.