Multiplexed fluorescent imaging which is essential for finding out how various biomolecules are spatially distributed in cells or tissues is disclosed. The present disclosure may obtain 10 or more different biomolecule images with one labeling and imaging by newly designing selection of fluorophores, detection spectral ranges, and signal unmixing algorithm. The present disclosure is a blind unmixing technology for unmixing an image without an emission spectrum of fluorophore, and in this technology, 4 pairs of fluorophores are used, and each pair consists of two fluorophores in which emission spectra are overlapped. Each pair of fluorophores is strongly excited by only one excitation laser. Two images with different detection spectral ranges are obtained for each pair, and two images are unmixed via mutual information minimization without fluorophore emission spectrum information. Two images also may be unmixed via Gram-Schmidt orthogonalization and fluorescence measurement based unmixing. This signal unmixing is repeated for each pair of fluorophores. Furthermore, a total of 10 or more fluorophores may be simultaneously used by adding two large stoke's shift fluorophores emitting light in wavelength ranges that does not overlap with the emission spectra of the above 8 fluorophores.

The usability in annotating an image of a subject derived from a living body is improved. An image analysis method is implemented by one or more computers and includes: displaying a first image that is an image of a subject derived from a living body; acquiring information regarding a first region based on a first annotation added to the first image by a user (S101); specifying a similar region similar to the first region from a region different from the first region in the first image, or a second image obtained by image capture of a region including at least a part of a region of the subject subjected to capture of the first image, based on the information regarding the first region (S102, S103); and displaying a second annotation in a second region corresponding to the similar region in the first image (S104).

An artificial intelligence system is trained and used to detect regions of interest on mechanically-enhanced or otherwise mechanically-altered tissue. An internal imaging device (e.g., endoscope) with a mechanical enhancement element alters tissue from its natural state or orientation such that regions of interest on the tissue may be more clearly distinguished from the surrounding tissue. Images of such mechanically-altered tissue are used to train an artificial intelligence system to detect regions of interest with greater accuracy.

A method is proposed for generating a series of ultra-thin sections of a microscopic sample (10), wherein the sections (11) are detached from the sample (10) using an ultramicrotome (100) and wherein the sections (11), which are detached from the sample (10) are caused to float on a liquid surface and are thereafter transferred onto a solid carrier element (20). For at least for some of the sections (11) detached from the sample (10) a position and an orientation on the solid carrier element (20) are determined by monitoring the placement of these sections (11) onto the solid carrier element (20) using a monitoring system (400) comprising a camera (410), obtaining monitoring data. A method (2000) for the three-dimensional reconstruction of a microscopic sample (10), a microtome system and a computer program are also part of the present invention.

A cell culture evaluation device includes at least one processor. The processor is configured to acquire a cell image obtained by imaging a cell that is being cultured, to input the cell image to an image machine learning model and output an image feature amount set composed of a plurality of types of image feature amounts related to the cell image from the image machine learning model; and to input the image feature amount set to a data machine learning model and output an expression level set composed of expression levels of a plurality of types of ribonucleic acids of the cell from the data machine learning model.

Described herein are systems, methods, and programming for analyzing and classifying digital pathology images. Some embodiments include receiving whole slide images (WSIs) and dividing each of the WSIs into tiles. For each WSI, a random subset of the tiles may be selected and augmented views of each of the selected tiles may be generated. For each of the selected tiles, a first convolutional neural network (CNN) may be trained to: generate, using a first one of the augmented views corresponding to the selected tile, a first representation of the selected tile, and predict a second representation of the selected tile to be generated by a second CNN, wherein the second representation is generated based on a second one of the augmented views of the selected tile.

Systems and methods are disclosed for receiving a digital image corresponding to a target specimen associated with a pathology category, determining a quality control (QC) machine learning model to predict a quality designation based on one or more artifacts, providing the digital image as an input to the QC machine learning model, receiving the quality designation for the digital image as an output from the machine learning model, and outputting the quality designation of the digital image. A quality assurance (QA) machine learning model may predict a disease designation based on one or more biomarkers. The digital image may be provided to the QA model which may output a disease designation. An external designation may be compared to the disease designation and a comparison result may be output.

A method for matching a three-dimensional first image of at least one discrete entity with a three-dimensional second image of the at least one discrete entity is provided. The at least one discrete entity includes a biological sample and a plurality of constituent parts of a marker. The method includes: generating a first representation of the marker from the first image; generating a second representation of the marker from the second image; and based upon the representations matching, matching the first image with the second image; or based upon the representations not matching, rejecting the match. Generating the representations includes determining vectors from at least one reference item to at least some of the constituent parts of the marker, determining for the vectors at least one value of a property, and generating the representations of the marker based on a frequency of the at least one value of the property.

A computer-implemented method for processing electronic medical images, the method including receiving a plurality of electronic medical images of a medical specimen. Each of the plurality of electronic medical images may be divided into a plurality of tiles. A plurality of sets of matching tiles may be determined, the tiles within each set corresponding to a given region of a plurality of regions of the medical specimen. For each tile of the plurality of sets of matching tiles, a blur score may be determined corresponding to a level of image blur of the tile. For each set of matching tiles, a tile may be determined with the blur score indicating the lowest level of blur. A composite electronic medical image, comprising a plurality of tiles from each set of matching tiles with the blur score indicating the lowest level of blur, may be determined and provided for display.

The technology disclosed present systems and methods to produce an enhanced resolution image from images of a target using structured illumination microscopy (SIM). The method includes transforming at least three images of the target captured by a sensor in a spatial domain into a Fourier domain to produce at least three frequency domain matrices that each include first blocks of complex coefficients and redundant second blocks of complex coefficients that are conjugates to the first blocks. The method includes reducing computing resources required to produce the enhanced resolution image by using first blocks of complex coefficients to produce at least three phase-separated half-matrices in the Fourier domain. The method includes performing one or more intermediate transformation on the phase-separated half-matrices to produce realigned shifted half-matrices. The method includes calculating complex coefficients of second blocks in the Fourier domain to produce full matrices from half-matrices.