Method for detecting spatial coupling comprising the steps of: a. providing a set of data, b. identifying and segmenting a first and a second sets of objects of interest, wherein the objects of the second set are assimilated to punctual objects, c. determining, using a level set function, an expected number of objects of the second set present within a specified range of distances to at least one given object of the first set in case there were no interactions between said at least one given object of the first set and the objects of the second set, d. determining, using a level set function, an actual number of objects of the second set within the same range of distances to the at least one given object of the first set, and e. comparing said expected amount and said determined amount.

Systems and methods for machine learning tissue classification are provided herein. In one embodiment, a system includes a storage element operable to store datasets of a plurality of biological samples. The dataset of each biological sample includes image data of the biological sample and molecular measurement data of the biological sample captured at a plurality of capture areas of the biological sample. The capture areas of the biological sample are registered to corresponding locations in the image data of the biological sample. A processor is operable to train a machine learning model with the stored datasets to learn molecular measurements of the biological samples. The processor may then process an image from another biological sample through the trained machine learning module to predict molecular measurement data of the other biological sample.

Systems and methods are provided for automatically imaging and analyzing cell samples in an incubator. An actuated microscope operates to generate images of samples within wells of a sample container across days, weeks, or months. A plurality of images is generated for each scan of a particular well, and the images within such a scan are used to image and analysis metabolically active cells in the well. Tins analysis includes generating a “range image” by subtracting the minimum intensity value, across the scan, for each pixel from the maximum intensity value. This range image thus emphasizes cells or portions of cells that exhibit changes in activity over a scan period (e.g., neurons, myocytes, cardiomyocytes) while de-emphasizing regions that exhibit consistently high intensities when images (e.g., regions exhibiting a great deal of autofluorescence unrelated to cell activity).

The present disclosure relates to an apparatus including one or more processors configured to receive a digitized image of a region of tissue demonstrating a disease, and containing cellular structures represented in the digitized image, each of the cellular structures being associated with a cell category of a plurality of cell categories; select a cellular structure of the cellular structures based on the cell category for the cellular structure; for the cellular structure selected, compute a set of contextual features; assign, based on the set of contextual features, the cellular structure to at least one cluster of a plurality of clusters; compute cluster features, the cluster features describing characteristics of the at least one cluster of the plurality of clusters; and generate a prediction that describes a pathologic or phenotypic state of the disease based, at least in part, on the cluster features and/or the set of contextual features.

An image diagnosis method comprises a step of acquiring an image including at least one of a tissue and a cell as an element, a step of classifying, for each partial image that is a part of the image, a property of the element included in the partial image, and a step of sorting the image into any one of benign indicating that no lesion element is present, malignant indicating that a lesion element is present, and follow-up based on classification results of the plurality of partial images.

Systems and methods are provided for classifying microbial cells according to morphological features of microcolonies. A dark-field objective is employed to acquire a dark-field image of a microcolony during a microcolony growth phase that is characterized by phenotypic expression of microcolony morphological features which evolve with time and are differentiated among classes of microbial cell types. The dark-field image is processed to classify the microcolony according to two or more microbial cell types, such as Gram status and/or speciation. The dark-field objective may have a numerical aperture selected to facilitate the imaging of microcolony morphological features, residing, for example, between 0.15 and 0.35. A set of dark-field images of a microcolony may be collected during the microcolony growth phase and processed to classify the microcolony. Classification may be performed according to a temporal ordering of the dark-field images, for example, using a recurrent neural network.

Systems and methods are provided for medical image classification of images from varying sources. A set of microscopic medical images are acquired, and a first neural network module configured to reduce each of the set of microscopic medical images to a feature representation is generated. The first neural network module, a second neural network module, and a third neural network module are trained on at least a subset of the set of microscopic medical images. The second neural network module is trained to receive feature representation associated with an image of the microscopic images and classify the image into one of a first plurality of output classes. The third neural network module is trained to receive the feature representation, classify the image into one of a second plurality of output classes based on the feature representation, and provide feedback to the first neural network module.

The first aspect of the present invention provides a migration ability evaluation method comprising: a trajectory generation step of generating a trajectory of movement of an object being observed of a living body on the basis of a plurality of images of the object being observed acquired by capturing observation images of the object being observed multiple times in a time-series manner; a migration ability calculation step of calculating a migration ability measure indicating the degree of migration of the object being observed in a certain direction on the basis of the trajectory of movement of the object being observed; and a migration ability evaluation step of evaluating whether or not the object being observed satisfies a predetermined condition on the basis of the migration ability measure of the object being observed.

This application provides an artificial intelligence-based pathological image processing method performed by an electronic device. The method includes: determining a seed pixel of an immune cell region from a pathological image; obtaining a seed pixel mask image corresponding to the seed pixel of the immune cell region from the pathological image based on the seed pixel of the immune cell region; segmenting an epithelial cell region in the pathological image, to obtain an epithelial cell mask image of the pathological image; fusing the seed pixel mask image and the epithelial cell mask image of the pathological image, to obtain an effective seed pixel mask image corresponding to the immune cell region in the pathological image; and determining a ratio value of the immune cell region in the pathological image based on the effective seed pixel mask image.

The disclosed feature makes it possible to accurately determine a change that has occurred in a tissue. The feature includes: a binarizing section (41) that generates, from an image to be analyzed, a plurality of binarized images having respective binarization reference values different from each other; a Betti number calculating section (42) that calculates, for each of the plurality of binarized images, a one-dimensional Betti number indicating the number of hole-shaped regions each of which is surrounded by pixels each having a first pixel value obtained by binarization and is constituted by pixels each having a second pixel value obtained by binarization; and a determining section (44) that determines a change that has occurred in the tissue, based on a binarization reference value and a one-dimensional Betti number in a binarized image in which the one-dimensional Betti number is maximized.