A method for detecting cells in images using an autoencoder, a computer device, and a storage medium extracts a first feature vector from each of a plurality of sample medical images. The first feature vector is inputted into an autoencoder, and a first latent feature of each of the plurality of sample medical images is extracted. A first predicted value of a number of cells in each of the plurality of sample medical images is generated based on the first latent feature. The first latent feature is inputted into the autoencoder, and a plurality of reconstructed images are obtained. The autoencoder is optimized based on the plurality of reconstructed images and the first predicted value. This method can be run in the computer device to improve efficiency of detection from images.

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.

A urine analysis system according to an embodiment includes: a testing apparatus that measures particles included in a urine sample according to a flow cytometry method; an image capturing apparatus that captures images of particles in the urine sample to acquire particle images; and a management apparatus that receives a measurement result obtained by the testing apparatus and the particle images acquired by the image capturing apparatus. The management apparatus generates an order to capture an image of the urine sample based on the measurement result obtained by the testing apparatus. The image capturing apparatus executes the image capturing processing of the particles in the urine sample for which the image capturing order has been generated by the management apparatus, and transmits the acquired particle images to the management apparatus.

Optical biopsy staining panels for in vivo or in situ fluorescent staining of optical tissue (or other appropriate tissue), e.g., for the purpose of a direct biopsy such as an optical biopsy. The stain panels may feature a combination of a nuclear stain and a cytoplasmic stain, as a means of functioning as an invivo or in situ hematoxylin and eosin (H&E) stain. Examples of said stains may include anthracyclines such as Daunomcibin, acriflavines like Proflavine, anthracenediones such as Mitoxantrone, phenothiazines like Methylene Blue, and tri- and tetra-heterocyclic dyes like Fluorescein, Phloxine B, Phenol Red, Rose Bengal, Congo Red, and Indigo Carmine.

Methods and systems for determining interaction between cells are described wherein the method includes determining or receiving a sequence of images representing manipulating first cells, in a holding space, the holding space including a functionalized wall comprising second cells, the manipulating including settling of the first cells onto the functionalized wall and applying a force on the settled first cells; detecting groups of pixels representing first cells in first images representing the settling of the first cells onto the functionalized wall; tracking locations of detected first cells in the first images; and, determining settling events, a settling event being determined if a cell in a first image is not distinguishable from background of the first image, the location in the image at which a cell settling event is detected defining a cell settling location; detecting groups of pixels representing cells in second images captured during the application of the force and tracking locations of detected cells, wherein tracked locations of a detected cell in the second images form a tracking path, the first location of the tracking path defining a pop-up event, the location in a second image at which a pop-up event is detected defining a pop-up location; and, determining detachment events based on the settling locations and based on the pop-up locations, a detachment event defining a first cell being detached from a second cell due to application of the force on the first cell, and determining information about the interaction between first and second cells based on the force applied to the first cells.

Systems and methods can quantify the tumor microenvironment for diagnosis, prognosis and therapeutic response prediction by fusing different data types (e.g., morphological information from histology and molecular information from omics) using an algorithm that harnesses deep learning. The algorithm employs tensor fusion to provide end-to-end multimodal fusion to model the pairwise interactions of features across multiple modalities (e.g., histology and molecular features) and deep learning. The systems and methods improve upon traditional methods for quantifying the tumor microenvironment that rely on concatenation of extracted features.

A deep-ultraviolet microscopy system includes a light source for outputting a light beam for illuminating a biological sample, the light beam being inclusive of ultraviolet wavelengths; a reception space for reception of a biological sample for illumination by the light beam; an ultraviolet microscope objective for collecting and relaying light that interacts with the biological sample to an image capture device; and an ultraviolet sensitive image capture device for capturing images of the biological sample, with the microscopy system configured to capture multiple images of the biological sample at one or more ultraviolet wavelengths. A method of processing ultraviolet images of biological samples includes receiving a plurality of multi-spectral ultraviolet images of a biological sample; normalizing and scaling the images; and assigning each image to a channel in the RGB color-space based on wavelength.

The present disclosure relates to a non-transitory computer-readable medium storing computer-executable instructions that, when executed, cause a processor to perform operations. The operations include extracting one or more image characterization metrics from respective ones of a plurality of digitized images within an imaging data set. The plurality of digitized images have batch effects. The operations further include identifying a plurality of batch effect groups of the digitized images using the one or more image characterization metrics, and dividing the plurality of batch effect groups between a training set and/or a validation set. The training set and/or the validation set include some of the plurality of digitized images associated with respective ones of the plurality of batch effect groups.

An image acquire device comprising: an irradiator configured to irradiate an undyed tissue with excitation light; an image sensor configured to acquire a third harmonic image of the undyed tissue based on light generated in third harmonic generation caused by interaction between the undyed tissue and the excitation light.

A method for categorizing biological structure of interest (BSOI) in digitized images of biological tissues comprises a stage of identifying BSOIs in digitized images and further comprises presenting an image from the plurality of images that comprises at least one BSOI with high level of entropy to a user, receiving from the user input indicative of a category to be associated with the BSOI that had the high level of entropy and updating the cell categories classifier according to the category of the BSOI provided by the user.