METHODS, APPARATUSES, AND COMPUTER PROGRAM PRODUCTS FOR ANALYZING IMAGE DATA RELATED TO FLUID SAMPLES

Example methods, apparatuses, and computer program products related to analyzing fluid samples are provided. For example, an example computer-implemented method for analyzing fluid samples includes receiving digital holography image data associated with a fluid sample in a flow chamber device; extracting, from the digital holography image data, an upper reference mark image region associated with an upper reference mark and a lower reference mark image region associated with a lower reference mark; determining a maximum focal depth and a minimum focal depth associated with the digital holography image data, respectively; focusing each of a plurality of focal depth layers associated with the digital holography image data; and extracting, from the plurality of focal depth layers, one or more region of interest (ROI) portions that are associated with the fluid sample.

FIELD OF THE INVENTION

Example embodiments of the present disclosure relate generally to performing fluid sample analysis and include, for example, methods, apparatuses and computer program products for analyzing digital holography image data associated with fluid samples.

BACKGROUND

Applicant has identified many technical challenges and difficulties associated with analyzing fluid samples. For example, many methods and systems fail to provide an effective mechanism that allows peritoneal dialysis (PD) effluent to be properly analyzed.

BRIEF SUMMARY

Various embodiments described herein relate to methods, apparatuses, and systems for analyzing image data related to fluid samples.

In accordance with various embodiments of the present disclosure, an example computer-implemented method for analyzing fluid samples is provided. In some embodiments, the example computer-implemented method comprises receiving digital holography image data associated with a fluid sample in a flow chamber device comprising an upper reference mark on an upper surface of the flow chamber device and a lower reference mark on a lower surface of the flow chamber device; and determining a maximum focal depth and a minimum focal depth associated with the digital holography image data based at least in part on an upper reference mark image region and a lower reference mark image region of the digital holography image data, respectively.

In some embodiments, the computer-implemented method comprises extracting, from the digital holography image data, the upper reference mark image region associated with the upper reference mark and the lower reference mark image region associated with the lower reference mark.

In some embodiments, the computer-implemented method comprises focusing each of a plurality of focal depth layers associated with the digital holography image data based at least in part on the maximum focal depth and the minimum focal depth; and extracting, from the plurality of focal depth layers, one or more region of interest (ROI) portions associated with the fluid sample.

In some embodiments, the digital holography image data is received from an imaging device that is positioned under the lower surface of the flow chamber device.

In some embodiments, the maximum focal depth corresponds to a first focal depth between the upper surface of the flow chamber device and the imaging device. In some embodiments, the minimum focal depth corresponds to a second focal depth between the lower surface of the flow chamber device and the imaging device.

In some embodiments, the upper reference mark image region is extracted from the digital holography image data based at least in part on an upper reference mark location associated with the upper reference mark. In some embodiments, the lower reference mark image region is extracted from the digital holography image data based at least in part on a lower reference mark location associated with the lower reference mark.

In some embodiments, the computer-implemented method further comprises focusing the upper reference mark image region based at least in part on an Angular Spectrum Propagation (ASP) based image focusing algorithm. In some embodiments, the upper reference mark is in focus from the upper reference mark image region at the maximum focal depth.

In some embodiments, the computer-implemented method further comprises focusing the lower reference mark image region based at least in part on an ASP-based image focusing algorithm. In some embodiments, the lower reference mark is in focus from the lower reference mark image region at the minimum focal depth.

In some embodiments, at least one of the upper reference mark or the lower reference mark comprises an authentication indicium. In some embodiments, the computer-implemented method further comprises: extracting authentication data associated with at least one of the upper reference mark or the lower reference mark.

In some embodiments, the computer-implemented method further comprises: extracting a fluid sample relevant image region from the digital holography image data. In some embodiments, the upper reference mark and the lower reference mark are not on the fluid sample relevant image region.

In some embodiments, focusing each of the plurality of focal depth layers associated with the digital holography image data further comprises focusing only the fluid sample relevant image region.

In some embodiments, focusing each of the plurality of focal depth layers associated with the digital holography image data further comprising: determining a focal depth layer count number associated with the plurality of focal depth layers; and calculating a corresponding focal depth range associated with each of the plurality of focal depth layers based at least in part on the maximum focal depth, the minimum focal depth, and the focal depth layer count number.

In some embodiments, the fluid sample comprises one or more particles. In some embodiments, extracting the one or more ROI portions further comprises: determine a plurality of candidate ROI portions associated with the one or more particles of the fluid sample; and determining, for each of the one or more particles, an optimally focused ROI portion from the plurality of candidate ROI portions.

In accordance with various embodiments of the present disclosure, an apparatus for analyzing fluid samples is provided. In some embodiments, the apparatus comprises at least one processor and at least one non-transitory memory comprising program code. In some embodiments, the at least one non-transitory memory and the program code are configured to, with the at least one processor, cause the apparatus to at least: receive digital holography image data associated with a fluid sample in a flow chamber device comprising an upper reference mark on an upper surface of the flow chamber device and a lower reference mark on a lower surface of the flow chamber device; and determine a maximum focal depth and a minimum focal depth associated with the digital holography image data based at least in part on an upper reference mark image region and a lower reference mark image region of the digital holography image data, respectively. In some embodiments, the flow chamber device is removable or replaceable.

In accordance with various embodiments of the present disclosure, a computer program product for analyzing fluid samples is provided. In some embodiments, the computer program product comprises at least one non-transitory computer-readable storage medium having computer-readable program code portions stored therein. In some embodiments, the computer-readable program code portions comprise an executable portion configured to: receive digital holography image data associated with a fluid sample in a flow chamber device comprising an upper reference mark on an upper surface of the flow chamber device and a lower reference mark on a lower surface of the flow chamber device; and determine a maximum focal depth and a minimum focal depth associated with the digital holography image data based at least in part on an upper reference mark image region and a lower reference mark image region of the digital holography image data, respectively; focus each of a plurality of focal depth layers associated with the digital holography image data based at least in part on the maximum focal depth and the minimum focal depth; and extract, from the plurality of focal depth layers, one or more ROI portions associated with the fluid sample.

The foregoing illustrative summary, as well as other exemplary objectives and/or advantages of the disclosure, and the manner in which the same are accomplished, are further explained in the following detailed description and its accompanying drawings.

DETAILED DESCRIPTION OF THE INVENTION

As used herein, terms such as “front,” “rear,” “top,” etc. are used for explanatory purposes in the examples provided below to describe the relative position of certain components or portions of components. Furthermore, as would be evident to one of ordinary skill in the art in light of the present disclosure, the terms “substantially” and “approximately” indicate that the referenced element or associated description is accurate to within applicable engineering tolerances.

As described above, there are many technical challenges and difficulties associated with analyzing fluid samples including, but not limited to, those related to medical analysis in renal care.

“Renal care” refers to medical care that provides diagnosis and treatment associated with the kidney (including but not limited to, chronic renal disease and/or acute renal disease). For example, when the patient's kidney stops working properly, renal care may include, but not limited to, performing dialysis procedures. The dialysis procedures are designed to remove waste products and excess fluids from the blood of the patient, and therefore improve the health of the patient.

While peritoneal dialysis (PD) provides a mechanism to remove waste products from a patient's blood when the patient's kidneys cannot adequately function, PD is different from traditional hemodialysis (HD).

In particular, during an example PD procedure, a cleansing fluid (such as, but not limited to, a dialysis solution such as water with sugar and other additive) flows through a tube or a pipe (such as, but not limited to, a catheter) into the patient's body. More specifically, the cleansing fluid is injected into a part of a patient's abdomen. When the cleansing fluid is inside the patient's body, the cleansing fluid absorbs waste products from the patient's body. The lining of the abdomen (also known as peritoneum) can act as a filter and remove waste products from the patient's blood. After a set period of time, the fluid with the filtered waste products (referred herein as peritoneal dialysis (PD) effluent) flows out of the patient's abdomen and can be discarded.

However, PD procedures are faced with some drawbacks. One of the drawbacks is that patients who undergo PD may develop infections, which can force patients to switch back to HD. As such, early detection of infections after a patient undergoes PD can be beneficial for alerting patients, as well as care providers, so that early action can be taken to limit the severity and frequency of infections.

Various embodiments of the present disclosures enable such early detection of infections while a patient undergoes PD.

For example, various embodiments of the present disclosures provide a fluid sample imaging system that provides an effective mechanism to sample PD effluent and capture image data associated with the PD effluent. In some embodiments, the fluid sample imaging system works in tandem with a PD machine (also referred to as a “cycler”). For example, the fluid sample imaging system may be integrated into the PD machine. Additionally, or alternatively, the fluid sample imaging system may operate as a stand-alone device that is connected to the fluid conduit from the PD machine to receive the PD effluent. In particular, the fluid sample imaging system comprises a flow chamber device. As the PD effluent is pumped out of the patient's body, some of the fluid passes through the flow chamber device.

The fluid sample imaging system may also include an imaging device that can generate digital holography image data of the PD effluent. In some embodiments, after the digital holography image data is generated, the fluid sample imaging system may upload the digital holography image data to a remote computing platform (for example, one or more remote computing servers that are in data communications with one another). In some embodiments, the digital holography image data comprises digital holography image(s) of the PD effluent, and the remote computing platform can computationally generate reconstructed/focused image(s) based on the digital holography image(s) using a computer algorithm based on Angular Spectrum Propagation (ASP). In some embodiments, estimated sample characteristics data associated with the fluid sample can be determined based on the reconstructed/focused image(s). For example, the reconstructed/focused image(s) can be provided to one or more machine learning (ML) models to detect, count, classify, and/or measure the sizes of the detected particles and cells from the PD effluent as shown in the reconstructed/focused image(s).

In some embodiments, the results (e.g. estimated sample characteristics data) from the ML models can be provided to mobile computing devices operated by end users (for example, patients, healthcare providers, etc.), enabling near-real-time analysis of the PD fluid contents and detection of infection. For example, the more white blood cells that there are in the PD effluent, the more likely that the patient is having an infection as the white blood cells make the PD effluent cloudy. As such, various embodiments of the present disclosure can detect indicators of infections based on the PD effluent, and can have the potential to detect infections earlier (which can lead to better patient outcomes) and provide better specificity in the detection results (for example, based on the concentrations of white blood cells and/or types of white blood cells).

However, there are many technical challenges and difficulties associated with analyzing the digital holography image data of the PD effluent and ensuring that the generated image quality is sufficient for accurate detection and counting of white blood cells in the PD effluent.

For example, particles of interest in a fluid sample (such as, but not limited to, white blood cells in a PD effluent) may be not captured on a single imaging plane. In many instances, particles of interest (such as, but not limited to, white blood cells in a PD effluent) can be suspended at various depths within the flow channel of the fluid flow chamber as the fluid sample (such as PD effluent) flows through the flow channel.

In some embodiments, an example ASP-based image focusing algorithm may automatically determine a global focal depth for a global “best focus” of the digital holography image within a pre-specified focal depth search range. However, if there are too many variations in the actual depth Z values of particles of interest relative to the global focal depth, not all particles of interest (such as, but not limited to, white blood cells in a PD effluent) can be in focus in the global “best focus” image based on the digital holography image. As such, many ASP-based image focusing algorithms fail to generate a sufficiently focused image where all particles of interest (such as, but not limited to, white blood cells in a PD effluent) are in focus, resulting in the detection, count, classification, and/or measurement of the sizes of the detected particles and cells from the PD effluent to be inaccurate.

Additionally, mechanical variations in the fluid sample imaging system may cause many technical challenges and difficulties in analyzing the digital holography image data.

For example, an example fluid sample imaging system may include a flow chamber device and an imaging device that is positioned under the flow chamber device for generating digital holography image data. In some embodiments, there may be variations (within tolerance) in the actual dimensions of the flow chamber device from the designed dimensions of the flow chamber device due to its manufacturing process. Similarly, there may be variations (within tolerance) in the actual dimensions of the imaging device from the designed dimensions of the imaging device due to its manufacturing process. Additionally, or alternatively, the flow chamber device may need to be replaced regularly, and inevitable variations in the insertion locations of the flow chamber device may cause small shifts in the relative distance between the image sensing surface of the imaging device and the fluid sample in the flow chamber device (in X, Y, and/or Z dimensions). As such, mechanical variations can cause the minimum focal depth Zmin between the imaging device and the flow chamber device and/or the maximum focal depth Zmax between the imaging device and the flow chamber device to shift. As the focal depth shifts, many example ASP-based image focusing algorithms cannot generate an accurately focused image based on the digital holography image data to be used by a ML model to detect, count, classify, and/or measure the sizes of the detected particles and cells from the PD effluent.

In contrast, various example embodiments of the present disclosure overcome such technical challenges and difficulties in analyzing fluid samples, and provide various technical advancements and improvements.

For example, various embodiments of the present disclosure provide reference marks on the upper interior surface and the lower interior surface of the flow chamber device. The reference marks can provide focal plane references for determining the Zmax value and the Zmin value, which can be used to calibrate an example ASP-based image focusing algorithm. Because the reference marks can provide clear indicators for determining the Zmax value and the Zmin value, various embodiments of the present disclosure overcome technical challenges and difficulties related to shifting Zmax value and Zmin value due to mechanical variations.

By implementing the reference marks, various embodiments of the present disclosure also prevent an example ASP-based image focusing algorithm from focusing on debris and/or defects on the external surfaces of the flow chamber device, especially when there are low concentrations of cells/particles of interest (which can be a common condition). As such, particles on the external surfaces will be out of focus in images generated by an example ASP-based image focusing algorithm in some embodiments of the present disclosure, and therefore are less likely to interfere with the subsequent detection, counting, classification, and/or measurement the sizes of the detected particles and cells from the PD effluent by a ML model.

In addition, the refractive index of the fluid sample in the flow chamber device may vary over time, which may in turn cause shifts in the apparent focal depth Z range between the flow chamber device and the imaging device as estimated by an example ASP-based image focusing algorithm. By focusing on the reference marks, various embodiments of the present disclosure automatically adjusts the apparent focal depth Z range when the refractive index of the fluid sample in the flow chamber device varies, therefore reducing inaccuracies in estimating the apparent focal depth Z range for calibrating the example ASP-based image focusing algorithm.

Further, as described above, an example ASP-based image focusing algorithm may automatically determine a focal depth within a pre-specified focal depth search range for a global “best focus” image based on the digital holography image. Various embodiments of the present disclosure may calibrate the focal depth search range used by the ASP-based image focusing algorithm based on the focal depth Z values associated with the reference marks. Subsequently, various embodiments of the present disclosure may segment the focal depth Z range into multiple focal depth segments/layers (such as, but not limited to, four focal depth layers). Because the reference marks can provide focal plane references, various embodiments of the present disclosure enable the focal depth Z range to be segmented properly.

Various embodiments of the present disclosure may implement the ASP-based image focusing algorithm on each of the focal depth segments/layers to generate a “locally” focused image, and then combine results from different focal depth segments/layers to generate an optimally focused image. For example, various embodiments of the present disclosure provide optimization of focus for individual particles/cells of interest in the fluid sample (such as, but not limited to, white blood cells with cell diameters approximately between 12 microns and 15 microns in the PD effluent that is flowing within a flow channel with a depth approximately between 0.2 millimeters and 0.8 millimeters). As such, various embodiments of the present disclosure increase the speed and the accuracy in analyzing digital holography image data and generating focused images based on the digital holography image data.

Continuing from the PD effluent example described above, various embodiments of the present disclosure provide a fluid sample imaging system that can be used in conjunction/separation with a PD machine to capture a digital holography image when the PD effluent fluid is passed through a flow chamber device. The captured image may be uploaded to a cloud server (or, additionally or alternatively, processed by a processor component in the fluid sample imaging system as described herein), where an ASP-based image focusing algorithm is applied on the reference mark regions of the digital holography image to determine focal depth Z value of the reference marks. The ASP-based image focusing algorithm generates focused images based at least in part on determined Z values, and the focused images are fed to ML models to detect, classify, and/or count the detected particles and cells. As such, various embodiments of the present disclosure not only mitigate many technical challenges and difficulties that need to be overcome for analyzing image data related to fluid samples from realistic environments, but also enable additional diagnostic and analytical functionalities that may be useful for enhanced capabilities in image data analysis, details of which are described herein.

Referring now toFIG.1, an example diagram illustrating an example fluid sample analytics platform100in accordance with some example embodiments described herein is provided.

As shown inFIG.1, the example fluid sample analytics platform100may comprise apparatuses, devices, and components such as, but not limited to, a fluid sample imaging system107, one or more mobile computing devices101A . . .101N, a remote computing server105in a remote computing platform, and one or more networks103.

In some embodiments, each of the components of the example fluid sample analytics platform100may be in electronic communication with, for example, one another over the same or different wireless or wired networks103including, for example, a wired or wireless Personal Area Network (PAN), Local Area Network (LAN), Metropolitan Area Network (MAN), Wide Area Network (WAN), and/or the like. Additionally, whileFIG.1illustrates certain system entities as separate, standalone entities, the various embodiments are not limited to this particular architecture.

For example, the fluid sample imaging system107, one or more mobile computing devices101A . . .101N, the remote computing server105in the remote computing platform may be in electronic communication with one another to exchange data and information. As described herein, the fluid sample imaging system107may receive a fluid sample (such as, but not limited to, peritoneal dialysis effluent, urine, oil, blood, and/or the like) and may comprise an imaging device that generates digital holography image data associated with the fluid sample. In some embodiments, the fluid sample imaging system107may transmit the digital holography image data to the one or more mobile computing devices101A . . .101N and/or the remote computing server105in the remote computing platform for analysis.

In some embodiments, the one or more mobile computing devices101A . . .101N and/or the remote computing server105in the remote computing platform may receive the digital holography image data from the imaging device of the fluid sample imaging system107, and may generate estimated sample characteristics data associated with the fluid sample based at least in part on the digital holography image data. For example, the one or more mobile computing devices101A . . .101N and/or the remote computing server105may generate one or more focused images based on the digital holography image data in accordance with various example methods described herein, including, but not limited to, those described in connection with at leastFIG.5toFIG.8. In some embodiments, the one or more mobile computing devices101A . . .101N and/or the remote computing server105may provide the one or more focused images to one or more machine learning (ML) models.

The term “machine learning model” refers to a computer algorithm that may perform one or more specific tasks through pattern/interference recognition and without the need for explicit instructions. Example machine learning models may include, but not limited to, deep learning models, ensemble models, regression models, and/or the like. For example, the one or more mobile computing devices101A . . .101N and/or the remote computing server105in the remote computing platform may implement an example recurrent neural network (RNN) to analyze the focused images to generate estimated sample characteristics data. In such an example, the example RNN may be trained to detect, count, classify, and/or measure the sizes of the detected particles and cells from the focused images. In particular, the example RNN may comprise one or more layers of interconnected nodes, where each node may produce one or more output vectors based on one or more input vectors. The computing entity may provide the focused images generated by the ASP-based image focusing algorithm as input vectors to the input layer of an example RNN, and nodes in the input layer may produce one or more output vectors, which may be fed into the next layer of nodes. Eventually, the example RNN may output estimated sample characteristics data.

In some embodiments, the estimated sample characteristics data comprises an estimated number of white blood cells within the fluid sample, an estimated concentration level of white blood cells within the fluid sample, estimated size values of particles within the fluid sample, and/or the like. In some embodiments, the one or more mobile computing devices101A . . .101N and/or the remote computing server105may transmit the estimated sample characteristics data to another device (such as, but not limited to, the fluid sample imaging system107, one of the one or more mobile computing devices101A . . .101N, and/or another remote computing server in the remote computing platform).

While the description above provides an example of analyzing digital holography image data, it is noted that the scope of the present disclosure is not limited to the description above. In some embodiments, the fluid sample imaging system107may additionally or alternatively determine estimated sample characteristics data associated with the fluid sample based at least in part on the digital holography image data. For example, the fluid sample imaging system107may comprise a processor component, similar to the processor component of the one or more mobile computing devices101A . . .101N and/or the remote computing server105described herein. In some embodiments, the processor component of the fluid sample imaging system107may generate one or more focused images based on the digital holography image data in accordance with various example methods described herein, including, but not limited to, those described in connection with at leastFIG.5toFIG.8. In some embodiments, the processor component of the fluid sample imaging system107may provide the one or more focused images to one or more ML models to generate estimated sample characteristics data, similar to those described above.

While the description above provides an example fluid sample analytics platform, it is noted that the scope of the present disclosure is not limited to the description above. In some examples, an example fluid sample analytics platform may comprise one or more additional and/or alternative elements. For example, an example fluid sample analytics platform in accordance with embodiments of the present disclosure may comprise more than one fluid sample imaging system. Additionally, or alternatively, an example fluid sample analytics platform in accordance with embodiments of the present disclosure may comprise more than one remote computing server and/or more than one remote computing platform.

Referring now toFIG.2, an example schematic representation of an example mobile computing device in accordance with some example embodiments described herein is provided. For example,FIG.2provides an illustrative schematic representative of one of the mobile computing devices101A to101N that can be used in conjunction with embodiments of the present disclosure.

In some embodiments, the mobile computing device101A can include an antenna212, a transmitter204(e.g., radio), a receiver206(e.g., radio), and a processor component208that provides signals to and receives signals from the transmitter204and receiver206, respectively. The signals provided to and received from the transmitter204and the receiver206, respectively, may include signaling information/data in accordance with an air interface standard of applicable wireless systems to communicate with various entities, such as a remote computing server105, another mobile computing device101A, an example fluid sample imaging system and/or the like. In this regard, the mobile computing device101A may be capable of operating with one or more air interface standards, communication protocols, modulation types, and access types. More particularly, the mobile computing device101A may comprise a network interface220, and may operate in accordance with any of a number of wireless communication standards and protocols. In a particular embodiment, the mobile computing device101A may operate in accordance with multiple wireless communication standards and protocols, such as GPRS, UMTS, CDMA1900, 1×RTT, WCDMA, TD-SCDMA, LTE, E-UTRAN, EVDO, HSPA, HSDPA, Wi-Fi, WiMAX, UWB, IR protocols, Bluetooth protocols, USB protocols, and/or any other wireless protocol.

Via these communication standards and protocols, the mobile computing device101A can communicate with various other entities using Unstructured Supplementary Service data (USSD), Short Message Service (SMS), Multimedia Messaging Service (MMS), Dual-Tone Multi-Frequency (DTMF) Signaling, Subscriber Identity Module Dialer (SIM dialer), and/or the like. The mobile computing device101A can also download changes, add-ons, and updates, for instance, to its firmware, software (e.g., including executable instructions, applications, program modules), and operating system.

The mobile computing device101A may also comprise a user interface comprising one or more user input/output interfaces (e.g., a display216and/or speaker/speaker driver coupled to a processor component208and a touch screen, keyboard, mouse, and/or microphone coupled to a processor component208). For example, the user output interface may be configured to provide an application, browser, user interface, dashboard, webpage, and/or similar words used herein interchangeably executing on and/or accessible via the mobile computing device101A to cause display or audible presentation of information/data and for user interaction therewith via one or more user input interfaces. The user output interface may be updated dynamically from communication with the remote computing server105. The user input interface can comprise any of a number of devices allowing the mobile computing device101A to receive data, such as a keypad218(hard or soft), a touch display, voice/speech or motion interfaces, scanners, readers, or other input device. In embodiments including a keypad218, the keypad218can include (or cause display of) the conventional numeric (0-9) and related keys (#, *), and other keys used for operating the mobile computing device101A and may include a full set of alphabetic keys or set of keys that may be activated to provide a full set of alphanumeric keys. In addition to providing input, the user input interface can be used, for example, to activate or deactivate certain functions, such as screen savers and/or sleep modes. Through such inputs the mobile computing device101A can collect information/data, user interaction/input, and/or the like.

Referring now toFIG.3, an example schematic representation of an example remote computing server105in an example remote computing platform in accordance with some example embodiments described herein. In some embodiments, the example remote computing platform may be a cloud computing platform, and the example remote computing server may be a cloud computing server.

As indicated, in some embodiments, the remote computing server105may include one or more network and/or communications interface307for communicating with various computing entities, such as by communicating data, content, information, and/or similar terms used herein that can be transmitted, received, operated on, processed, displayed, stored, and/or the like. For instance, the remote computing server105may communicate with fluid sample imaging system107, one or more mobile computing devices101A . . .101N, and/or the like.

As shown inFIG.3, in one embodiment, the remote computing server105may include or be in communication with one or more processor components (for example, processor component301) (also referred to as processor components, processing circuitry, and/or similar terms used herein interchangeably) that communicate with other elements within the remote computing server105via a bus, for example, or network connection. As will be understood, the processor component301may be embodied in a number of different ways. For example, the processor component301may be embodied as one or more complex programmable logic devices (CPLDs), microprocessor components, multi-core processor components, co-processing entities, application-specific instruction-set processor components (ASIPs), and/or controllers. Further, the processor component301may be embodied as one or more other processing devices or circuitry. The term circuitry may refer to an entirely hardware embodiment or a combination of hardware and computer program products. Thus, the processor component301may be embodied as integrated circuits, application specific integrated circuits (ASICs), field programmable gate arrays (FPGAs), programmable logic arrays (PLAs), hardware accelerators, other circuitry, and/or the like. As will therefore be understood, the processor component301may be configured for a particular use or configured to execute instructions stored in volatile or non-volatile media or otherwise accessible to the processor component301. As such, whether configured by hardware or computer program products, or by a combination thereof, the processor component301may be capable of performing steps or operations according to embodiments of the present disclosure when configured accordingly.

In one embodiment, the remote computing server105may further include or be in communication with volatile media (also referred to as volatile storage, memory, memory storage, memory circuitry and/or similar terms used herein interchangeably). In one embodiment, the volatile storage or memory may also include one or more memory element303as described above, such as RAM, DRAM, SRAM, FPM DRAM, EDO DRAM, SDRAM, DDR SDRAM, DDR2 SDRAM, DDR3 SDRAM, RDRAM, RIMM, DIMM, SIMM, VRAM, cache memory, register memory, and/or the like. As will be recognized, the volatile storage or memory element303may be used to store at least portions of the databases, database instances, database management system entities, data, applications, programs, program modules, scripts, source code, object code, byte code, compiled code, interpreted code, machine code, executable instructions, and/or the like being executed by, for example, the processor component301as shown inFIG.3. Thus, the databases, database instances, database management system entities, data, applications, programs, program modules, scripts, source code, object code, byte code, compiled code, interpreted code, machine code, executable instructions, and/or the like may be used to control certain aspects of the operation of the remote computing server105with the assistance of the processor component301and operating system.

In one embodiment, the remote computing server105may further include or be in communication with non-volatile media (also referred to as non-volatile storage, memory, memory storage, memory circuitry and/or similar terms used herein interchangeably). In one embodiment, the non-volatile storage or memory may include one or more non-volatile storage or storage media305as described above, such as hard disks, ROM, PROM, EPROM, EEPROM, flash memory, MMCs, SD memory cards, Memory Sticks, CBRAM, PRAM, FeRAM, RRAM, SONOS, racetrack memory, and/or the like. As will be recognized, the non-volatile storage or storage media305may store databases, database instances, database management system entities, data, applications, programs, program modules, scripts, source code, object code, byte code, compiled code, interpreted code, machine code, executable instructions, and/or the like. The term database, database instance, database management system entity, and/or similar terms used herein interchangeably and in a general sense to refer to a structured or unstructured collection of information/data that is stored in a computer-readable storage medium.

Storage media305may also be embodied as a data storage device or devices, as a separate database server or servers, or as a combination of data storage devices and separate database servers. Further, in some embodiments, storage media305may be embodied as a distributed repository such that some of the stored information/data is stored centrally in a location within the system and other information/data is stored in one or more remote locations. Alternatively, in some embodiments, the distributed repository may be distributed over a plurality of remote storage locations only. An example of the embodiments contemplated herein would include a cloud data storage system maintained by a third-party provider and where some or all of the information/data required for the operation of the recovery prediction system may be stored.

As will be appreciated, one or more of the remote computing server's components may be located remotely from components of other remote computing servers, such as in a distributed system. Furthermore, one or more of the components may be aggregated and additional components performing functions described herein may be included in the remote computing server105. Thus, the remote computing server105can be adapted to accommodate a variety of needs and circumstances.

Referring now toFIG.4AandFIG.4B, example schematic representations of example views of an example fluid sample imaging system400are provided. In particular,FIG.4Aprovides an example schematic representation of an example side view of the example fluid sample imaging system400.FIG.4Bprovides an example schematic representation of an example top view of the example fluid sample imaging system400.

In the example shown inFIG.4A, the fluid sample imaging system400comprises an illumination device402, a flow chamber device404, and an imaging device406.

In some embodiments, the flow chamber device404comprises an upper flow chamber substrate408and a lower flow chamber substrate410.

In some embodiments, the upper flow chamber substrate408may comprise transparent, semi-transparent, and/or translucent materials. For example, the upper flow chamber substrate408may comprise glass. Additionally, or alternatively, the upper flow chamber substrate408may comprise other material(s). In some embodiments, the upper flow chamber substrate408may comprise material(s) that allow light beams to pass through.

In some embodiments, the upper flow chamber substrate408may be in shape similar to a rectangular shape. For example, the upper flow chamber substrate408may be shaped similar to a microscope slide. Additionally, or alternatively, the upper flow chamber substrate408may be in other shapes.

Similarly, in some embodiments, the lower flow chamber substrate410may comprise transparent, semi-transparent, and/or translucent materials. For example, the lower flow chamber substrate410may comprise glass. Additionally, or alternatively, the lower flow chamber substrate410may comprise other material(s). In some embodiments, the lower flow chamber substrate410may comprise material(s) that allow light beams to pass through.

Similarly, in some embodiments, the lower flow chamber substrate410may be in shape similar to a rectangular shape. For example, the lower flow chamber substrate410may be shaped similar to a microscope slide. Additionally, or alternatively, the lower flow chamber substrate410may be in other shapes.

In some embodiments, the upper flow chamber substrate408is positioned above the lower flow chamber substrate410. In some embodiments, the flow chamber device404may define a hollow portion that forms a flow channel. For example, the flow channel within the flow chamber device404may be in the form of a cavity that is between the upper flow chamber substrate408and the lower flow chamber substrate410. In some embodiments, the flow channel provides a passageway for a fluid sample412to flow inside the flow chamber device404. For example, the flow chamber device404may comprise an fluidic inlet that injects the fluid sample412into the flow channel of the flow chamber device404, and may comprise a fluidic outlet where the fluid sample412may be discharged from the flow channel of the flow chamber device404. In some embodiments, the flow chamber device is removable or replaceable. For example, the flow chamber device404can be replaced after each use.

In some embodiments, the fluid sample412may comprise PD effluent. In the present disclosure, the term “PD effluent” refers to a liquid that is discharged from a PD procedure. For example, the PD effluent may be a liquid that is discharged from a patient's body as an end product from performing a PD procedure on the patient. As described above, a dialysis solution is injected into the patient's body when a PD procedure is performed on the patient. The dialysis solution dwells within the patient's body and eventually is discharged as a PD effluent.

In some embodiments, the PD effluent may be received from a PD machine. For example, a discharging conduit of the PD machine that discharges fluids from the PD procedure can be connected to the flow channel of the flow chamber device404. In such an example, the fluid discharged from the PD machine is the PD effluent. In some embodiments, the flow channel of the flow chamber device404may be connected to a fluid output conduit to discharge the PD effluent.

While the description above provides an example of a fluid sample, it is noted that the scope of the present disclosure is not limited to the description above. In some examples, an example fluid sample may comprise one or more additional and/or alternative fluids. For example, the fluid sample may comprise urine. Additionally, or alternatively, the fluid sample may comprise oil. Additionally, or alternatively, the fluid sample may comprise blood. Additionally, or alternatively, the fluid sample may comprise joint fluid.

In some embodiments, the example fluid sample imaging system400comprises at least one illumination device. In the example shown inFIG.4A, an example illumination device402is illustrated.

For example, the illumination device402may be configured to produce, generate, emit, and/or trigger the production, generation, and/or emission of light. The example illumination device402may include, but is not limited to, laser diodes (for example, UV, visible, or IR laser diodes, edge-emitting laser diodes, surface-emitting laser diodes, and/or the like). Additionally, or alternatively, the illumination device402may comprise one or more light-emitting diodes (LEDs). Additionally, or alternatively, the illumination device402may comprise one or more other forms of natural and/or artificial sources of light.

In some embodiments, at least one illumination device is configured to emit at least one light beam. In some embodiments, the at least one light beam emitted by the at least one illumination device may comprise coherent light. In the present disclosure, the term “coherent light” refers to a light beam where the wavefront has a synchronized phase. Examples of coherent light include, but are not limited to, laser light. For example, the light beam in laser light has the same frequency and phase. In some embodiments, to emit coherent light, the at least one illumination device includes, but is not limited to, laser diodes (for example, UV, visible, or IR laser diodes, edge-emitting laser diodes, surface-emitting laser diodes, and/or the like).

In some embodiments, the at least one light beam emitted by the at least one illumination device may comprise incoherent light or at least partially incoherent light. In the present disclosure, the term “incoherent light” (or “low coherence light” as used interchangeably herein) refers to a light beam where the wavefront does not have a synchronized phase. For example, incoherent light does not contain photons with the same frequency and does not have wavelengths that are in phase with one another. In some embodiments, to emit incoherent light, the at least one illumination device includes, but is not limited to, light-emitting diodes (LEDs).

In some embodiments, the illumination device402is positioned above the flow chamber device404. For example, the illumination device402is positioned above the upper flow chamber substrate408of the flow chamber device404.

In some embodiments, at least one light beam emitted by the illumination device402is directed to a top surface of the flow chamber device404(for example, to the upper flow chamber substrate408of the flow chamber device404). As described above, the upper flow chamber substrate408of the flow chamber device404may comprise transparent material, and the flow channel of the flow chamber device404may receive a fluid sample412. In some embodiments, at least one light beam from the illumination device402passes through the fluid sample412in the flow channel of the flow chamber device404after passing through the upper flow chamber substrate408of the flow chamber device404.

As described above, the flow chamber device404also comprises a lower flow chamber substrate410. In some embodiments, the at least one light beam from the illumination device402passes through the lower flow chamber substrate410of the flow chamber device404after passing through the fluid sample412in the flow channel of the flow chamber device404. Because the lower flow chamber substrate410comprises transparent material, the at least one light beam emitted by the illumination device402passes through the lower flow chamber substrate410without being blocked by the lower flow chamber substrate410.

In some embodiments, the imaging device406is positioned under the flow chamber device404. For example, the imaging device406may be positioned under the lower flow chamber substrate410of the flow chamber device404. In such an example, the image sensing surface426of the imaging device406(for example, a sensing surface of imagers and/or image sensors described herein) is positioned under the lower flow chamber substrate410of the flow chamber device404to receive the at least one light beam from the illumination device402after it passes through the lower flow chamber substrate410.

For example, the illumination device402is aligned to flow chamber device404and to the imaging device406. The at least one light beam emitted by the illumination device402may enter the flow chamber device404via the upper flow chamber substrate408of the flow chamber device404. Because the upper flow chamber substrate408comprises transparent materials, the at least one light beam travels through the fluid sample412in the flow channel of the flow chamber device404, and then exits the flow chamber device404via the lower flow chamber substrate410of the flow chamber device404. Because the flow chamber device404is aligned to the imaging device406, the at least one light beam emitted by the illumination device402then enters the imaging device406(for example, the image sensing surface426of the imaging device406).

In some embodiments, the imaging device406comprises an image sensor that generates digital holography image data associated with the fluid sample412in the flow channel of the flow chamber device404.

In the present disclosure, the term “digital holography image data” refers to image data that is generated based on digital holography techniques, including, but not limited to, lensless holography techniques. For example, the digital holography image data may be generated by the image sensor without any imaging lenses and without any adjustments. In such an example, there are no imaging lenses between the bottom surface of the flow chamber device404and the image sensor. The digital holography image data may comprise a digital holography image of the fluid sample412(for example, a digital holography image of various particles, cells, etc. in the fluid sample412). In some embodiments, the digital holography image is blurry and/or out of focus, and example embodiments of the present disclosure may generate focused images associated with the fluid sample412based at least in part on the digital holography image, details of which are described herein.

In some examples, the image sensor may comprise one or more imagers and/or image sensors. Various examples of the image sensor may include, but are not limited to, a charge-coupled device (CCD), a complementary metal-oxide semiconductor (CMOS) sensor, and/or the like. As described above, in some embodiments, the image sensor does not comprise any lenses so as to generate digital holography image data based on lensless holography techniques.

While the description above provides an example of implementing digital holography techniques, it is noted that the scope of the present disclosure is not limited to the description above. In some examples, an example fluid sample imaging system may implement other imaging techniques. For example, example embodiments of the present disclosure may implement optical microscopy as the imaging technique. Additionally, or alternatively, example embodiments of the present disclosure may implement ultraviolet (UV) fluorescence as the imaging technique.

While the description above provides an example positional arrangement between the illumination device402and the flow chamber device404and an example positional arrangement between the flow chamber device404and the imaging device406, it is noted that the scope of the present disclosure is not limited to the description above. In some examples, the illumination device, the flow chamber device, and/or the imaging device of an example fluid sample imaging system may be positioned differently than those shown inFIG.4A. For example, the illumination device may be positioned under the flow chamber device, and the imaging device may be positioned above the flow chamber device.

Referring back toFIG.4A, the flow chamber device404further comprises/defines an upper surface414and a lower surface416.

In some embodiments, the upper surface414corresponds to a surface of the upper flow chamber substrate408that is in contact with the fluid sample412(for example, a bottom surface of the upper flow chamber substrate408and/or an upper inner surface of the flow channel of the flow chamber device404).

In some embodiments, the lower surface416corresponds to the surface of the lower flow chamber substrate410that is in contact with the fluid sample412(for example, a top surface of the lower flow chamber substrate410and/or a lower inner surface of the flow channel of the flow chamber device404).

As shown inFIG.4A, a maximum focal depth Zmax may be associated with the positional relationship between the upper surface414of the flow chamber device404and the imaging device406, and a minimum focal depth Zmin may be associated with the positional relationship between the lower surface416of the flow chamber device404and the imaging device406.

In the present disclosure, the term “focal depth” refers to a distance between an object (for example, a particle/cell of interest in the fluid sample412) and an image sensing surface of an imaging device (for example, the image sensing surface426of the imaging device406) in the depth Z dimension (e.g. a longitudinal dimension that is perpendicular to the image sensing surface of the imaging device).

In some embodiments, the maximum focal depth Zmax corresponds to a first focal depth between the upper surface414of the flow chamber device404and the imaging device406(for example, the image sensing surface426of the imaging device406). As described above, the upper surface414corresponds to a surface of the upper flow chamber substrate408that is in contact with the fluid sample412. In some embodiments, the minimum focal depth Zmin corresponds to a second focal depth between the lower surface416of the flow chamber device404and the imaging device406(for example, the image sensing surface426of the imaging device406).

As described above and illustrated inFIG.4A, the fluid sample412flows between the upper surface414and the lower surface416of the flow chamber device404, and the imaging device406is positioned under the flow chamber device404. As such, the maximum focal depth Zmax indicates a maximum focal distance between the fluid sample412in the flow chamber device404and the imaging device406(for example, the image sensing surface426of the imaging device406), and the minimum focal depth Zmin indicates a minimum focal distance between the fluid sample412in the flow chamber device404and the imaging device406(for example, the image sensing surface426of the imaging device406).

In some embodiments, the flow chamber device404comprises an upper reference mark418and a lower reference mark420.

In some embodiments, the upper reference mark418is disposed on the upper surface414of the flow chamber device404. As described above, the focal depth between the upper surface414of the flow chamber device404and the imaging device406(for example, the image sensing surface426of the imaging device406) corresponds to the maximum focal depth Zmax. As such, the upper reference mark418is positioned at the maximum focal depth Zmax in the depth Z dimension.

In some embodiments, the lower reference mark420is disposed on the lower surface416of the flow chamber device404. As described above, the focal depth between the lower surface416of the flow chamber device404and the imaging device406(for example, the image sensing surface426of the imaging device406) corresponds to the minimum focal depth Zmin. As such, the lower reference mark420is positioned at the minimum focal depth Zmin in the depth Z dimension.

While the description above provides example positions of the upper reference mark and the lower reference mark in the depth Z dimension, it is noted that the scope of the present disclosure is not limited to the description above. In some embodiments, example upper reference marks and/or example lower reference marks may be positioned at various other Z positions.

In some embodiments, materials, sizes, and shapes of the upper reference mark418and the lower reference mark420can provide various technical advantages and benefits.

In some embodiments, the upper reference mark418and the lower reference mark420comprise opaque material. For example, the upper reference mark418and/or the lower reference mark420may comprise ink imprinted on the upper surface414and the lower surface416, respectively. Additionally, or alternatively, the upper reference mark418and the lower reference mark420may comprise additional and/or alternative material(s). In some embodiments, the opaque material of the upper reference mark418and the lower reference mark420provides technical advantages and benefits including, but not limited to, enabling the ASP-based image focusing algorithm to identify the upper reference mark418and the lower reference mark420from the digital holography image data, details of which are described herein.

While the description above provides some example materials associated with the upper reference mark and the lower reference mark, it is noted that the scope of the present disclosure is not limited to the description above. For example, the upper reference mark and/or the lower reference mark may be in the form of laser-engraved markings on the upper surface414and the lower surface416, respectively. In such an example, the laser-engraved markings comprise opaque surfaces that provide contrast to transparent, semi-transparent, and/or translucent upper surface414and/or the lower surface416.

In some embodiments, the upper reference mark418and/or the lower reference mark420have sharp (for example, high contrast) edges. In some embodiments, the sharp edges of the upper reference mark418and/or the lower reference mark420may provide various technical advantages and benefits such as, but not limited to, allowing the upper reference mark418and/or the lower reference mark420to be more accurately and quickly identified from the digital holography image data, details of which are described herein.

In some embodiments, the upper reference mark418and/or the lower reference mark420may be in geometric shapes or other unique shapes (including, but are not limited to, alpha-numeric shapes). For example, the upper reference mark418and/or the lower reference mark420may be in the form of or comprise a serial number and/or media authentication markings. Such example shapes of the upper reference mark418and/or the lower reference mark420may provide technical advantages and benefits such as, but not limited to, allowing the flow chamber device404to be authenticated, details of which are described herein.

In some embodiments, the upper reference mark418and/or the lower reference mark420may be in regular shapes (such as, but not limited to, triangular shapes, rectangular shapes, and/or the like). Such example shapes of the upper reference mark418and/or the lower reference mark420may simplify edge detection by the ASP-based image focusing algorithm and/or size estimation of the particles of interest, details of which are described herein.

While the upper reference mark418and the lower reference mark420illustrated inFIG.4AandFIG.4Bare in circular shapes, it is noted that the scope of the present disclosure is not limited to these examples.

In some embodiments, the thickness of the upper reference mark418and the thickness of the lower reference mark420are less than or equal 1 micron. Such example thickness provides technical advantage and benefits such as, but not limited to, providing well-defined Z values for the upper reference mark418and the lower reference mark420in the depth Z dimension.

Referring now toFIG.4B, an example schematic representation of an example top view of the example fluid sample imaging system400is illustrated. In particular,FIG.4Billustrates an example full field of view422of the imaging device406and a relevant field of view424associated with the imaging device406.

In some embodiments, the full field of view422of the imaging device406corresponds to the entire field of view of the imaging device406(for example, the field of view of the image sensing surface426of the imaging device406). In some embodiments, the full field of view422of the imaging device406may capture the entirety of the flow channel of the flow chamber device404or most of the flow channel of the flow chamber device404.

In some embodiments, the relevant field of view424of the imaging device406corresponds to a field of view that captures particles/cells of interest in the fluid sample412from the flow channel of the flow chamber device404. For example, as shown inFIG.4B, the relevant field of view424of the imaging device406is a portion of the full field of view422of the imaging device406that does not comprise the upper reference mark418and the lower reference mark420.

In some embodiments, a size of the upper reference mark418(including the width and/or the height of the upper reference mark418) and/or a size of the lower reference mark420(including the width and/or the height of the lower reference mark420) are larger than a size of the particle/cell of interest (including the width and/or the height of the particles/cells of interest). In some embodiments, a size of the upper reference mark418(including the width and/or the height of the upper reference mark418) and/or a size of the lower reference mark420(including the width and/or the height of the lower reference mark420) are smaller than a height of the full field of view422of the imaging device406(or a width of the full field of view422of the imaging device406).

The example size of the upper reference mark418and the example size of the lower reference mark420described above can provide various technical advantages and benefits. For example, having the size of the upper reference mark418and the size of the lower reference mark420larger than the size of the particles/cells of interest allows the imaging device406to detect image signals associated with the upper reference mark418and the lower reference mark420when generating the digital holography image data, even if the upper reference mark418or the lower reference mark420are obstructed by particles/cells of interest from the fluid sample412in the flow chamber device404. As another example, the upper reference mark418and the lower reference mark420obstruct and effectively reduce the relevant field of view424of the imaging device406(and, therefore, reducing the effective fluid volume for a single hologram image). As such, having the size of the upper reference mark418and/or the size of the lower reference mark420smaller than a height of the full field of view422of the imaging device406(or a width of the full field of view422of the imaging device406) prevents the upper reference mark418and the lower reference mark420from obstructing too much of the field of view of the imaging device406.

As an example, the size of the upper reference mark418and the size of the lower reference mark420are larger than the cell size of white blood cells (for example, larger than approximately 12 microns to 15 microns), and are smaller than the height of the full field of view422of the imaging device406(for example, smaller than approximately 3 millimeters). In some embodiments, the size of the upper reference mark418and the size of the lower reference mark420may be ten times the cell size of the white blood cell (for example, approximately between 120 microns and 150 microns).

While the description above provides some example sizes of the upper reference mark418and the lower reference mark420, it is noted that the scope of the present disclosure is not limited to the description above.

As illustrated and described above in connection withFIG.4A, the upper reference mark418is on the upper inner surface of the flow channel of the flow chamber device404, and the lower reference mark420is on the lower inner surface of the flow channel of the flow chamber device404. As illustrated inFIG.4B, both the upper reference mark418and the lower reference mark420are within the full field of view422of the imaging device406.

In some embodiments, each of the upper reference mark418and/or the lower reference mark420may be positioned on a side or a corner of the full field of view422of the imaging device406. Positioning them on the side and/or the corner of the full field of view422of the imaging device406provides technical advantages and benefits such as, but not limited to, allowing the upper reference mark418and the lower reference mark420to be more easily identified from the digital holography image data.

In some embodiments, the upper reference mark418and the lower reference mark420do not overlap in the transverse dimensions (e.g. the X dimension and the Y dimension). In other words, the upper reference mark418and the lower reference mark420do not overlap in dimensions that are parallel to the image sensing surface of the imaging device406. As such, the image sensing surface of the imaging device406can capture separate image data of the upper reference mark418and of the lower reference mark420.

The upper reference mark and the lower reference mark described herein provide various technical benefits and advantages. For example, the upper reference mark and the lower reference mark enable determining the maximum focal depth and the minimum focal depth that can be used to calibrate the ASP-based image focusing algorithm. In situations where the fluid sample is very clean (e.g. a low concentration of cells), the reference marks would establish focal depth information that may not be able to be extracted from the cell/particle characteristics alone. For example, PD effluent is generally free of particles/cells in patients without infection, and the upper reference mark and the lower reference mark can provide technical advantages in calibrating the ASP-based image focusing algorithm. Additionally, or alternatively, the upper reference mark and the lower reference mark can establish a reference frame for determining position and/or movement of particles/cells of interest in subsequent images. In other words, the position and/or movement of particles/cells of interest in the fluid sample from different images can be determined based on comparing the locations of the upper reference mark in different images and/or comparing the locations of the lower reference mark in different images.

Referring now toFIG.5,FIG.6,FIG.7andFIG.8, example flow diagrams illustrating example methods of analyzing fluid samples in accordance with some example embodiments of the present disclosure are provided.

It is noted that each block of the flowchart, and combinations of blocks in the flowchart, may be implemented by various means such as hardware, firmware, circuitry and/or other devices associated with execution of software including one or more computer program instructions. For example, one or more of the steps/operations described inFIG.5,FIG.6,FIG.7andFIG.8may be embodied by computer program instructions, which may be stored by a non-transitory memory of an apparatus employing an embodiment of the present disclosure and executed by a processor component in an apparatus (such as, but not limited to, a fluid sample imaging system, a mobile computing device, a remote computing server, and/or the like). For example, these computer program instructions may direct the processor component to function in a particular manner, such that the instructions stored in the computer-readable storage memory produce an article of manufacture, the execution of which implements the function specified in the flowchart block(s).

Referring now toFIG.5, an example method500of analyzing fluid samples in accordance with some example embodiments described herein is illustrated. In particular,FIG.5illustrates an example method for analyzing digital holography image data associated with fluid samples.

InFIG.5, the example method500starts at step/operation501. In some embodiments, subsequent to and/or in response to step/operation501, the example method500proceeds to step/operation503. At step/operation503, a processor component (such as, but not limited to, the processor component301of the example remote computing server105described above in connection with at leastFIG.1andFIG.3, and/or the processor component208of the example mobile computing device101A described in connection with at leastFIG.1andFIG.2) receives digital holography image data associated with a fluid sample in a flow chamber device.

In some embodiments, the digital holography image data is received from an imaging device (such as, but not limited to, the imaging device406of the fluid sample imaging system400described above in connection withFIG.4AandFIG.4B.

Similar to described above in connection withFIG.4AandFIG.4B, the imaging device may be positioned under a flow chamber device. In some embodiments, the flow chamber device receives a fluid sample, and the digital holography image data generated by the imaging device is associated with the fluid sample in the flow chamber device.

In some embodiments, the flow chamber device comprises an upper reference mark that is on an upper surface of the flow chamber device (similar to the upper reference mark418described above in connection withFIG.4AandFIG.4B) and a lower reference mark that is on a lower surface of the flow chamber device (similar to the lower reference mark420described above in connection withFIG.4AandFIG.4B). As such, the digital holography image data generated by the imaging device is associated with the upper reference mark and the lower reference mark of the flow chamber device.

In some embodiments, the digital holography image data comprises image data of the entire field of view of the imaging device (for example, the full field of view422of the imaging device406as shown above in connection withFIG.4B). As described above, the digital holography image data may comprise a digital holography image of the fluid sample, the upper reference mark, and the lower reference mark. In some embodiments, the digital holography image is out of focus or blurry.

Referring back toFIG.5, subsequent to and/or in response to step/operation503, the example method500proceeds to step/operation505. At step/operation505, a processor component (such as, but not limited to, the processor component301of the example remote computing server105described above in connection with at leastFIG.1andFIG.3, and/or the processor component208of the example mobile computing device101A described in connection with at leastFIG.1andFIG.2) extracts an upper reference mark image region and a lower reference mark image region.

In some embodiments, the upper reference mark image region is associated with the upper reference mark, and the lower reference mark image region is associated with the lower reference mark. In some embodiments, the upper reference mark image region is extracted from the digital holography image data based at least in part on an upper reference mark location associated with the upper reference mark, and the lower reference mark image region is extracted from the digital holography image data based at least in part on a lower reference mark location associated with the lower reference mark.

For example, the digital holography image data may comprise a digital holography image as described above. In some embodiments, the processor component may determine the locations of the upper reference mark and the lower reference mark in the digital holography image. For example, the upper reference mark and/or the lower reference mark may be positioned on a side and/or a corner of the full field of view of the imaging device as described above. In such an example, the processor component may determine the corresponding side or the corresponding corner of the full field of view where the upper reference mark is located as the upper reference mark location, and may determine the corresponding side or the corresponding corner of the full field of view where the lower reference mark is located as the lower reference mark location.

In some embodiments, the processor component may extract/crop the upper reference mark image region around the upper reference mark location from the digital holography image, and may extract/crop the lower reference mark image region around the lower reference mark location from the digital holography image.

In some embodiments, a size of upper reference mark image region (for example, a height of the upper reference mark image region or a width of the upper reference mark image region) and/or a size of lower reference mark image region (for example, a height of the lower reference mark image region or a width of the lower reference mark image region) may be selected according to an image focusing algorithm (such as an ASP-based image focusing algorithm), such that the upper reference mark image region and the lower reference mark image region provide sufficient sizes to enable the image focusing algorithm to generate focused images of the upper reference mark and the lower reference mark. Additionally, or alternatively, the size of the upper reference mark image region and/or the size of the lower reference mark image region may account for the mechanical variations (within tolerance) associated with the flow chamber device.

Referring back toFIG.5, subsequent to and/or in response to step/operation505, the example method500proceeds to step/operation507. At step/operation507, a processor component (such as, but not limited to, the processor component301of the example remote computing server105described above in connection with at leastFIG.1andFIG.3, and/or the processor component208of the example mobile computing device101A described in connection with at leastFIG.1andFIG.2) determines a maximum focal depth and a minimum focal depth.

In some embodiments, the maximum focal depth and the minimum focal depth are associated with the digital holography image data received at step/operation503.

Similar to those described above in connection with at leastFIG.4AandFIG.4B, the maximum focal depth indicates a maximum distance between the fluid sample and the imaging device, and the minimum focal depth indicates a minimum distance between the fluid sample and the imaging device.

For example, the maximum focal depth corresponds to a first focal depth between the upper surface of the flow chamber device and the imaging device. In some embodiments, the upper surface of the flow chamber device corresponds to the upper inner surface of the flow channel in the flow chamber device. Because the imaging device is positioned under the flow channel of the flow chamber device, when a volume of fluid sample flows through the flow channel, the upper surface of the flow chamber device corresponds to a depth in the Z dimension where the fluid sample is the furthest away from the imaging device. As such, the maximum focal depth indicates a maximum focal depth between the imaging device and the volume of fluid sample.

Similarly, the minimum focal depth corresponds to a second focal depth between the lower surface of the flow chamber device and the imaging device. In some embodiments, the lower surface of the flow chamber device corresponds to the lower inner surface of the flow channel in the flow chamber device. Because the imaging device is positioned under the flow channel of the flow chamber device, when a volume of fluid sample flows through the flow channel, the lower surface of the flow chamber device corresponds to a depth in the Z dimension where the fluid sample is the closest to the imaging device. As such, the minimum focal depth indicates a minimum focal depth between the imaging device and the volume of fluid sample.

In some embodiments, the processor component determines the maximum focal depth and the minimum focal depth based at least in part on the upper reference mark image region and the lower reference mark image region, respectively, that are extracted at step/operation505. In some embodiments, the processor component may separately provide the upper reference mark image region and the lower reference mark image region to an image focusing algorithm, and the image focusing algorithm may determine the maximum focal depth and the minimum focal depth, respectively.

In the present disclosure, the term “image focusing algorithm” refers to a computer software program (and, in some embodiments, associated computer hardware such as memory and processor components) that receives an out of focus image (such as, but not limited to, the digital holography image from the digital holography image data) and computationally generates an optimally focused image based on the out of focus image. For example, the image focusing algorithm may process the out of focus image and generate a series of images, where each of the series of images is associated with a different computational focal depth. Additionally, in some embodiments, the image focusing algorithm selects an image from the series of images that is best in focus, and outputs the selected image.

For example, the processor component may implement an image focusing algorithm on the upper reference mark image region to focus the upper reference mark image region and determine the maximum focal depth. In such an example, the image focusing algorithm may generate a focused image based on the upper reference mark image region, where the upper reference mark is optimally focused in the focused image. Because the upper reference mark is on the upper surface of the flow chamber device, the focal depth associated with the focused image corresponds to the maximum focal depth. In other words, the processor component can determine the maximum focal depth between the imaging device and the volume of fluid sample by implementing the image focusing algorithm on the upper reference mark image region.

Separately, the processor component may implement an image focusing algorithm on the lower reference mark image region to focus the lower reference mark image region and determine the minimum focal depth. In such an example, the image focusing algorithm may generate a focused image based on the lower reference mark image region, where the lower reference mark is optimally focused in the focused image. Because the lower reference mark is on the lower surface of the flow chamber device, the focal depth associated with the focused image corresponds to the minimum focal depth. In other words, the processor component can determine the minimum focal depth between the imaging device and the volume of fluid sample by implementing the image focusing algorithm on the lower reference mark image region.

In some embodiments, the processor component may separately focus the upper reference mark image region and the lower reference mark image region with an ASP-based image focusing algorithm to determine depth Z dimension values for focal depths of the upper reference mark and the lower reference mark, details of which are described herein in connection with at leastFIG.6.

While the description above provides an example of determining the maximum focal depth and the minimum focal depth based at least in part on extracting the upper and lower reference mark image regions and focusing the upper and lower reference mark image regions, it is noted that the scope of the present disclosure is not limited to the description above.

For example, additionally, or alternatively, an example processor component may implement an image focusing algorithm on the digital holography image data. In such an example, the image focusing algorithm may focus the entire digital holography image from the digital holography image data. Subsequently, the processor component may determine an upper reference mark location associated with the upper reference mark from the focused image, generate a focused image where the upper reference mark is optimally focused, and determine a maximum focal depth based on the focused image, similar to those described above. Additionally, or alternatively, the processor component may determine a lower reference mark location associated with the lower reference mark from the focused image, generate a focused image where the lower reference mark is optimally focused, and determine a minimum focal depth based on the focused image.

While the description above provides some example technical benefits and advantages of implementing the upper reference mark and the lower reference mark, it is noted that the scope of the present discourse is not limited to the examples described above.

For example, in some embodiments, the processor component may determine whether the upper reference mark can be resolved/identified in the upper reference mark image region, and whether the lower reference mark can be resolved/identified in the lower reference mark image region. As described above, the upper reference mark image region is extracted/cropped from the digital holography image around the expected location of the upper reference mark, and the lower reference mark image region is extracted/cropped from the digital holography image around the expected location of the lower reference mark. If the upper reference mark cannot be resolved/identified in the upper reference mark image region, and/or the lower reference mark cannot be resolved/identified in the lower reference mark image region, the processor component may determine that the flow chamber device is not aligned correctly with the imaging device, the fluid sample is too opaque, and/or the flow chamber device is an unauthenticated or authorized device (for example, a counterfeit). As such, the upper reference mark and the lower reference mark can provide a useful diagnostic indicator.

As another example, the upper reference mark and the lower reference mark are each associated with reference mark sizes as described above. In some embodiments, the sizes of the upper reference mark and the lower reference mark can be used to calibrate the scale of dimensions in a digital hologram reconstructed based on the digital holography image data. For example, the processor component may compare the actual sizes of the upper reference mark and the lower reference mark with image sizes of the upper reference mark and the lower reference mark in the digital holography image or the focused image based on the digital holography image. The processor component may then scale the particles/cells of interest in the digital holography image or the focused image based on the comparison, so that the actual sizes of the particles/cells of interest can be determined.

Referring back toFIG.5, subsequent to and/or in response to step/operation507, the example method500proceeds to step/operation509. At step/operation509, a processor component (such as, but not limited to, the processor component301of the example remote computing server105described above in connection with at leastFIG.1andFIG.3, and/or the processor component208of the example mobile computing device101A described in connection with at leastFIG.1andFIG.2) focuses each of a plurality of focal depth layers associated with the digital holography image data.

In some embodiments, the processor component may determine the plurality of focal depth layers associated with the digital holography image data based at least in part on the maximum focal depth and the minimum focal depth determined at step/operation507. In some embodiments, each of the plurality of focal depth layers is associated with a focal depth range/segment. Additional details associated with determining the plurality of focal depth layers are described in connection with at leastFIG.7.

Subsequently, the processor component may focus each of a plurality of focal depth layers by implementing an image focusing algorithm. For example, the processor component may implement an ASP-based image focusing algorithm to focus each of the plurality of focal depth layers associated with the digital holography image data.

For example, the processor component may provide the digital holography image from the digital holography image data to the image focusing algorithm, along with the focal depth range associated with a focal depth layer. In some embodiments, the image focusing algorithm may computationally focus the digital holography image at different focal depths in the focal depth range to generate a series of images for the focal depth layer, where each of the series of images is associated with a different focal depth within the focal depth range.

In some embodiments, the image focusing algorithm may select an optimally focused image from the series of images for the focal depth layer, similar to those described above. In such embodiments, the focused image generated by the image focusing algorithm is associated with a focal depth within the focal depth layer that provides the optimum focus of particles/cells of interest as compared to other focal depths within the focal depth layer.

Referring back toFIG.5, subsequent to and/or in response to step/operation509, the example method500proceeds to step/operation511. At step/operation511, a processor component (such as, but not limited to, the processor component301of the example remote computing server105described above in connection with at leastFIG.1andFIG.3, and/or the processor component208of the example mobile computing device101A described in connection with at leastFIG.1andFIG.2) extracts one or more region of interest (ROI) portions.

In some embodiments, the processor component may extract one or more ROI portions from the plurality of focal depth layers. For example, as described above in connection with at least step/operation509, the processor component may generate an optimally focused image or a series of images for each of the plurality of focal depth layers, and the processor component may extract the one or more ROI portion from the optimally focused images or the series of images associated with the plurality of focal depth layers.

In some embodiments, a ROI portion is associated with the fluid sample (e.g. associated with particle(s)/cell(s) of interest in the fluid sample). For example, the ROI portion may comprise image(s) of particle(s)/cell(s) of interest from the fluid sample. As an example, the fluid sample may be in the form of a PD effluent. In such an example, the ROI portions may comprise images of white blood cells in the PD effluent. As such, the processor component may extract optimally focused particle/cell ROI portions for the white blood cells from each focus layer.

Referring back toFIG.5, subsequent to and/or in response to step/operation511, the example method500proceeds to step/operation513and ends.

In some embodiments, subsequent to and/or in response to step/operation503, the example method500optionally proceeds to step/operation515. At step/operation515, a processor component (such as, but not limited to, the processor component301of the example remote computing server105described above in connection with at leastFIG.1andFIG.3, and/or the processor component208of the example mobile computing device101A described in connection with at leastFIG.1andFIG.2) extracts a fluid sample relevant image region from the digital holography image data.

In some embodiments, the upper reference mark and the lower reference mark are excluded from the fluid sample relevant image region that is extracted at step/operation515. For example, the fluid sample relevant image region from the digital holography image data may correspond to the relevant field of view424associated with the imaging device406illustrated and described above in connection withFIG.4AandFIG.4B.

In some embodiments, extracting the fluid sample relevant image region from the digital holography image data may provide various technical benefits and advantages. For example, the fluid sample relevant image region may provide a useful image area of field of view for cell/particle analysis of the fluid sample. Because the fluid sample relevant image region does not comprise images of the reference marks, the processor component may implement the image focusing algorithm to focus the fluid sample relevant image region without having to processing other image regions that may not be relevant to the cell/particle analysis of the fluid sample, which can increase the speed of processing digital holography image data.

For example, in some embodiments, the plurality of focal depth layers described above in connection with step/operation509and step/operation511are associated with the fluid sample relevant image region extracted at step/operation515. At step/operation509, when focusing each of a plurality of focal depth layers associated with the digital holography image data, the processor component may provide only the fluid sample relevant image region from the digital holography image data to the image focusing algorithm, along with the focal depth range associated with a focal depth layer. In some embodiments, the image focusing algorithm may computationally focus only the fluid sample relevant image region at different focal depths in the focal depth range to generate a series of images for the focal depth layer, where each of the series of images is associated with a different focal depth within the focal depth range. In some embodiments, the image focusing algorithm may select an optimally focused image (e.g. of the fluid sample relevant image region) from the series of images for the focal depth layer, similar to those described above. Subsequently, the processor component may extract the one or more ROI portions at step/operation511from the focused images or the series of images, similar to those described above.

Referring now toFIG.6, an example method600of analyzing fluid samples in accordance with some example embodiments described herein is illustrated. In particular, the example method600illustrates some example additional and/or alternative steps/operations associated with determining a maximum focal depth and a minimum focal depth in an example method for analyzing fluid samples (for example, associated with step/operation507described above in connection withFIG.5) in accordance with some embodiments of the present disclosure.

In the example shown inFIG.6, the example method600starts at block A. As illustrated inFIG.5, block A is connected to step/operation507, where the processor component determines a maximum focal depth and a minimum focal depth.

In some embodiments, subsequent to block A, the example method600proceeds to step/operation602. At step/operation602, a processor component (such as, but not limited to, the processor component301of the example remote computing server105described above in connection with at leastFIG.1andFIG.3, and/or the processor component208of the example mobile computing device101A described in connection with at leastFIG.1andFIG.2) focuses the upper reference mark image region based at least in part on an Angular Spectrum Propagation (ASP) based image focusing algorithm.

In the present disclosure, the terms “Angular Spectrum Propagation based image focusing algorithm” or “ASP-based image focusing algorithm” refer to a type of image focusing algorithm that implements angular spectrum propagation techniques. For example, the ASP-based image focusing algorithm may computationally model the propagations of a light wave field (for example, the electromagnetic wave from the light) from the digital holography image data. As an example, the ASP-based image focusing algorithm may computationally expand the light wave field from the digital holography image data into a summation of light wave planes (for example, based on Fourier optics), where each light wave plane corresponds to a focal depth. As such, the ASP-based image focusing algorithm may computationally focus the upper reference mark image region and generate different images based on the upper reference mark image region at different focal depths.

In some embodiments, the upper reference mark is in focus from the upper reference mark image region at the maximum focal depth. As described above, the maximum focal depth corresponds to a first focal depth between the upper surface of the flow chamber device and the imaging device. Because the upper reference mark is disposed on the upper surface of the flow chamber device, the upper reference mark is in focus when the ASP-based image focusing algorithm computationally focuses the upper reference mark image region at the maximum focal depth.

As such, the processor component may determine the maximum focal depth by providing the upper reference mark image region to the ASP-based image focusing algorithm. For example, the ASP-based image focusing algorithm may computationally focus the upper reference mark image region at different focal depths to generate a series of images, and determine in which one of the series of images is the upper reference mark optimally focused. Once the processor component determines that the upper reference mark is optimally focused in an image generated by the ASP-based image focusing algorithm, the processor component determines that the focal depth of such image corresponds to the maximum focal depth.

In some embodiments, subsequent to block A, the example method600proceeds to step/operation604. At step/operation604, a processor component (such as, but not limited to, the processor component301of the example remote computing server105described above in connection with at leastFIG.1andFIG.3, and/or the processor component208of the example mobile computing device101A described in connection with at leastFIG.1andFIG.2) focuses the lower reference mark image region based at least in part on an ASP-based image focusing algorithm.

In some embodiments, the lower reference mark is in focus from the lower reference mark image region at the minimum focal depth. As described above, the minimum focal depth corresponds to a second focal depth between the lower surface of the flow chamber device and the imaging device. Because the lower reference mark is disposed on the lower surface of the flow chamber device, the lower reference mark is in focus when the ASP-based image focusing algorithm computationally focuses the lower reference mark image region at the minimum focal depth.

As such, the processor component may determine the minimum focal depth by providing the lower reference mark image region to the ASP-based image focusing algorithm. For example, the ASP-based image focusing algorithm may computationally focus the lower reference mark image region at different focal depths to generate a series of images, and determine in which one of the series of images is the lower reference mark optimally focused. Once the processor component determines that the lower reference mark is optimally focused in an image generated by the ASP-based image focusing algorithm, the processor component determines that the focal depth of such image corresponds to the minimum focal depth.

In some embodiments, subsequent to block A, the example method600may optionally proceed to step/operation606. At step/operation606, a processor component (such as, but not limited to, the processor component301of the example remote computing server105described above in connection with at leastFIG.1andFIG.3, and/or the processor component208of the example mobile computing device101A described in connection with at leastFIG.1andFIG.2) extracts authentication data associated with at least one of the upper reference mark or the lower reference mark.

In some embodiments, at least one of the upper reference mark or the lower reference mark comprises an authentication indicium that provides authentication data. As described above, the upper reference mark and/or the lower reference mark may be in the form of or comprise a serial number and/or media authentication markings. For example, at least one of the upper reference mark and/or the lower reference mark may comprise an authentication indicium in the form of a serial number. In such an example, the processor component may extract the authentication data (e.g. the serial number) from the upper reference mark image region and/or the lower reference mark image region (based on whether the upper reference mark and/or the lower reference mark comprises the authentication indicium). The processor component may further determine whether the flow chamber device is an authenticated or genuine flow chamber device.

For example, the processor component may determine whether the extracted authentication data (e.g. the serial number) matches any authentication data associated with the flow chamber device that is stored in an authentication database. If the extracted authentication data matches authentication data in the authentication database, the processor component may determine that the flow chamber device is an authenticated or genuine flow chamber device, and may provide an authentication success notification to a client device (for example, the mobile computing device described above) and continue with other steps/operations described herein. If the extracted authentication data does not match authentication data in the authentication database, the processor component may determine that the flow chamber device is an unauthenticated or counterfeit flow chamber device, and may provide an authentication failure notification to a client device (for example, the mobile computing device described above) and forgo other steps/operations described herein.

In some embodiments, step/operation602, step/operation604, and/or step/operation606may be performed in any sequence. Subsequent to and/or in response to step/operation602, step/operation604, and step/operation606, the example method600proceeds to block B. Referring back toFIG.5, block B returns back to step/operation507.

Referring now toFIG.7, an example method700of analyzing fluid samples in accordance with some example embodiments described herein is illustrated. In particular, the example method700illustrates some example additional and/or alternative steps/operations associated with determining the plurality of focal depth layers associated with the digital holography image data in an example method for analyzing fluid samples (for example, associated with step/operation509described above in connection withFIG.5) in accordance with some embodiments of the present disclosure.

Referring now toFIG.7, the example method700starts at block C. As illustrated inFIG.5, block C is connected to step/operation509, where the processor component focuses each of a plurality of focal depth layers associated with the digital holography image data. In some embodiments, the processor component may determine the plurality of focal depth layers prior to focusing each of the plurality of focal depth layers.

Subsequent to and/or in response to block C, the example method700proceeds to step/operation701. At step/operation701, a processor component (such as, but not limited to, the processor component301of the example remote computing server105described above in connection with at leastFIG.1andFIG.3, and/or the processor component208of the example mobile computing device101A described in connection with at leastFIG.1andFIG.2) determines a focal depth layer count number.

In some embodiments, the focal depth layer count number is associated with the plurality of focal depth layers. For example, the focal depth layer count number indicates the number of focal depth layers.

In some embodiments, the focal depth layer count number may be determined based on the image focusing algorithm utilized by the processor component so as to calibrate the image focusing algorithm and optimize the accuracy of the image focusing algorithm. For example, the processor component may implement an ASP-based image focusing algorithm, and may determine that the focal depth layer count number equals four. While the description above provides an example of four focal depth layers, it is noted that the scope of the present disclosure is not limited to the description above. In some examples, an example method may determine less than four or more than four focal depth layers.

Referring back toFIG.7, subsequent to and/or in response to step/operation701, the example method700proceeds to step/operation703. At step/operation703, a processor component (such as, but not limited to, the processor component301of the example remote computing server105described above in connection with at leastFIG.1andFIG.3, and/or the processor component208of the example mobile computing device101A described in connection with at leastFIG.1andFIG.2) calculates a corresponding focal depth range associated with each of the plurality of focal depth layers.

In some embodiments, the processor component may calculate the corresponding focal depth range associated with each of the plurality of focal depth layers based at least in part on the maximum focal depth, the minimum focal depth, and the focal depth layer count number. In some embodiments, the maximum focal depth and the minimum focal depth may be determined in accordance with various examples described herein. In some embodiments, the focal depth layer count number may be determined in connection with step/operation701above.

In some embodiments, the maximum focal depth and the minimum focal depth may be used to calibrate an image focusing algorithm (for example, an ASP-based image focusing algorithm). For example, the maximum focal depth indicates a maximum focal distance between the imaging device and the volume of fluid sample in the flow channel of the flow chamber device, and the minimum focal depth indicates a minimum focal distance between the imaging device and the volume of fluid sample in the flow channel of the flow chamber device. In some embodiments, the focal depth difference between the maximum focal depth and the minimum focal depth indicates a span of focal depths of the fluid sample in the flow channel of the flow chamber device. As such, various embodiments of the present disclosure may calibrate the focal depth search range of the ASP-based image focusing algorithm based on the focal depth difference.

As described above, each of the plurality of focal depth layers is associated with a range or segment of focal depths. In some embodiments, the processor component may divide the focal depth difference between the maximum focal depth and the minimum focal depth by the focal depth layer count number determined at step/operation701to calculate the corresponding focal depth range associated with each of the plurality of focal depth layers. In some embodiments, the processor component may separately focus each focal depth layer by implementing an ASP-based image focusing algorithm.

As an example, the processor component may determine that the maximum focal depth is 0.8 millimeters and the minimum focal depth is 0.2 millimeters. The processor component may further determine that the focal depth layer count number is 4. In this example, the processor component may determine the focal depth difference is 0.6. The processor component may determine that the first focal depth layer is associated with a focal depth range between 0.2 millimeters (inclusive) to 0.35 millimeters (exclusive), the second focal depth layer is associated with a focal depth range from 0.35 millimeters (inclusive) to 0.5 millimeters (exclusive), the third focal depth layer is associated with a focal depth range from 0.5 millimeters (inclusive) to 0.65 millimeters (exclusive), and the fourth focal depth layer is associated with a focal depth range from 0.65 millimeters (inclusive) to 0.8 millimeters (inclusive). In some embodiments, the processor component may implement an ASP-based image focusing algorithm to focus each of the first focal depth layer, the second focal depth layer, the third focal depth layer, and the fourth focal depth layer, similar to various examples described herein.

While the description above provides some example values of the maximum focal depth, the minimum focal depth, and the focal depth layer count number, it is noted that the scope of the present disclosure is not limited to the description above.

As illustrated in various examples herein, the upper reference mark and the lower reference mark provide various technical advantages and benefits. For example, the upper reference mark and the lower reference mark can indicate accurate depth Z dimensions of the volume of the fluid sample in the flow channel without being affected by factors such as mechanical variations, thereby enabling various embodiments of the present disclosure to accurately calibrate the image focusing algorithm to improve its accuracy. By enabling the image focusing algorithm to focus only on segments of focal depths associated with the volume of the fluid sample in the flow channel, various embodiments of the present disclosure further improves the speed of the image focusing algorithm in processing the digital holography image data.

Referring back toFIG.7, subsequent to and/or in response to step/operation703, the example method700proceeds block D. Referring back toFIG.5, block D returns back to step/operation509, where the processor component may focus each of the plurality of focal depth layers.

Referring now toFIG.8, an example method800of analyzing fluid samples in accordance with some example embodiments is illustrated. In particular, the example method800illustrates some example additional and/or alternative steps/operations associated with extracting one or more ROI portions in an example method for analyzing fluid samples (for example, associated with step/operation509described above in connection withFIG.5) in accordance with some embodiments of the present disclosure.

InFIG.8, the example method800starts at block E. As illustrated inFIG.5, block E is connected to step/operation511, where the processor component extracts one or more ROI portions.

As described above, the fluid sample may comprise one or more particles. For example, the one or more particles may comprise a plurality of particles/cells that are of interest to the subsequent analysis. As an example, the fluid sample may be in the form of PD effluent. In such an example, one or more particles that are of interest may include, but not limited to, white blood cells.

InFIG.8, subsequent to and/or in response to block E, the example method800proceeds to step/operation802. At step/operation802, a processor component (such as, but not limited to, the processor component301of the example remote computing server105described above in connection with at leastFIG.1andFIG.3, and/or the processor component208of the example mobile computing device101A described in connection with at leastFIG.1andFIG.2) determines a plurality of candidate ROI portions.

As described above, the processor component may focus each of the plurality of focal depth layers based at least in part on an ASP-based image focusing algorithm and generate an optimally focused image for each of the plurality of focal depth layers. In some embodiments, the processor component may process the optimally focused images associated with different focal depth layers to identify the one or more particles/cells of interest shown in the optimally focused images. In some embodiments, the processor component may extract a plurality of candidate ROI portions from the optimally focused images, where each of the plurality of candidate ROI portions shows one or more particles/cells of interest.

For example, particles/cells of interest may be suspended at various depths in the fluid sample as described above, and a particular particle/cell of interest may be captured in different optimally focused images associated with different focal depth layers (e.g. at different focal depth). In this example, the processor component may determine a candidate ROI portion from each of the different optimally focused images where the particular particle/cell of interest is shown in the candidate ROI portion. For example, the processor component may implement image recognition techniques to process the optimally focused images and extract/crop candidate ROI portions from the optimally focused images where the particular particle/cell of interest is shown.

As an example, the fluid sample may be a PD effluent, and the particles of interest may be white blood cells that are suspended at various depths in the PD effluent. In this example, a white blood cell may be captured in different optimally focused images at different focal depths, and the processor component may extract a candidate ROI portion from each of the optimally focused images where the white blood cell is shown.

Referring back toFIG.8, subsequent to and/or in response to step/operation802, the example method800proceeds to step/operation804. At step/operation804, a processor component (such as, but not limited to, the processor component301of the example remote computing server105described above in connection with at leastFIG.1andFIG.3, and/or the processor component208of the example mobile computing device101A described in connection with at leastFIG.1andFIG.2) determines an optimally focused ROI portion for each of the plurality of particles.

As illustrated in the example above, a particle of interest may be visible in more than one focal depth layer. In some embodiments, the processor component selects the optimum representation of the particle of interest. For example, the processor component may compare the ROI portions where a particle of interest is shown, and determine which one of the ROI portions is the most in focus. The processor component may determine the ROI portion that is the most in focus as the optimally focused ROI portion for the particle of interest.

In some embodiments, the processor component may repeat this process for each particle of interest, and therefore determine an optimally focused ROI portion for each of the plurality of particles.

In some embodiments, subsequent to determining an optimally focused ROI portion for each of the plurality of particles, the processor component may combine different optimally focused ROI portions into a final image. In such an example, the final image comprises images of particles/cells of interest from the fluid sample that are optimally focused.

Referring back toFIG.8, subsequent to step/operation804, the example method800proceeds to block F. Referring back toFIG.5, block F returns back to step/operation511.