A method is provided for non-invasively predicting characteristics of one or more cells and cell derivatives. The method includes training a machine learning model using at least one of a plurality of training cell images representing a plurality of cells and data identifying characteristics for the plurality of cells. The method further includes receiving at least one test cell image representing at least one test cell being evaluated, the at least one test cell image being acquired non-invasively and based on absorbance as an absolute measure of light, and providing the at least one test cell image to the trained machine learning model. Using machine learning based on the trained machine learning model, characteristics of the at least one test cell are predicted. The method further includes generating, by the trained machine learning model, release criteria for clinical preparations of cells based on the predicted characteristics of the at least one test cell.

A method, device, and computer program product are designed for non-convolutional image processing in microscopy of an input image into an output image using an artificial neural network with at least one contracting path including layers, at least one expanding path including layers, and at least one filter kernel. The method includes determining, in one or multiple artificial neural network layers, a similarity metric between at least one filter kernel and one output of the previous layer. Additionally, in at least one layer of the contracting path, the resolution of the output of the previous layer is reduced, and, in at least one layer of the expanding path, the resolution of the output of the previous layer is increased. The first artificial neural network layer treats the input image as the output of the previous layer, and the output of the last artificial neural network layer is the output image.

Methods, systems, and apparatuses, including computer programs for detecting pancreatic neoplasms. A method includes providing an image as an input to a first model, obtaining first output data generated by the first model based on the first model's processing of the image, the first output data representing a portion of the image that depicts a pancreas, providing the first output data as an input to a second model, obtaining second output data generated by the second model based on the second model's processing of the second input data, the second output indicating whether the depicted pancreas is normal or abnormal, providing the first output data and the second output data as an input to a third model, and obtaining third output data generated by the third model, the third output data including data indicating that the pancreas is normal or data indicating a likely location of a pancreatic neoplasm.

A method of analyzing a specimen includes detecting a specimen integrity error in the specimen; capturing an image of the specimen; sending the image of the specimen to a customer support center at a remote location; analyzing the image of the specimen at the customer support center; and determining a cause of the specimen integrity error in response to analyzing the image of the specimen. Diagnostic analyzers and diagnostic systems are also disclosed.

The invention relates to a method and to an apparatus for analyzing an image of a microlithographic microstructured component wherein in the image each of a multiplicity of pixels is assigned in each case an intensity value. A method according to the invention comprises the following steps: isolating a plurality of edge fragments in the image;

classifying each of the isolated edge fragments either as a relevant edge fragment or as an irrelevant edge fragment; and ascertaining contiguous segments in the image based on the relevant edge fragments.

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.

The present invention relates to a method for subjecting a plurality of microwells containing cells to a high-content assay, said method comprising: a) Acquiring at least one image of said plurality of microwells; b) In said image, detecting a plurality of areas of interest, each area of interest corresponding to a single cell; c) Measuring at least one derived property, and, optionally, at least one direct property of said areas of interest, where said one or more properties is a selection property; d) Selecting a subset of said plurality of microwells, where said microwells belonging to the subset contain areas of interest selected based on said at least one selection property; e) Extrapolating an output parameter from a property measured in the set of areas of interest selected, where said property is defined as output property, said output property being distinct from said selection properties where said output parameter is the processing of an output property measured in said set of areas of interest. In a further aspect, there are claimed a system for subjecting a plurality of microwells containing cells to a high-content assay and a computer program which comprises instructions for subjecting a plurality of microwells containing cells to a high-content assay.

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.