Abstract: Techniques for acquiring focused images of a microscope slide are disclosed. During a calibration phase, a “base” focal plane is determined using non-synthetic and/or synthetic auto-focus techniques. Furthermore, offset planes are determined for color channels (or filter bands) and used to generate an auto-focus model. During subsequent scans, the auto-focus model can be used to quickly estimate the focal plane of interest for each color channel (or filter band) rather than re-employing the non-synthetic and/or synthetic auto-focus techniques.

Abstract: A method for analyzing an image of a tissue section may include obtaining a plurality of image locations, each corresponding to a different one of a plurality of biological structures; obtaining a plurality of locations of a first biomarker in the image; and calculating a distance transform array for at least a portion of the image that includes the plurality of seed locations. The method may include, for each of the plurality of seed locations and based on information from the first distance transform array, detecting whether the first biomarker is expressed at the seed location, and storing, to a data structure associated with the seed location, an indication of whether expression of the first biomarker at the seed location was detected. The method may include detecting, based on the stored indications, co-localization of at least two phenotypes in at least a portion of the tissue section.

Abstract: Techniques for acquiring focused images of a microscope slide are disclosed. During a calibration phase, a “base” focal plane is determined using non-synthetic and/or synthetic auto-focus techniques. Furthermore, offset planes are determined for color channels (or filter bands) and used to generate an auto-focus model. During subsequent scans, the auto-focus model can be used to quickly estimate the focal plane of interest for each color channel (or filter band) rather than re-employing the non-synthetic and/or synthetic auto-focus techniques.

Abstract: Methods and systems can include: accessing a digital pathology image; generating, using a first machine-learning model, a segmented image that identifies at least: a predicted diseased region and a background region in the digital pathology image; detecting depictions of a set of cells in the digital pathology image; generating, using a second machine-learning model, a cell classification for each cell of the set of cells, wherein the cell classification is selected from a set of potential classifications that indicate which, if any, of a set of biomarkers are expressed in the cell; detecting that a subset of the set of cells are within the background region; and updating the cell classification for each cell of at least some cells in the subset to be a background classification that was not included in the set of potential classifications.

Abstract: Methods and systems for predictive measures of anti-EGFR therapy response in wild type RAS/EGFR+ samples, e.g., histochemical staining methods for staining EGFR, AREG, and EREG, digital analysis of stained slides, and scoring algorithms that allow prediction of a response to anti-EGFR therapies. Analysis of the stained slides and scoring algorithms may include but are not limited to: a percent tumor cell positivity, computerized clustering algorithms, area density (e.g., area of tumor positive for one or more markers over total tumor area), average intensity (e.g., computerized methodology measuring average gray scale pixel intensity), average intensity broken down according to membrane, cytoplasmic, or punctate staining patterns), or any other appropriate parameter or combination of parameters. The methods of the present invention allow for resolving spatial expression patterns of the ligands and the receptor to determine what patterns are predictive for response to anti-EGFR therapies.

Abstract: The present disclosure relates to computer-implement techniques for cell localization and classification. Particularly, aspects of the present disclosure are directed to accessing an image for a biological sample, where the image depicts cells comprising a staining pattern of a biomarker; inputting the image into a machine learning model; encoding, by the machine learning model, the image into a feature representation comprising extracted discriminative features; combining, by the machine learning model, feature and spatial information of the cells and the staining pattern of the biomarker through a sequence of up-convolutions and concatenations with the extracted discriminative features from the feature representation; and generating, by the machine learning model, two or more segmentation masks for the biomarker in the image based on the combined feature and spatial information of the cells and the staining pattern of the biomarker.

Abstract: A machine learning model is accessed that is configured to use one or more parameters to process images to generate labels. The machine learning model is executed to transform at least part of each of at least one digital pathology image into a plurality of predicted labels; and generate a confidence metric for each of the plurality of predicted labels. An interface is availed that depicts the at least part of the at least one digital pathology image and that differentially represents predicted labels based on corresponding confidence metrics. In response to availing of the interface, label input is received that confirms, rejects, or replaces at least one of the plurality of predicted labels. The one or more parameters of the machine learning model are updated based on the label input.

Abstract: Techniques for acquiring focused images of a microscope slide are disclosed. During a calibration phase, a “base” focal plane is determined using non-synthetic and/or synthetic auto-focus techniques. Furthermore, offset planes are determined for color channels (or filter bands) and used to generate an auto-focus model. During subsequent scans, the auto-focus model can be used to quickly estimate the focal plane of interest for each color channel (or filter band) rather than re-employing the non-synthetic and/or synthetic auto-focus techniques.

Abstract: Efficient methods for identifying biomarkers are described. The method may include identifying a tumor area. The method may further include identifying a plurality of regions. The method may also include defining, for each region, a bounding area for the region that encompasses the region. The method may include determining, for each region of a first subset of the plurality of regions, that the region is to be ascribed to the tumor, where the bounding area is fully within the tumor area. The method may further include determining, for each region of a second subset of the plurality of regions, whether to ascribe the region to the tumor based on an intersection of the region and the tumor area. The method may also include accessing a metric characterizing a biological observation and generating a result based on the metrics. The result may be used as a biomarker.

Abstract: A method for using a federated learning classifier in digital pathology includes distributing, by a centralized server, a global model to a plurality of client devices. The client devices further train the global model using a plurality images of a specimen and corresponding annotations to generate at least one further trained model. The client devices provide further trained models to the centralized server, which aggregates the further trained models with the global model to generate an updated global model. The updated global model is then distributed to the plurality of client devices.

Abstract: Techniques for acquiring focused images of a microscope slide are disclosed. During a calibration phase, a “base” focal plane is determined using non-synthetic and/or synthetic auto-focus techniques. Furthermore, offset planes are determined for color channels (or filter bands) and used to generate an auto-focus model. During subsequent scans, the auto-focus model can be used to quickly estimate the focal plane of interest for each color channel (or filter band) rather than re-employing the non-synthetic and/or synthetic auto-focus techniques.

Abstract: Techniques relate to object classifications using bootstrapping of region-level annotations. For each of multiple images, regions within the image can be identified. For each region, a region-specific label can be identified, a set of objects within the region can be detected, and an object-specific label can be assigned to each object. The object-specific label can be the same as the region-specific label assigned to the region within which the object is located. A training data set can be defined to include, for each image of the multiple images, object-location data (indicating intra-image location data for the detected object) and label data (indicating the object-specific labels assigned to the objects). An image-processing model can be trained using the training data. Training can include learning values for a set of parameters that define calculations performed by the image-processing model.

Abstract: Systems and methods disclosed herein describe a platform that automatically creates and executes a scoring guide for use in anatomical pathology. The platform can employ a fully-automated workflow for clustering the biological objects of interest and for providing cell-by-cell read-outs of heterogeneous tumor biomarkers based on their stain appearance. The platform can include a module for automatically creating and storing a scoring guide in a training database based on training digital images (240, 250), and an object classification module that executes the scoring guide when presented with new digital images to be scored pursuant to the scoring guide (299).

Abstract: A scoring functions is developed and used for identifying patients who might be responsive to a PD-1 axis directed therapy. The scoring functions are obtained by extracting features from multiplex-stained sections, selecting features that correlate with response to the therapy using a feature selection function, and fitting one or more of the selected features to a plurality of candidate scoring functions. A candidate scoring function showing the desired balance between predictive sensitivity and specificity may then selected for incorporation into a scoring system that includes at least an image analysis system.

Abstract: Systems and methods for unmixing of multichannel image data in the presence of locally varying image characteristics. Feature images created from a multi-channel input image form a feature vector for each pixel in the input image. Feature vectors are classified based on the local image characteristics, and areas are formed in the input image that share local image characteristics. Each area is unmixed separately using reference vectors that were obtained from regions in example images that have the same image characteristics. The unmixing results are combined to form a final unmixing result image created from the individually unmixed image areas.

Abstract: Techniques for acquiring focused images of a microscope slide are disclosed. During a calibration phase, a “base” focal plane is determined using non-synthetic and/or synthetic auto-focus techniques. Furthermore, offset planes are determined for color channels (or filter bands) and used to generate an auto-focus model. During subsequent scans, the auto-focus model can be used to quickly estimate the focal plane of interest for each color channel (or filter band) rather than re-employing the non-synthetic and/or synthetic auto-focus techniques.

Abstract: Techniques for acquiring focused images of a microscope slide are disclosed. During a calibration phase, a “base” focal plane is determined using non-synthetic and/or synthetic auto-focus techniques. Furthermore, offset planes are determined for color channels (or filter bands) and used to generate an auto-focus model. During subsequent scans, the auto-focus model can be used to quickly estimate the focal plane of interest for each color channel (or filter band) rather than re-employing the non-synthetic and/or synthetic auto-focus techniques.

Abstract: A method and associated method and computer program product for acquiring focused images of a specimen on a slide, by determining optimal scanning trajectories. The method includes capturing a relatively low magnification image of the slide to locate the specimen, forming a grid that includes an arrangement of grid points, overlaying at least part of the grid over a field of view that covers at least part of the specimen, capturing a relatively high magnification Z-stack of images of the specimen within the field of view, determining a best focus for each grid point within said at least part of the grid to form a resulting grid of three dimensional points, and based on the resulting grid, determining one or more three dimensional scanning trajectories.

Abstract: A computer-based specimen analyzer (10) is configured to detect a level of expression of genes in a cell sample by detecting dots that represent differently stained genes and chromosomes in a cell. The color of the stained genes and the chromosomes is enhanced and filtered to produce a dot mask that defines areas in the image that are genes, chromosomes, or non-genetic material. Metrics are determined for the dots and/or pixels in the image of the cell in areas corresponding to the dots. The metrics are fed to a classifier that separates genes from chromosomes. The results of the classifier are counted to estimate the expression level of genes in the tissue samples.

Abstract: A computer-based specimen analyzer (10) is configured to detect a level of expression of genes in a cell sample by detecting dots that represent differently stained genes and chromosomes in a cell. The color of the stained genes and the chromosomes is enhanced and filtered to produce a dot mask that defines areas in the image that are genes, chromosomes, or non-genetic material. Metrics are determined for the dots and/or pixels in the image of the cell in areas corresponding to the dots. The metrics are fed to a classifier that separates genes from chromosomes. The results of the classifier are counted to estimate the expression level of genes in the tissue samples.