A system for analyzing biological specimens by spectral imaging includes a biosensor comprising at least one graphene layer on a substrate and a memory in communication with a processor. The biosensor is configured to acquire a biological specimen sample. The memory and the processor are configured to conduct Raman spectroscopy to obtain spectral data for the sample, transmit the spectral data to a hub for direct or indirect transmission to one or more servers, perform multivariate analysis on the spectral data, and deliver a report based on the multivariate analysis of the spectral data.

Methods and apparatuses for athletic performance and technique monitoring are disclosed. In one example, a sensor output is received associated with a movement of a user torso during a running motion. The sensor output is analyzed to identify an undesirable torso motion.

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.

Embodiments of the invention relate to a computer-implemented method for quantifying a biomarker in a tissue sample of an organism. An image analysis system receives images of a stained tissue sample. Each received digital image depicts the tissue sample region at the end of an exposure interval. The system analyzes the intensity values and exposure intervals of the received digital images for determining the time when the intensity values corresponding to the plurality of exposure intervals ordered according to ascending exposure interval lengths reach a plateau (saturation residence time—SRT). The system determines the amount of a biomarker in the tissue sample and/or predicts a tumor stage and/or a treatment recommendation as a function of the SRT.

Some embodiments are directed to a system and computer-implemented method are provided for determining one or more regions for tissue dissection in a series of pathology slides using a series of images which represent a digitized version of the series of pathology slides. Annotations are obtained for at least two reference images, with each annotation representing a region for tissue dissection in the respective reference image. Annotations are then generated for intermediate images between the reference images on the basis of bidirectional image registration and the subsequent propagation of both annotations to each intermediate image, which annotations are then combined to obtain a combined annotation for each intermediate image. The above measures are well suited to generate annotations for series of pathology slides which contain tissue slices of a tissue of interest having a complex 3D shape.

A method and system are provided for illuminating and imaging a biological sample using a brightfield microscope for the purpose of counting biological cells. The method comprises positioning a sample to be viewed by way of an objective lens of the microscope, the sample comprising a plurality of biological cells; capturing and storing, using an image capturing apparatus, one or more focal image stacks; processing the one or more focal image stacks using a cell localisation neural network, the cell localisation neural network outputting a list of one or more cell locations; determining, using the list of cell locations, one or more cell focal image stacks, each cell focal image stack being obtained from the one or more focal image stacks; processing the one or more cell focal image stacks using an encoder neural network; determining, using the list of cell locations and the list of cell fingerprints, a number of cells within the sample. The present disclosure aims to provide a quick, non-invasive and reliable mode of counting biological cells.

Exemplary embodiments provide methods, mediums, and systems for processing multiplexed image data from a fluorescence in-situ hybridization (FISH) experiment. According to exemplary embodiments, a convolutional neural network (CNN) may be applied to the image data to localize and identify hybridization spots in images corresponding to different sets of targeting probes. The CNN is configured in such a way that it is able to discriminate hybridization spots in situations that are difficult for conventional techniques. The CNN may be trained on a relatively small amount of data by exploiting the nature of the FISH codebook.

Techniques for performing diagnostic assessments based on digital pathology data are disclosed. In one particular embodiment, the techniques may be realized as a method for performing a diagnostic assessment based on digital pathology data comprising obtaining first digital pathology data comprising intensity information, the first digital pathology data being associated with a plurality of regions of interest in a biological sample; applying first machine learning models to the first digital pathology data, the first machine learning models identifying first regions of interest among the plurality of regions of interest based on the intensity information; applying second machine learning models to the first digital pathology data, the second machine learning models identifying at least one pattern associated with at least one of the first regions of interest; generating a diagnostic assessment based on the first regions of interest and the at least one pattern.

A microscope system includes an eyepiece, an objective, a tube lens that is disposed between the eyepiece and the objective, a projection apparatus that projects a projection image onto an image plane on which an optical image is formed by the tube lens, and a processor that performs processes. The processes include performing for digital image data of the sample at least one analysis process selected from a plurality of analysis processes, and generating projection image data representing the projection image on the basis of the analysis result and the at least one analysis process. The projection image data indicates the analysis result in a display format including an image color corresponding to the at least one analysis process. The generating the projection image data includes determining a color for the projection image in accordance with the at least one analysis process selected from the plurality of analysis processes.

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.