Systems and methods for detecting cytopathic effect in cells

A method for detecting cytopathic effect (CPE) in a well sample includes generating a well image depicting a well containing cells and a medium (and possibly viruses), and pre-processing the well image at least by partitioning the well image into sub-images each corresponding to a different portion of the well. The method also includes, for each of some or all of the sub-images, determining, by analyzing the sub-image using a convolutional neural network, a respective score indicative of a likelihood that any cells in the portion of the well corresponding to the sub-image exhibit CPE. The method further includes determining a CPE status of the cells contained in the well based on the respective scores for the sub-images, and generating output data indicating the CPE status.

FIELD OF DISCLOSURE

The present application relates generally to viral detection techniques, and more specifically to techniques for detecting cytopathic effect (CPE) in cells.

BACKGROUND

When a virus infects a host cell, the host cell may undergo structural/morphological changes referred to as “cytopathic effect,” or CPE. In some applications (e.g., when performing quality control procedures in connection with certain commercial drugs, or for research and development purposes), it is necessary to inspect cell culture samples for the presence of CPE. For example, virus stock potency is typically measured using titration assays, which are classical, cell culture-based methods that rely on visual observations of virus-induced cytopathology. One commonly used technique for quantifying the amount of an infectious virus is the “tissue culture infection dose 50%,” or TCID50, assay. TCID50assays are endpoint dilution assays that quantify the amount of virus required to produce CPE in 50% of inoculated tissue culture cells. TCID50assays may be used for viral clearance studies (e.g., when determining the ability of a particular purification process to remove or inactivate a virus), for example.

Conventionally, CPE is manually detected by human analysts inspecting images of wells. For a TCID50assay, for example, a human analyst may need to inspect a number of well images that each correspond to a different dilution level. Manual visual inspection is a time consuming process, as the analyst must carefully inspect each image for any evidence of CPE. Moreover, the task is complicated—and the accuracy of CPE or non-CPE classifications can suffer—due to the fact that different cell lines (e.g., the L929, PG4, Vero and 324K cell lines) can have different morphologies when exhibiting CPE, as well as the fact that different viruses can induce different cytopathic effects in host cells of a single cell line. Different cytopathic effects may include elongation, inclusion bodies, foci formation, syncytia formation, and/or cell lysis, for example.

SUMMARY

Embodiments described herein relate to systems and methods that improve upon conventional visual inspection techniques used for CPE detection. In particular, a visual inspection system captures at least one digital image of each well within a well plate, with each well containing a number of cells in a medium (and possibly viruses, e.g., according to a controlled dilution). As used herein “well” refers to any laboratory-scale cell culture environment that permits optical inspection of its contents. While wells on multi-well plates are discussed by way of example herein, it will be appreciated that wherever a “well” and a “well plate” are mentioned, unless stated otherwise, these terms are contemplated to encompass any suitable laboratory-scale cell culture environment permitting optical inspection of its contents. Each well image is pre-processed by partitioning the image into a number of segments, or “sub-images,” that each correspond to (i.e., depict) a different portion of the well. The well image may also be pre-processed in other ways, such as removing portions of the image that depict areas outside of the well.

For a given well image, each sub-image is analyzed using a convolutional neural network (CNN), in order to determine a score for that sub-image. The CNN may be specific to the cell line in the well that is being inspected (e.g., the L929, PG4, Vero or 324K cell line). The score for each sub-image is indicative of the likelihood that any cells in the portion of the well corresponding to the sub-image exhibit CPE. For example, each score may be a probability that is greater than 0.00000 and less than 1.00000. Collectively, the scores for the various sub-images may be used to determine (e.g., predict) a CPE status of the cells depicted in the entire well image. For example, the sub-image scores may be used to determine, in binary fashion, whether the contents of the well image, as a whole, exhibit CPE. In one such embodiment, the sub-image scores are input to a support vector machine (SVM) that classifies the contents depicted in the well image as “CPE” or “not CPE” (or another, similar binary classification). In other embodiments, the CPE status is not binary. For example, the sub-image scores may be used to determine a probability that the contents of the well image, as a whole, exhibit CPE, such as a probability at or below a specified threshold, for example no more than a 50%, 40%, 30%, 20%, 10%, 5%, 3%, 2%, 1%, or 0.1% probability that the contents of the well image, as a whole, exhibit CPE. Optionally, a well may be determined to be “not CPR” or to have a probability that the content of the well image, as a whole, exhibits CPE below the specified threshold can be selected for further cell culture. For example, a cell of the well (as a single cell, or comprised by a portion of the contents of the well) can be transferred to a new culture environment, and cultured in the new culture environment. Information on cell culture can be found, for example, in Green and Sambrook, “Molecular Cloning: A Laboratory Manual” (4th edition) Cold Spring Harbor Laboratory Press 2012, which is incorporated by reference herein in its entirety.

The CPE status may be determined at each of a number of serial dilution stages (e.g., for a TCID50assay), in some embodiments. Depending on the application, the CPE status (e.g., classification), or CPE statuses at different stages of serial dilution, may be used in different ways. For example, a graphical user interface (GUI) may present the CPE status or statuses to a human user. As another example, the CPE status(es) may be provided to another software application or computer system, e.g., for the purpose of gathering statistics across many wells and/or well plates. The CPE status(es), and/or statistics that take the CPE statuses of a number of wells or well plates into account, may be used to determine the capacity of a purification process to remove or inactivate a virus, for example (e.g., for a quality control procedure during drug manufacture, or for research and development purposes, etc.), or for any other suitable purpose.

DETAILED DESCRIPTION

The various concepts introduced above and discussed in greater detail below may be implemented in any of numerous ways, and the described concepts are not limited to any particular manner of implementation. Examples of implementations are provided for illustrative purposes.

FIG.1is a simplified block diagram of an example system100that may implement the techniques described herein. System100includes a visual inspection system102communicatively coupled to a computer system104. Visual inspection system102includes hardware (e.g., a well plate stage, one or more lenses and/or mirrors, an imager, etc.), as well as firmware and/or software, that is configured to capture digital images of wells within a well plate. WhileFIG.1depicts, and is primarily described herein with reference to, an embodiment in which visual inspection system102is controlled by computer system104, it is understood that, in other embodiments, visual inspection system102may purely (or primarily) implement local control (e.g., if visual inspection system102includes an off-the-shelf product such as the CloneSelect imager from Molecular Devices, LLC).

An example embodiment of visual inspection system102is shown inFIG.2. As seen inFIG.2, visual inspection system102may include a stage202that is configured to receive a well plate204containing a number of wells (not shown inFIG.2). Well plate204may be any suitable size and/or shape, and have any suitable number of wells disposed thereon (e.g., 6, 24, 96, 384, 1536, etc.). Moreover, the wells may be arranged in any suitable pattern on well plate204, such as a 2:3 rectangular matrix, for example.

Visual inspection system102further includes an illumination system208, and an imager210that is configured to acquire images. Illumination system208may include any suitable number and/or type(s) of light source(s) configured to generate source light, and illuminates each well of well plate204when that well is positioned in the optical path of imager210. In various embodiments, each well may have one or more transparent and/or opaque portions. For example, each of the wells may be entirely transparent, or may have transparent bottoms with the side walls being opaque. Each of the wells may generally be cylindrical, or have any other suitable shape (e.g., a cube, etc.).

Visual inspection system102may image each of the wells in well plate204sequentially. To this end, visual inspection system102may be configured to move imager210, and/or one or more optical elements (e.g., mirrors) that adjust the optical path of imager210, so as to successively align each of the wells with the optical path of imager210for individual well analysis. Alternatively, visual inspection system102may move stage202along one or more (e.g., x and/or y) axes to successively align the different wells. Imager210, stage202, and/or other components of visual inspection system102may be coupled to one or more motorized actuators, for example. Regardless of which mechanism is used to align different wells with the optical path of imager210, as each well is aligned imager210acquires one or more images of the illuminated well.

It is understood thatFIG.2shows only one example embodiment of visual inspection system102, and that others are possible. For example, visual inspection system102may include multiple imagers similar to imager210(e.g., for three-dimensional imaging), illumination system208may instead be configured to provide backlighting for well plate204, and so on. Moreover, while not shown inFIG.2, visual inspection system102may include one or more communication interfaces and processors to enable communication with computer system104, and/or one or more processors to provide local control of certain operations (e.g., capturing images by imager210, if not controlled by computer system104).

Referring again now toFIG.1, computer system104may, in this embodiment, generally be configured to control/automate the operation of visual inspection system102, and to receive and process images captured/generated by visual inspection system102, as discussed further below. Computer system104is also coupled to a training server106via a network108. Network108may be a single communication network, or may include multiple communication networks of one or more types (e.g., one or more wired and/or wireless local area networks (LANs), and/or one or more wired and/or wireless wide area networks (WANs) such as the Internet). As discussed further herein, training server106is generally configured to train one or more machine learning (ML) models109, which training server106sends to computer system104via network108. In various embodiments, training server106may provide ML model(s)109as a “cloud” service (e.g., Amazon Web Services), or training server106may be a local server. Alternatively or additionally, ML model(s)109is/are transferred to computer system104by a technique other than a remote download (e.g., by physically transferring a portable storage device to computer system104), in which case system100may not include network108. In some embodiments, one, some or all of ML model(s)109may be trained on computer system104, and then uploaded to server106. In other embodiments, computer system104performs the model training locally without uploading the ML model(s)109to training server106, in which case system100may omit both network108and training server106. As yet another example, system100may include a cloud computing environment in which training server106(or another server that is not shown inFIG.1but is communicatively coupled to computer system104via network108) performs the scoring, classification, and/or other operations discussed below in connection with computer system104. In some embodiments, some or all of the components of computer system104shown inFIG.1(e.g., one, some or all of modules120through126) are instead included in visual inspection system102, in which case visual inspection system102may communicate directly with training server106via network108.

Computer system104may be a general-purpose computer that is specifically programmed to perform the operations discussed herein, or may be a special-purpose computing device. As seen inFIG.1, computer system104includes a processing unit110, a network interface112and a memory unit114. In some embodiments, however, computer system104includes two or more computers that are either co-located or remote from each other. In these distributed embodiments, the operations described herein relating to processing unit110, network interface112and/or memory unit114may be divided among multiple processing units, network interfaces and/or memory units, respectively.

Processing unit110includes one or more processors, each of which may be a programmable microprocessor that executes software instructions stored in memory114to execute some or all of the functions of computer system104as described herein. Processing unit110may include one or more graphics processing units (GPUs) and/or one or more central processing units (CPUs), for example. Alternatively, or in addition, some of the processors in processing unit110may be other types of processors (e.g., application-specific integrated circuits (ASICs), field-programmable gate arrays (FPGAs), etc.), and some of the functionality of computer system104as described herein may instead be implemented in hardware. Network interface112may include any suitable hardware (e.g., a front-end transmitter and receiver hardware), firmware, and/or software configured to communicate with training server106via network108using one or more communication protocols. For example, network interface112may be or include an Ethernet interface, enabling computer system104to communicate with training server106over the Internet or an intranet, etc. Memory unit114may include one or more volatile and/or non-volatile memories. Any suitable memory type or types may be included, such as read-only memory (ROM), random access memory (RAM), flash memory, a solid-state drive (SSD), a hard disk drive (HDD), and so on. Collectively, memory unit114may store one or more software applications, the data received/used by those applications, and the data output/generated by those applications.

Memory unit114stores the software instructions of a CPE detection application118that, when executed by processing unit110, determines a CPE status for the contents of a well based on a well image. While various modules of application118are discussed below, it is understood that those modules may be distributed among different software applications, and/or that the functionality of any one such module may be divided among different software applications.

In some embodiments, a visual inspection system (VIS) control module120of application118controls/automates operation of visual inspection system102, via commands or other messages, such that images of the samples within the wells of well plate204inFIG.2can be generated with little or no human interaction. Visual inspection system102may send the images to computer system104for storage in memory unit114, or another suitable memory not shown inFIG.1. The operation of VIS control module120is discussed in further detail below. As noted above, however, visual inspection system102may not be externally controlled in certain embodiments, in which case VIS control module120may have less functionality than is described herein (e.g., only handling the retrieval of images from visual inspection system102), or may be omitted entirely from application118.

An image pre-processing module122of application118performs one or more operations to prepare a given well image for further processing. In particular, image pre-processing module122partitions a well image into a number of sub-images, and may perform one or more other tasks (e.g., removing portions of the well image that do not depict any contents of the well, and/or processing the well image to enhance contrast and/or remove noise, etc.). The sub-images may be square images, rectangular images, or have some other suitable shape (e.g., a pie slice shape that extends from the center of the well to the outer perimeter of the well). All sub-images may be of equal size, or the sizes may differ (e.g., with larger square images near the well center, and smaller square images near the outer perimeter of the well). The operation of image pre-processing module122is discussed in further detail below.

A sub-image scoring module124of application118analyzes each of some or all of the sub-images generated by image pre-processing module122using a CNN (e.g., one of ML model(s)109). For each sub-image, the CNN outputs a score that indicates a likelihood that the well contents depicted in that sub-image exhibit CPE. Thus, each score may be viewed as a confidence level associated with a positive CPE classification for a respective sub-image. The CNN may include any suitable number of convolutional layers for two-dimensional convolution (e.g., to detect features such as edges within images), any suitable number of pooling layers (e.g., a down-sampling layer, to reduce computation while preserving the relative locations of features), and any suitable number of fully-connected layers (e.g., to provide high-level reasoning based on features). Alternatively (e.g., if visual inspection system102implements three-dimensional imaging techniques), the CNN of sub-image scoring module124may utilize three-dimensional convolution to detect features in three dimensions. The operation of sub-image scoring module124is discussed in further detail below.

A CPE classification module126of application118analyzes the sub-image scores for a particular well image, and outputs a CPE status for the well image. The CPE status may be binary (e.g., “CPE” versus “not CPE”), in which case CPE classification module126may generate the status by inputting the sub-image scores to an SVM (e.g., one of ML model(s)109). If scores were determined for n+1 different sub-images of a well, for example, the SVM may classify the CPE status for the well contents using an n-dimensional hyperplane (e.g., a hyperplane constructed during training by training server106). In other embodiments, the CPE status is some other suitable indicator relating to the existence of CPE, the likelihood of CPE, and/or the extent to which CPE exists, in the entirety of the well contents. For example, the CPE status may be a score that indicates a likelihood that the well contents exhibit CPE. As such, the CPE status may be expressed as a probability such as a percentage. Optionally, a determination may be made (for example, whether the risk of CPE is sufficiently low to use the well contents for further cell culture) based on whether the likelihood that the well contents exhibit CPE falls below a specified threshold, for example, less than or equal to 50%, 40%, 30%, 20%, 10%, 5%, 3%, 2%, 1%, or 0.1% probability that the well contents exhibited CPE. As another example, the CPE status may be the percentage portion of the well contents (e.g., by area) that exhibits CPE. In some embodiments, CPE classification module126also provides other information, such as information relating to the exhibited morphology (e.g., elongation, cell lysis, etc.). The operation of CPE classification module126is discussed in further detail below.

Operation of system100, according to some embodiments, will now be described with reference toFIGS.1and2, and with reference to a particular embodiment in which computer system104controls visual inspection system102and implements models trained by training server106. Initially, in this embodiment, training server106trains ML model(s)109using data stored in a training database130(e.g., input/feature data, and corresponding labels). ML model(s)109includes a CNN implemented by sub-image scoring module124, and possibly an SVM implemented by CPE classification module126. Training database130may include a single database stored in a single memory (e.g., HDD, SSD, etc.), a single database distributed across multiple memories, or multiple databases stored in one or more memories. To train the CNN implemented by sub-image scoring module124, training database130may include a large number of training data sets each corresponding to a single sub-image (e.g., with the same magnification level, size and/or other characteristics as the sub-images output by image pre-processing module122), along with a label indicating a correct classification for that sub-image (e.g., “CPE” or “not CPE”). The labels may be classifications that were made by human analysts when reviewing the training sub-images. In some embodiments, the training data includes images of a variety of cell lines, to ensure that the CNN can accurately score sample sub-images across different cell lines.

Alternatively, to improve classification accuracy, ML model(s)109may include a different CNN for each of multiple cell lines (e.g., L929, PG4, Vero, and 324K cell lines), with each CNN having been trained using only sub-images that depict cells of the corresponding cell line. In such embodiments, computer system104may initially obtain copies of the CNNs for all cell lines, and sub-image scoring module124may select and implement the CNN corresponding to the cell line that is currently being inspected (e.g., as indicated by a user entering the cell line via a user interface of computer system104, not shown inFIG.1). Alternatively, computer system104may only retrieve the CNN for the cell line currently being inspected on an as-needed basis (e.g., by sending a request, including an indication of the user-specified cell line, to training server106). If ML model(s)109include an SVM implemented by CPE classification module126, the SVM may or may not be specific to the cell line currently being inspected, depending on the embodiment.

In some embodiments where ML model(s)109include a CNN (or one CNN per cell line, etc.) and an SVM (or one SVM per cell line, etc.), the SVM(s) is/are trained using outputs of the CNN(s). If ML model(s)109include a separate SVM for each cell line, each SVM may be trained using outputs of the trained CNN corresponding to that same cell line. As one example, for a given cell line, a CNN may be trained using thousands of well sub-images that were manually labeled by human analysts. Outputs (scores) generated by the CNN may then be used as inputs for training the SVM, with the labels for training the SVM (e.g., “CPE” or “not CPE” for entire well images) also being provided by a human analyst, or being determined automatically based on the manual labels that were assigned to the sub-images.

In some embodiments, training server106uses additional labeled data sets in training database130in order to validate the generated ML model(s)109(e.g., to confirm that a given one of ML model(s)109provides at least some minimum acceptable accuracy). Training server106then provides ML model(s)109to computer system104(e.g., via a remote download over network108) or, in a cloud computing embodiment, either implements ML model(s)109locally or provides ML model(s)109to one or more other servers. In some embodiments, training server106also updates/refines one or more of ML model(s)109on an ongoing basis. For example, after ML model(s)109are initially trained to provide a sufficient level of accuracy, visual inspection system102or computer system104may provide additional images to training server106over time, and training server106may use supervised or unsupervised learning techniques to further improve the model accuracy.

Each of the wells within well plate204of visual inspection system102is at least partially filled, either automatically or manually, with a medium that includes suitable nutrients for cells (e.g., amino acids, vitamins, etc.), growth factors, and/or other ingredients, and the well is inoculated with cells of a particular cell line. As used herein, a “particular cell line” refers to a cell line having a discrete identity, such as a specified cell line. Well plate204is then loaded onto stage202, and VIS control module120causes visual inspection system102to move stage202, illumination system208, and/or other components (e.g., one or more mirrors) in small increments, and to activate imager210(and possibly illumination system208) in a synchronized manner, such that imager210captures at least one image for each of the wells in well plate204.

Either as well images are generated, or in batches after subsets (or all) of the images have been generated (e.g., after locally storing all images on a hard drive), visual inspection system102sends the images to computer system104for automated analysis. As with the process of capturing well images, the process of transferring images to computer system104may be automated (e.g., triggered by commands from VIS control module120), in some embodiments.

The process of imaging the wells in well plate204may be repeated in certain embodiments and/or scenarios. For a TCID50assay, for instance, VIS control module120may cause visual inspection system102to capture an image of each well at each of a series of different dilution levels. In some embodiments, VIS control module120(or another module within application118) also controls/automates a system (not shown inFIG.1) that sets the dilution levels for the well samples.

For each of the well images received from visual inspection system102, as noted above, image pre-processing module122partitions the well image into sub-images, and possibly performs one or more other pre-processing operations such as removing parts of the image that depict areas outside of the well. Sub-image scoring module124then uses a CNN of ML model(s)109to score each sub-image, with the score indicating the likelihood that the well contents depicted in the sub-image exhibit CPE. The score may be a confidence level associated with a classification of “CPE,” for example. CPE classification module126then analyzes all of the scores for the sub-images of that well to determine the CPE status of the well. For example, CPE classification module126may use an SVM of ML model(s)109to classify the well contents as “CPE” or “not CPE.” As another example, CPE classification module126may apply one or more heuristics to classify the well contents (e.g., by classifying the contents of the well as “CPE” any time that the scores for the sub-images, when added, exceed some threshold value, or any time at least three sub-images have a score over 0.5000, etc.). Regardless of how CPE classification module126uses the scores to classify the well contents, the process may be repeated for different well images until a suitable stopping point is reached (e.g., until images of all wells in a well plate are analyzed, or until images of all wells at all desired dilution levels are analyzed, etc.).

Application118also generates output data reflecting the classification/status as determined by CPE classification module126. This output data may take various forms, and be used in various ways, depending on the embodiment. For example, application118may cause a user interface (e.g., a GUI displayed on a screen of computer system104or another system, not shown inFIG.1) to present the output data, including the CPE status of one or more well images, to a user. As another example, application118may send the output data to another application being executed on computer system104(or another system not shown inFIG.1), e.g., to trigger a next stage in a viral clearance or other process. As another example, application118may, based on the generated output data, cause samples within wells exhibiting CPE to be discarded or set aside for other purposes.

FIG.3depicts example images of various well samples of different cell lines, with and without CPE, to illustrate some of the challenges that may be associated with determining CPE status of a sample based on a well image if using conventional approaches. A first image pair300shows L929 (left) and 324K (right) cell lines that do not exhibit CPE, while a second image pair302shows two different morphologies of the L929 cell line when exhibiting CPE. Each of the images in image pair300and each of the images in image pair302may be a sub-image from a larger well image, for example.

As can be seen from the image pairs300and302, L929 cells exhibiting CPE may be quite difficult to distinguish from 324K cells not exhibiting CPE. Accordingly, as noted above, sub-image scoring module124may implement a CNN that is specific to the cell line being inspected. Image pair302further shows that even a single cell line can have very different morphologies (e.g., when infected by different viruses). Thus, even for a single cell line, it may be beneficial for training server106to train the CNN corresponding to that cell line using samples with different morphologies. Well sub-images corresponding to different morphologies may be intentionally introduced into training database130, or may simply be a result of having a suitably large database (e.g., thousands, or tens of thousands, etc., of well sub-images).

A third image pair304shows, on the left, an image of an entire well containing cells of the L929 cell line, and on the right, a specific sub-image corresponding to one portion of that well image. This example illustrates the fact that, conventionally, CPE may also be difficult to detect due to its localization within the well. In the image pair304, for instance, CPE is exhibited as a relatively small spot within the well. The potential localization of CPE, and/or other patterns or trends of CPE that may occur with different cell lines and/or different viruses, may inherently be accounted for by CPE classification module126when analyzing the sub-image scores for a given well image. To ensure that CPE classification module126can handle such variations, an SVM of CPE classification module126may have been trained using sub-image score arrangements that reflect different types of CPE patterns and/or localization.

FIG.4depicts an example image400of a well402. Well402may be one of the wells in well plate204, and/or well image400may be an image that was generated by visual inspection system102in response to a command from VIS control module120, for example. Well image400may represent a bottom-up perspective of well402.

Image pre-processing module122partitions well image400into a number of sub-images404. WhileFIG.4shows that sub-images404are generated only in areas of image400that depict at least a portion of well402, in other embodiments the entire well image400may be partitioned into equal-size sub-images, and/or sub-images of different sizes and/or shapes may be generated. Regardless of whether the entire well image400is partitioned, image pre-processing module122may discard or ignore the portions of well image400that do not depict at least a portion of well402. This cropping of well image400may occur before or after the partitioning into sub-images404. To ensure that sub-image scoring module124can accurately assess sub-images that include part of the wall of well402, and/or areas outside of well402, the training data for the CNN of sub-image scoring module124may have included similar sub-images. Alternatively, image pre-processing module122may entirely remove the wall of well402, and the areas outside well402, prior to the analysis performed by the CNN of sub-image scoring module124.

FIG.4also depicts an expanded view of one of sub-images404. In the depicted embodiment and scenario, the CNN of sub-image scoring module124has generated a score of 0.99999 for the expanded-view sub-image404, as a result of a CPE spot in the corresponding portion of well402. While not shown inFIG.4, it is understood that scores for all of the other sub-images404may also be determined by sub-image scoring module124. In other embodiments, however, not all of sub-images404are scored. In an example embodiment where CPE classification module126classifies the contents of any given well as “CPE” so long as at least one of the sub-images in the well is scored over some threshold (e.g., over 0.90000), for example, then sub-image scoring module124may save processing time/power by ceasing to analyze additional sub-images as soon as a first sub-image score for that well exceeds the threshold. By way of example, the threshold may be greater than or equal to 0.70, 0.80, 0.85, 0.90, 0.95, 0.97, 0.98, 0.99, or 0.999 (which may also be expressed as corresponding percentage probabilities, such as 70%, 85%, 90%, 95%, 97%, 98%, 99%, or 99.9%). Any system or method or computer readable medium as described herein may cease to analyze additional sub-images as soon as a first sub-image score for that well exceeds the threshold.

As noted herein, in some embodiments where CPE classification module126uses an SVM, and the SVM is trained specifically for the cell line being inspected. In this manner, the classification process performed by the SVM may inherently take into account patterns that are typical for that cell line. As a relatively simple example, if a first cell line typically exhibits only smaller, localized CPE spots, while a second cell line can (with roughly equal probability) exhibit CPE as either small spots or larger, contiguous areas, an SVM for the first cell line may be more likely to classify the contents of well402as “CPE” if the sub-images404having high scores collectively form a spot-like pattern, whereas an SVM for the second cell line may not give much (if any) weight to the relative positioning of the sub-images404having high scores.

FIG.5is a flow diagram of an example method500for detecting CPE in a well sample. Method500may be implemented by one or more portions of system100(e.g., visual inspection system102and computer system104) or another suitable system. As a more specific example, block502of method500may be implemented by at least a portion of visual inspection system102ofFIGS.1and2, while blocks504through510may be implemented by computer system104(e.g., by processing unit110when executing instructions stored in memory unit114).

At block502of method500, an image of a well containing cells and a medium (and possibly viruses, e.g., according to a controlled dilution) is generated by an imaging unit (e.g., by imager210inFIG.2). The medium may contain cell nutrients, growth factors, etc., and was previously inoculated with cells (e.g., cells of a single cell line).

At block504, the well image is pre-processed, at least by partitioning the well image into multiple sub-images that each correspond to a different portion of the imaged well. In some embodiments, the pre-processing also includes one or more other operations, such as removing one or more portions of the well image that correspond to one or more areas outside of the well, for example.

At block506, for each of the sub-images, a respective score is determined using a CNN. Block506may occur entirely after block504, or partially in parallel with block504(e.g., as sub-images are generated). The score for each sub-image is indicative of the likelihood that any cells in the corresponding portion of the well exhibit CPE. The score may be a confidence level associated with a “CPE” classification, for example. The score may be output by the CNN, or a result of some further processing of the CNN output and/or other factors. The CNN may be specific to a particular cell line corresponding to the cells in the well (e.g., the CNN may have been trained using labeled images of wells containing cells of that cell line). In one such embodiment, method500includes an additional block, occurring sometime prior to block506, in which the appropriate CNN is selected from among multiple CNNs associated with different cell lines (e.g., based on an input indicating the cell line that corresponds to the cells in the well, such as a user input, or an identifier such as a barcode associated with the well).

At block508, a CPE status of the cells contained in the well is determined based on the scores determined at block506. The CPE status may be a binary indicator of whether the cells exhibit CPE. For example block508may include inputting the respective scores to an SVM, which outputs the CPE status (e.g., “CPE” or “no CPE”) or a value on which the CPE status is based (e.g., based on one or more additional factors). In other embodiments, the CPE status is not binary. For example, the CPE status may be a probability of the existence of CPE, and/or an extent to which CPE is (or is likely) exhibited.

At block510, output data indicating the CPE status determined at block508is generated. The output data may be displayed to a user on a user interface of a computing device (e.g., by sending the output data, and a command that causes display of the output data, to another device or module), for example, and/or may be sent to one or more other software modules and/or computer systems for various purposes (e.g., to indicate viral clearance for a particular batch and/or trigger a next phase of cell line development, etc.).

Although the systems, methods, devices, and components thereof, have been described in terms of exemplary embodiments, they are not limited thereto. The detailed description is to be construed as exemplary only and does not describe every possible embodiment of the invention because describing every possible embodiment would be impractical, if not impossible. Numerous alternative embodiments could be implemented, using either current technology or technology developed after the filing date of this patent that would still fall within the scope of the claims defining the invention.