Disclosed herein are platforms, systems, and methods including a cell culture system that includes a cell culture container comprising a cell culture, the cell culture receiving input cells, a cell imaging subsystem configured to acquire images of the cell culture, a computing subsystem configured to perform a cell culture process on the cell culture according to the images acquired by the cell imaging subsystem, and a cell editing subsystem configured to edit the cell culture to produce output cell products according to the cell culture process.

A method for analyzing a biochip image is provided, including: (S1) acquiring and preprocessing the biochip image to obtain a preprocessed image; (S2) performing a correction for angle deflection on the preprocessed image to obtain a deflection-corrected image; and (S3) performing an enhancement processing on the deflection-corrected image, and identifying a positive or negative of an area of interest in the preprocessed image according to an image on which the enhancement processing has been performed. An apparatus (100) for analyzing a biochip image, a method for analyzing an image, a computer device (200) and a storage medium are disclosed.

An image display method includes the following operations (a) to (e). The (a) is of obtaining a plurality of two-dimensional images by two-dimensionally imaging a specimen, in which a plurality of objects to be observed are present three-dimensionally in the specimen, at a plurality of mutually different focus positions. The (b) is of obtaining image data representing a three-dimensional shape of the specimen. The (c) is of obtaining a three-dimensional image of the specimen based on the image data. The (d) is of obtaining the two-dimensional image selected from the plurality of two-dimensional images or a two-dimensional image generated to be focused on the plurality of objects based on the plurality of two-dimensional images as an integration two-dimensional image. The (e) is of integrating the integration two-dimensional image obtained in the (d) with the three-dimensional image obtained in the (c) and displaying an integrated image on a display unit.

A biological tissue is classified using a computing system. Image data comprising a plurality of images of an examination area of a biological tissue is received at the computing system. Each of the plurality of images is captured at different times during a period in which topical application of a pathology differentiating agent to the examination area of the tissue causes transient optical effects. The received image data is provided as an input to a machine learning algorithm operative on the computing system. The machine learning algorithm is configured to allocate one of a plurality of classifications to each of a plurality of segments of the tissue.

A system and method for reducing the responsibility of the user significantly by applying an optical system that can identify container like sample tubes with respect to their characteristics, e.g., shapes and inner dimensions, from their visual properties by capturing images from a rack comprising container and processing said images for reliably identifying a container tyle.

A method and apparatus of a device that classifies an image is described. In an exemplary embodiment, the device segments the 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.

A system and method of performing deep cell body segmentation on a biological sample is provided. The method includes receiving a first and a second stained image. The first image is processed using a trained machine learned model that outputs locations of a plurality of cell nuclei in the first stained image. Seed points are then determined based on the locations of the plurality of cell nuclei. The second image is then processed using the seed points to determine a plurality of cell membranes using a watershed segmentation. The second image is then post-processed and an output image is produced. The output image is then analyzed and gene sequencing is performed.

A system for identifying biomarkers in a digital image of a Hematoxylin and Eosin-stained slide of a target tissue includes a processor and an electronic network; and a memory having stored thereon computer-executable instructions that, when executed by the one or more processors, cause the computing system to: process segmented tile images determine a predicted biomarker presence; and transmit the predicted presence. A non-transitory computer-readable medium includes a set of computer-executable instructions that, when executed by one or more processors, cause a computer to: process segmented tile images; determine a predicted biomarker presence; and transmit the predicted presence. A computer-implemented method includes processing segmented tile images; determining a predicted biomarker presence; and transmitting the predicted presence.

Disclosed herein include embodiments of a system, a device, and a method for sorting a plurality cells of a sample. A plurality of raw images comprising pixels of complex values in a frequency space can be generated from a plurality of channels of fluorescence intensity data of fluorescence emissions of fluorophores, the fluorescence emissions being elicited by fluorescence imaging using radiofrequency-multiplexed excitation in a temporal space. Spectral unmixing can be performed on the raw images prior to a sorting decision being made.

The disclosure provides a method of generating an artificial fluorescent image of cells is provided. The method includes receiving a brightfield image generated by a brightfield microscopy imaging modality of at least a portion of cells included in a specimen, applying, to the brightfield image, at least one trained model, the trained model being trained to generate the artificial fluorescent image based on the brightfield image, receiving the artificial fluorescent image from the trained model