Abstract: An analytics system uses one or more machine-learned models to predict a hormone receptor status from a H&E stain image. The system partitions H&E stain images each into a plurality of non-overlapping image tiles. Bags of tiles are created through sampling of the image tiles. For each H&E stain image, the system generates a feature vector from a bag of tiles sampled from the partitioned image tiles. The analytics system trains one or more machine-learned models with training H&E stain images having a positive or negative receptor status. With the trained models, the analytics system predicts a hormone receptor status by applying a prediction model to the feature vector for a test H&E stain image.

Abstract: A quantitative phase image generating method for a microscope, includes: irradiating an object with illumination light; disposing a focal point of an objective lens at each of a plurality of positions that are mutually separated by gaps ?z along an optical axis of the objective lens, and detecting light from the object; generating sets of light intensity distribution data corresponding to each of the plurality of positions based upon the detected light; and generating a quantitative phase image based upon the light intensity distribution data; wherein the gap ?z is set based upon setting information of the microscope.

Abstract: A cell image analysis method may include: obtaining, for each of cell images, a value of a feature parameter to be used in determination of a type of a cell, by analyzing the cell images; and displaying the value of the feature parameter in association with the each of the cell images.

Abstract: A system and method for image-guided microscopic illumination are provided. A processing module controls an imaging assembly such that a camera acquires an image or images of a sample in multiple fields of view, and the image or images are automatically transmitted to a processing module and processed by the first processing module automatically in real-time based on a predefined criterion so as to determine coordinate information of an interested region in each field of view. The processing module also controls an illuminating assembly to illuminate the interested region of the sample according to the received coordinate information regarding to the interested region, with the illumination patterns changing among the fields of view.

Abstract: Systems and methods for predicting images with enhanced spatial resolution using a neural network are provided herein. According to an aspect of the invention, a method includes accessing an input image of a biological sample, wherein the input image includes a first spatial resolution and a plurality of spectral images, and wherein each spectral image of the plurality of spectral images includes data from a different wavelength band at a different spectral channel; applying a trained artificial neural network to the input image; generating an output image at a second spatial resolution, wherein the second spatial resolution is higher than the first spatial resolution, and wherein the output image includes a fewer number of spectral channels than the plurality of spectral images included in the input image; and outputting the output image.

Abstract: Embodiments of the present systems and methods may provide imaging techniques for multidirectional imaging, light conditioning, illumination sequences, or machine learning to create algorithms created from training by other advanced imaging techniques. In an embodiment, a method for generating an image may comprise obtaining an image of an object produced by a camera and generating, from the obtained image produced by a conventional camera, using an artificial intelligence model and imaging process, an output image including additional information similar to additional information present in an image of the object produced by an advanced imaging system.

Abstract: A hyperspectral imaging spectrophotometer and system, with calibration, data collection, and image processing methods designed to match human visual perception and color matching of complex colored objects.

Abstract: Methods are provided to project depth-spanning stacks of limited depth-of-field images of a sample into a single image of the sample that can provide in-focus image information about three-dimensional contents of the image. These methods include applying filters to the stacks of images in order to identify pixels within each image that have been captured in focus. These in-focus pixels are then combined to provide the single image of the sample. Filtering of such image stacks can also allow for the determination of depth maps or other geometric information about contents of the sample. Such depth information can also be used to inform segmentation of images of the sample, e.g., by further dividing identified regions that correspond to the contents of the sample at multiple different depths.

Abstract: A visual analysis method for cable element identification includes steps of constructing and labeling a picture data set, preparing a training data set and training a model, to train the preset identification and analysis model, such that the preset identification and analysis model can have accuracy of identification of cable elements; then, cable element information existing in a target image screen is identified by the completely trained preset identification and analysis model, so as to label a target picture; in the analysis method, the produced and manufactured cable elements can be shot, and then, shot pictures are identified and analyzed by using the preset identification and analysis model, such that a structural quality condition of each cable element is rapidly and comprehensively determined, possible structural defects of each cable element can be conveniently and accurately known, and the cable elements with unqualified quality can be screened out in time.

Abstract: A system and method for developing applications (Apps) for automated assessment and analysis of processed biological samples. Such samples are obtained, combined with nutrient media and incubated. The incubated samples are imaged and the image information is classified according to predetermined criteria. The classified image information is then evaluated according to Apps derived from classified historical image information in a data base. The classified historical image information is compared with the classified image information to provide guidance on further processing of the biological sample through Apps tailored to process provide sample process guidance tailored to the classifications assigned to the image information.

Abstract: A method of using a machine learning model to output a task-specific prediction may include receiving a digitized cytology image of a cytology sample and applying a machine learning model to isolate cells of the digitized cytology image. The machine learning model may include identifying a plurality of sub-portions of the digitized cytology image, identifying, for each sub-portion of the plurality of sub-portions, either background or cell, and determining cell sub-images of the digitized cytology image. Each cell sub-image may comprise a cell of the digitized cytology image, based on the identifying either background or cell. The method may further comprise determining a plurality of features based on the cell sub-images, each of the cell sub-images being associated with at least one of the plurality of features, determining an aggregated feature based on the plurality of features, and training a machine learning model to predict a target task based on the aggregated feature.

Abstract: The present disclosure provides a rapid three-dimensional imaging system based on a multi-angle 4Pi microscope. The system includes: an illumination module, configured to obtain a parallel light of which a size covering a projection surface of a spatial light modulator; a wavefront modulation module, configured to place the LCOS device on a Fourier plane of an illumination end; a two-dimensional scanning module, configured to control a light beam to realize a two-dimensional scanning on an object plane; an illumination interference module, configured to generate point spread function PSFs of a 4Pi through an illumination interference to irradiate a fluorescent sample; an imaging module, configured to acquire interference images of two fluorescent signals; and a controller, configured to control the wavefront modulation module to adjust a polarization direction of the light to generate PSFs of the 4Pi with different inclination angles.

Abstract: A Computer Aided Diagnosis, CADx, system (200) is described that comprises: at least one input (210, 212, 214) configured to provide at least one input medical image; and a CADx processing engine (220) configured to receive and process the at least one input medical image and produce at least one CADx score. A CADx score mapping circuit is operably coupled to the CADx processing engine (220) and configured to: map the at least one CADx score to a risk adjusted virtual score; and generate an output (235) of at least the risk adjusted virtual score associated with the processed at least one input medical image. The at least one CADx score and the risk adjusted virtual score correspond to an equivalent risk of condition or disease associated with a patient.

Abstract: A method for video object detection includes detecting an object in a first video frame, and selecting a first interest point and a second interest point of the object. The first interest point is in a first region of interest located at a first corner of a box surrounding the object. The second interest point is in a second region of interest located at a second corner of the box. The second corner is diagonally opposite the first corner. A first optical flow of the first interest point and a second optical flow of the second interest point are determined. A location of the object in a second video frame is estimated by determining, in the second video frame, a location of the first interest point based on the first optical flow and a location of the second interest point based on the second optical flow.

Abstract: A generalizable and interpretable deep learning model for predicting biomarker status and biomarker metrics from histopathology slide images is provided.

Abstract: Embodiments discussed herein facilitate building and/or employing model(s) for determining tumor prognoses based on a combination of radiomic features and pathomic features. One example embodiment can perform actions comprising: providing, to a first machine learning model, at least one of: one or more intra-tumoral radiomic features associated with a tumor or one or more peri-tumoral radiomic features associated with a peri-tumoral region around the tumor; receiving a first predicted prognosis associated with the tumor from the first machine learning model; providing, to a second machine learning model, one or more pathomic features associated with the tumor; receiving a second predicted prognosis associated with the tumor from the second machine learning model; and generating a combined prognosis associated with the tumor based on the first predicted prognosis and the second predicted prognosis.

Abstract: A mask structure optimization device includes a classification target image size acquisition unit that is configured to acquire a size of a classification target image which is an image including a classification target, a mask size setting unit that is configured to set a size of a mask applied to the classification target image, a brightness detection unit that is configured to detect a brightness of each pixel within the classification target image at a position on an opposite side of the mask from the classification target image, a sum total brightness calculation unit that is configured to calculate the sum total brightness of the each pixel within the classification target image detected by the brightness detection unit, an initial value setting unit that is configured to set an initial value for a mask pattern of the mask, and a movement unit that is configured to relatively move the mask with respect to the classification target image.

Abstract: A surgical specimen imaging system includes a micro-X-ray computed tomography (CT) unit for CT imaging of the specimen and a structured light imaging (SLI) unit for optical imaging at multiple wavelengths, multiple phase offsets, and multiple structured-light pattern periods including unstructured light. The system's image processing unit receives CT and optical images and is configured by firmware in memory to co-register the images and process the optical images to determine texture at multiple subimages of the optical images, determined textures forming a texture map. The texture map is processed by a machine-learning-based classifier to determine a tissue type map of the specimen, and the tissue type map is processed with the CT images to give a 3D tissue-type map. In embodiments, the firmware extracts optical properties including scattering and absorption at multiple wavelengths and the classifier also uses these properties in generating the tissue type map.

Abstract: Various implementations disclosed herein include devices, systems, and methods that complete content for a missing part of an image of an environment. For example, an example process may include obtaining an image including defined content and missing parts for which content is undefined, determining a spatial image transformation for the image based on the defined content and the missing parts of the image, altering the image by applying the spatial image transformation, and completing the altered image.

Abstract: A storage assembly for storing a plurality of specimens includes a frame, a plurality of modular storage units for perfusion and/or incubation of one or more specimens removably coupled to the frame, a sample transfer apparatus configured to retrieve a specimen holder from a chosen modular storage unit of the plurality of modular storage units, and a control unit communicatively coupled to the sample transfer apparatus. The control unit is configured to cause the sample transfer apparatus to retrieve a specimen from a modular storage unit of the plurality of modular storage units and deliver the specimen to a delivery position.

Abstract: Disclosed herein are systems, methods and computer-program products to create synthetic immunohistochemistry (IHC) stained digital slides generated using artificial neural networks (ANNs). In some implementations, the created digital slides can be used as a ground truth to evaluate a method of analyzing IHC stained tissues.

Abstract: The invention is notably directed to a computer-implemented method for normalizing medical images, e.g., whole slide images. This method includes steps performed for each image of a first subset of images of a dataset. Actual quantities are estimated for each image, including actual stain vectors and, possibly, robust maximum stain concentrations (typically hematoxylin and eosin stain vectors and concentrations). The actual quantities estimated are assessed by comparing them to reference data based on reference quantities estimated for one or more images of a second subset of images of the dataset, where the second subset of images differ from the first subset of images. The reference quantities include reference stain vectors. For each image, either the actual quantities or the reference quantities for the dataset are selected as effective quantities, based on an outcome of the previous assessment of the actual quantities. Each image is then normalized.

Abstract: The present application provides a convolution calculation method, a convolution calculation apparatus, a terminal device, and a computer readable storage medium. The method includes: inputting an image to be processed into a deep learning model, and obtaining a to-be-blocked convolution group and a target size of a block from all convolution layers of the deep learning model; blocking all input channel data of a first to-be-blocked convolution layer in said convolution group according to the target size, a size of each block being the target size; obtaining an output result of said convolution group according to all blocks of all input channel data of said first convolution layer; inputting the output result of said convolution group to a specified network of the deep learning model. Sizes of blocks of the to-be-blocked convolution layer and bandwidth consumption can be adjusted to adapt to frequently updating and upgrading the deep learning model.

Abstract: An automated method and system for determining the risk of developing a cancer in a subject, the method comprising preparing a tissue sample obtained from the subject for visually identifying at least one biological marker associated with the cancer, digitally scanning the prepared tissue sample, analyzing the scanned image of the tissue sample to identify regions of interest, quantifying at least one parameter associated with the marker, and executing an algorithm using the quantified parameter to calculate a risk score, wherein the risk score is representative of the risk of the individual developing the cancer.

Abstract: Provided is a novel cell sheet evaluation method. A cell sheet evaluation method includes: a step of analyzing, based on an observed image of a cell sheet, a characteristic indicating shape uniformity of cells constituting the cell sheet; and a step of evaluating a binding state between cells in the cell sheet based on the analysis result.

Abstract: Embodiments include controlling a processor to perform operations, the operations comprising accessing a digitized image of a region of tissue (ROT) demonstrating cancerous pathology; extracting a set of radiomic features from the digitized image, where the set of radiomic features are positively correlated with programmed death-ligand 1 (PD-L1) expression; providing the set of radiomic features to a machine learning classifier; receiving, from the machine learning classifier, a probability that the region of tissue will experience cancer recurrence, where the machine learning classifier computes the probability based, at least in part, on the set of radiomic features; generating a classification of the region of tissue as likely to experience recurrence or non-recurrence based, at least in part, on the probability; and displaying the classification and at least one of the probability, the set of radiomic features, or the digitized image.

Abstract: An ensemble learning prediction method includes: establishing a plurality of base predictors based on a plurality of training data; initializing a plurality of sample weights of a plurality of sample data and initializing a processing set; in each iteration round, based on the sample data and the sample weights, establishing a plurality of predictor weighting functions of the predictors in the processing set and predicting each of the sample data by each of the predictors in the processing set for identifying a prediction result; evaluating the predictor weighting functions, and selecting a respective target predictor weighting function from the predictor weighting functions established in each iteration round and selecting a target predictor from the predictors in the processing set to update the processing set and to update the sample weights of the sample data.

Abstract: There is provided a method for generating a 3D physical model of a patient specific anatomic feature from 2D medical images. The 2D medical images are uploaded by an end-user via a Web Application and sent to a server. The server processes the 2D medical images and automatically generates a 3D printable model of a patient specific anatomic feature from the 2D medical images using a segmentation technique. The 3D printable model is 3D printed as a 3D physical model such that it represents a 1:1 scale of the patient specific anatomic feature. The method includes the step of automatically identifying the patient specific anatomic feature.

Abstract: A method and apparatus of a device that classifies a mesothelioma image is described. In an exemplary embodiment, the device segments the mesothelioma image into a region of interest that includes information useful for classification, and a background region, by applying a first convolutional neural network. In addition, the device tiles the region of interest into a set of tiles. For each tile, the device extracts a feature vector of that tile by applying a second convolutional neural network, where the features of the feature vectors represent local descriptors of the tile. Furthermore, the device processes the extracted feature vectors of the set of tiles to classify the image.

Abstract: An integrity monitoring system for a first image sensor includes an electronic processor configured to receive sensor data for the provision of image data associated with an environment of the avionic sensor. The electronic processor is configured to monitor the avionic sensor for integrity. The electronic processor is configured to perform at least one of: determining a presence of an optical feature associated with optics of the first image sensor, comparing overlap information derived from the sensor data and other sensor data, comparing characteristics of a digital output stream of the sensor data to expected characteristics, or comparing a first motion derived from the image data and a second motion derived from avionic position equipment.

Abstract: The method according to the invention utilizes a color decomposition of histological tissue image data to derive a density map. The density map corresponds to the portion of the image data that contains the stain/tissue combination corresponding to the stroma, and at least one gland is extracted from said density map. The glands are obtained by a combination of a mask and a seed for each gland derived by adaptive morphological operations, and the seed is grown to the boundaries of the mask. The method may also derive an epithelial density map used to remove small objects not corresponding to epithelial tissue. The epithelial density map may further be utilized to improve the identification of glandular regions in the stromal density map. The segmented gland is extracted from the tissue data utilizing the grown seed as a mask. The gland is then classified according to its associated features.

Abstract: An analytics system uses one or more machine-learned models to predict a hormone receptor status from a H&E stain image. The system partitions H&E stain images each into a plurality of image tiles. Bags of tiles are created through sampling of the image tiles. The analytics system trains one or more machine-learned models with training H&E stain images having a positive or negative receptor status. The analytics system generates, via a tile featurization model, a tile feature vector for each image tile a test bag for a test H&E stain image. The analytics system generates, via an attention model, an aggregate feature vector for the test bag by aggregating the tile feature vectors of the test bag, wherein an attention weight is determined for each tile feature vector. The analytics system predicts a hormone receptor status by applying a prediction model to the aggregate feature vector for the test bag.

Abstract: Systems, methods, devices, and other techniques using machine learning for interpreting, or assisting in the interpretation of, biologic specimens based on digital images are provided. Methods for improving image-based cellular identification, diagnostic methods, methods for evaluating effectiveness of a disease intervention, and visual outputs useful in assisting professionals in the interpretation of biologic specimens are also provided.

Abstract: Automation of microscopic pathological diagnosis relies on digital image quality, which, in turn, affects the rates of false positive and negative cellular objects designated as abnormalities. Cytogenetic biodosimetry is a genotoxic assay that detects dicentric chromosomes (DCs) arising from exposure to ionizing radiation. The frequency of DCs is related to radiation dose received, so the inferred radiation dose depends on the accuracy of DC detection. To improve this accuracy, image segmentation methods are used to rank high quality cytogenetic images and eliminate suboptimal metaphase cell data in a sample based on novel quality measures. When sufficient numbers of high quality images are found, the microscope system is directed to terminate metaphase image collection for a sample. The International Atomic Energy Agency recommends at least 500 images be used to estimate radiation dose, however often many more images are collected in order to select the metaphase cells with good morphology for analysis.

Abstract: Aspects of the present disclosure include a method for sorting cells of a sample based on an image of a cell in a flow stream. Methods according to certain embodiments include detecting light from a sample having cells in a flow stream, generating an image mask of a cell from the sample and sorting the cell based on the generated image mask. Systems having a processor with memory operably coupled to the processor having instructions stored thereon, which when executed by the processor, cause the processor to generate an image mask of a cell in a sample in a flow stream and to sort the cell based on the generated image mask are also described. Integrated circuit devices (e.g., field programmable gate arrays) having programming for generating an image mask and for determining one or more features of the cell are also provided.

Abstract: A cell observation system according to the present invention includes: a first image-acquisition device disposed in an incubator and acquires a first image of cells in a culturing vessel; a second image-acquisition device disposed outside the incubator; a processing device connected to the first image-acquisition device and the second image-acquisition device; and a display displays at least a region of the first image, as well as the second image. The second image-acquisition device includes a second image-acquisition unit that acquires a second image of the interior of the culturing vessel, that removed from the incubator. The processing device extracts target cells in the first image, calculates positions at which the extracted target cells are present and stores the positions in the memory, and causes the display to display the positions at which the target cells are present in a superimposed manner on an indication indicating the culturing vessel.

Abstract: Various image diagnostic systems, and methods of operating thereof, are disclosed herein. Example embodiments relate to operating the image diagnostic system to identify one or more tissue types within an image patch according to a hierarchical histological taxonomy, identifying an image patch associated with normal tissue, generating a pixel-level segmented image patch for an image patch, generating an encoded image patch for an image patch of at least one tissue, searching for one or more histopathological images, and assigning an image patch to one or more pathological cases.

Abstract: The method of the present invention classifies the nuclei in prostate tissue images with a trained deep learning network and uses said nuclear classification to classify regions, such as glandular regions, according to their malignancy grade. The method according to the present disclosure also trains a deep learning network to identify the category of each nucleus in prostate tissue image data, said category representing the malignancy grade of the tissue surrounding the nuclei. The method of the present disclosure automatically segments the glands and identifies the nuclei in a prostate tissue data set. Said segmented glands are assigned a category by at least one domain expert, and said category is then used to automatically assign a category to each nucleus corresponding to the category of said nucleus' surrounding tissue. A multitude of windows, each said window surrounding a nucleus, comprises the training data for the deep learning network.

Abstract: An information processing system and a method for operating same are provided. The information processing system includes a first information processing apparatus and a second information processing apparatus. The first information processing apparatus is configured to display a first synchronous image in a first window, the first window having an operation right. The second information processing apparatus has a synchronous state or an asynchronous state. The second information processing apparatus is configured to: display a second synchronous image; in response to a first request, switch from the synchronous state to the asynchronous state; and in response to a second request, switch from the asynchronous state to the synchronous state.

Abstract: The invention relates, in part, to methods of deriving a value for % biomarker positivity (PBP) for all cells or optionally, one or more subsets thereof, present in a field of view of a tissue sample from a cancer patient. The values for PBP can be indicative of a patient's response to immunotherapy.

Abstract: A method of analyzing cell populations includes receiving, by a transceiver of a computing device, an image of a tissue sample. The method also includes analyzing, by a processor of the computing device, the image of the tissue sample using image analysis. The image analysis parameters are determined by machine learning. The method also includes determining, by the processor and based on the analyzing, one or more cell features, such as shape, of a cell in the tissue sample. The method further includes identifying, by the processor, an interaction of the cell with an additional cell based at least in part on the shape of the cell.

Abstract: A cell observation system according to the present invention includes: a first image-acquisition device disposed in an incubator and acquires a first image of cells in a culturing vessel; a second image-acquisition device disposed outside the incubator; a processing device connected to the first and second image-acquisition devices; and a display. The second image-acquisition device includes a second image-acquisition unit that acquires a second image of the interior of the culturing vessel that removed from the incubator, a support that supports the second image-acquisition unit and the culturing vessel, and a position measuring unit that measures a position between the culturing vessel and the second image-acquisition unit at the time of acquiring the second image. The processing device extracts target cells in the first image, calculates positions of the target cells, and displays the relationship between the positions of the target cells and the second image.

Abstract: Embodiments of a system, method and apparatus incorporate two-stage identification of targets in hyperspectral image analysis. In various embodiments, unmixing is employed that integrates F-test and model averaging approaches. Further, a multi-tier target library process provides an improvement in the spectra that can be used to detect target materials and spectra that can be used for unmixing in identification. Additionally, the hierarchical identification of the present disclosure combines probabilities from model averaging to generate target identifications simultaneously at multiple levels of specificity.

Abstract: An image processing apparatus includes a first extraction unit configured to extract a first target region from an image using a trained classifier, a setting unit configured to set region information to be used in a graph cut segmentation method based on a first extraction result including the first target region, a second extraction unit configured to extract a second target region using the graph cut segmentation method based on the set region information, and a generation unit configured to generate a ground truth image corresponding to the image based on a second extraction result including the second target region.

Abstract: The present invention concerns identifying matching tissue objects in two sections of a tissue block, as imaged using a microscope, for the purpose of mapping the biomarker-specific staining in one section onto the other section. The invention is useful because, in many workflows, it is not possible to add a sufficient number of different biomarker-specific stains to a single slide. By staining instead multiple slides, and mapping the staining data obtained across the slides, one obtains a data set that is similar to what one would be able to obtain by staining a single slide with all those stains. The invention first identifies a set of obviously correct matches, then propagates from those matches, using a priority queue driven process, to optimally match up all fibers in the two sections. The matching is based on the shape and neighborhood configuration of each tissue object.

Abstract: A system (90) for imaging microscopic samples comprises a light source (31) for exciting fluorescence from at least one sample, a photosensor (40) configured to detect light deflected by a beam splitter (20) from an optical excitation path directed to the sample and output an electrical signal of optical flux to the sample, a camera (30) configured to receive and form an image of fluorescence light emitted from the sample, and a controller (134) comprising an integrator (122) configured to integrate the electrical signal from the photodetector and a comparator configured to compare the integrated output to a predetermined threshold, wherein the controller is configured to control an exposure time of the camera such that each sample receives substantially the same total optical flux of incident light during a duration of camera exposure which is terminated when a predetermined threshold representative of the total optical flux is met.

Abstract: An image processor includes an image generator configured to generate corresponding image data corresponding to first microscopic image data obtained under a first observation condition, based on second microscopic image data and third microscopic image data obtained under a second observation condition, and an image output unit configured to output the corresponding image data. The corresponding image data may be image data corresponding to a first focal plane from which the first microscopic image data are obtained, and wherein the second microscopic image data and the third microscopic image data may be image data on a second focal plane and a third focal plane, respectively, which are different from the first focal plane.

Abstract: Techniques are provided for determining a cell count within a whole slide pathology image. The image is segmented using a global threshold value to define a tissue area. A plurality of patches comprising the tissue area are selected. Stain intensity vectors are determined within the plurality of patches to generate a stain intensity image. The stain intensity image is iteratively segmented to generate a cell mask using a local threshold value that is and gradually reduced after each iteration. A chamfer distance transform is applied to the cell mask to generate a distance map. Cell seeds are determined on the distance map. Cell segments are determined using a watershed transformation, and a whole cell count is calculated for the plurality of patches based on the cell segments. A client device may be configured for real-time cell counting based on the whole cell count.

Abstract: An image analysis method for generating data indicating a tumorigenic state of an image of a tissue or a cell. The image analysis method is an image analysis method for analyzing an image of a tissue or a cell using a deep learning algorithm of a neural network structure, analysis data are generated from the analysis target image including a tissue or cell to be analyzed, the analysis data are input to the deep learning algorithm, and data indicating the tumorigenic state of tissues or cells in the analysis target image are generated by the depth learning algorithm.

Abstract: A system for controlling a motion of a cell culture includes a tray adapted to hold a cell culture, a camera adapted to capture an image of the cell culture, and a device adapted to control a movement of the tray. The system also includes a processor adapted to determine a first movement of the tray, receive from the camera data representing an image of the cell culture, determine a characteristic of the cell culture based on the image data, determine a second movement of the tray based on the characteristic, the second movement being different from the first movement, and cause the tray to move in accordance with the second movement.