Various example embodiments pertain to processing images that depict tissue samples using a neural network algorithm. The neural network algorithm includes multiple encoder branches that are copies of each other that share the same parameters. The encoder branches can, accordingly, be referred to as Siamese copies of each other.

Systems and methods for predicting a patient response to various agents and/or combinations of agents using ex vivo dosing and imaging are disclosed. In one example, a method of determining treatment efficacy includes analyzing a solid cell culture over time, e.g., first and second responses to a solid cell culture to respective treatments may be compared to determine a treatment efficacy of each treatment. Systems and methods for applying the treatments to the cell culture and analyzing the cell culture and efficacy are disclosed.

The present disclosure provides methods of preparing a biological specimen for imaging analysis, comprising fixing and clearing the biological specimen and subsequently analyzing the cleared biological specimen using microscopy. Also included are methods of quantifying cells, for example, active populations of cells in response to a stimulant. The present disclosure also provides devices for practicing the described methods. A flow-assisted clearing device provides rapid clearing of hydrogel-embedded biological specimens without the need of specialized equipment such as electrophoresis or perfusion devices.

A cell evaluation method evaluates the quality of a cell population including a plurality of cells. The cell evaluation method comprises: an index calculation step of calculating an index, based on a captured image of the cell population, the index including at least any one of an average distance representing a packing degree of the cells, a spring constant representing a degree of consistency in distances between the cells, and a hexagonal order parameter representing a degree to which an arrangement of the cells resembles a regular hexagon; and an evaluation step of evaluating the cell population, based on the index calculated in the index calculation step.

A computer-implemented method is provided for analyzing videos of a living system captured with microscopic imaging. The method can include obtaining a base dataset including one or more videos captured with microscopic imaging with at least one of the one or more videos including a cellular event, and cropping out, from the base dataset, sub-videos including one or more objects of interest that may be involved in the cellular event. An artificial neural network (ANN) model can be trained using the plurality of selected sub-videos as training data, to perform unsupervised video alignment, a query sub-video can be aligned using the trained ANN model, and a determination can be made whether or not the query sub-video includes the cellular event.

Methods for evaluating micrometastases in a tissue region of a subject are described. The methods include administering to the subject a detectably effective amount of a tumor-targeted photoactivatable immunoconjugate; allowing a sufficient amount of time for the tumor-targeted photoactivatable immunoconjugate to enter micrometastases in the tissue region; illuminating the tumor-targeted photoactivatable immunoconjugate; obtaining an image of the tissue region of the subject using a fluorescent imaging device, and evaluating the micrometastases in the tissue region by conducting algorithmic analysis of the image. Methods of treating micrometastases in a tissue region of a subject are also described.

Provided is an information processing apparatus including an information acquisition section (104) that acquires information of a first region (700) specified by a filling input operation on image data (610) of a living tissue by a user, and a region determination section (108) that executes fitting on a boundary of the first region on the basis of the image data and information of the first region and determines a second region (702) to be subjected to predetermined processing.

A method and a device are useful for detecting a binding of autoantibodies from a patient sample to double-stranded deoxyribonucleic acid (DNA) using Crithidia luciliae cells by fluorescence microscopy and by digital image processing.

A method for a system and method for morphometric detection of malignancy associated change (MAC) is disclosed including the acts of obtaining a sample; imaging cells to produce 3D cell images for each cell; measuring a plurality of different structural biosignatures for each cell from its 3D cell image to produce feature data; analyzing the feature data by first using cancer case status as ground truth to supervise development of a classifier to test the degree to which the features discriminate between cells from normal or cancer patients; using the analyzed feature data to develop classifiers including, a first classifier to discriminate normal squamous cells from normal and cancer patients, a second classifier to discriminate normal macrophages from normal and cancer patients, and a third classifier to discriminate normal bronchial columnar cells from normal and cancer patients.

Systems and methods are disclosed for receiving a target electronic image corresponding to a target specimen, the target specimen comprising a tissue sample of a patient, applying a machine learning system to the target electronic image to determine at least one characteristic of the target specimen and/or at least one characteristic of the target electronic image, the machine learning system having been generated by processing a plurality of training images to predict at least one characteristic, the training images comprising images of human tissue and/or images that are algorithmically generated, and outputting the target electronic image identifying an area of interest based on the at least one characteristic of the target specimen and/or the at least one characteristic of the target electronic image.