Abstract: The present disclosure is directed, among other things, to automated systems and methods for analyzing, storing, and/or retrieving information associated with biological objects having irregular shapes. In some embodiments, the systems and methods partition an input image into a plurality of sub-regions based on localized colors, textures, and/or intensities in the input image, wherein each sub-region represents biologically meaningful data.

Abstract: Techniques for obtaining a synthetic histochemically stained image from a multiplexed immunofluorescence (MPX) image may include producing an N-channel input image that is based on information from each of M channels of an MPX image of a tissue section, where M and N are positive integers and N is less than or equal to M; and generating a synthetic image by processing the N-channel input image using a generator network, the generator network having been trained using a training data set that includes a plurality of pairs of images. The synthetic image depicts a tissue section stained with at least one histochemical stain. Each pair of images of the plurality of pairs of images includes an N-channel image, produced from an MPX image of a first section of a tissue, and an image of a second section of the tissue stained with the at least one histochemical stain.

Abstract: The present disclosure relates to techniques for obtaining a synthetic immunohistochemistry (IHC) image from a histochemically stained image. Particularly, aspects of the present disclosure are directed to accessing an input image that depicts a tissue section that has been stained with at least one histochemical stain; generating a synthetic image by processing the input image using a trained generator network; and outputting the synthetic image. The synthetic image depicts a tissue section that has been stained with at least one IHC stain that targets a first antigen, and techniques may also include receiving an input that is based on a level of expression of a first antigen from the synthetic image and/or generating, from the synthetic image, a value that is based on a level of expression of the first antigen.

Abstract: Embodiments disclosed herein generally relate to identifying auto-fluorescent artifacts in a multiplexed immunofluorescent image. Particularly, aspects of the present disclosure are directed to accessing a multiplexed immunofluorescent image of a slice of specimen, wherein the multiplexed immunofluorescent image comprises one or more auto-fluorescent artifacts, processing the multiplexed immunofluorescent image using a machine-learning model, wherein an output of the processing corresponds to a prediction that the multiplexed immunofluorescent image includes one or more auto-fluorescent artifacts at one or more particular portions of the multiplexed immunofluorescent image, adjusting subsequent image processing based on the prediction, performing the subsequent image processing, and outputting a result of the subsequent image processing, wherein the result corresponds to a predicted characterization of the specimen.

Abstract: The present disclosure relates to techniques for transforming digital pathology images obtained by different slide scanners into a common format for image analysis. Particularly, aspects of the present disclosure are directed to obtaining a source image of a biological specimen, the source image is generated from a first type of scanner, inputting into a generator model a randomly generated noise vector and a latent feature vector from the source image as input data, generating, by the generator model, a new image based on the input data, inputting into a discriminator model the new image, generating, by the discriminator model, a probability for the new image being authentic or fake, determining whether the new image is authentic or fake based on the generated probability, and outputting the new image when the image is authentic.

Abstract: The present disclosure is directed, among other things, to automated systems and methods for analyzing, storing, and/or retrieving information associated with biological objects having irregular shapes. In some embodiments, the systems and methods partition an input image into a plurality of sub-regions based on localized colors, textures, and/or intensities in the input image, wherein each sub-region represents biologically meaningful data.

Abstract: A multiplex image is accessed that depicts a particular slice of a particular sample stained with two or more dyes. Using a Generator network, a predicted singleplex image is generated that depicts the particular slice of the particular sample stained with each of the expressing biomarkers. The Generator network may have been trained by training a machine-learning model using a set of training multiplex images and a set of training singleplex images. Each of the set of training multiplex images depicted a slice of a sample stained with two or more dyes. Each of the set of training singleplex images depicted a slice of a sample stained with a single dye. The machine-learning model included a Discriminator network configured to discriminate whether a given image was generated by the Generator network or was a singleplex image of a real slide. The method further includes outputs the predicted singleplex image.

Abstract: The present application provides for systems and methods for detecting and estimating signals corresponding to one or more biomarkers in biological samples stained for the presence of protein and/or nucleic acid biomarkers. On particular aspect is directed to a method of estimating an amount of signal corresponding to at least one biomarker in an image of a biological sample. The method includes detecting isolated spots in a first image, deriving an optical density value of a representative isolated spot based on signal features from the detected isolated spots, estimating a number of predictive spots in signal aggregates in each of a plurality of sub-regions based on the derived optical density value of the representative isolated spot, and storing the estimated number of predictive spots and detected isolated spots in each of the plurality of generated sub-regions in a database.

Abstract: The present disclosure is directed, among other things, to automated systems and methods for analyzing, storing, and/or retrieving information associated with biological objects having irregular shapes. In some embodiments, the systems and methods partition an input image into a plurality of sub-regions based on localized colors, textures, and/or intensities in the input image, wherein each sub-region represents biologically meaningful data.

Abstract: The present disclosure relates machine learning techniques for segmenting non-tumor regions in specimen images to support tumor detection and analysis. Particularly, aspects of the present disclosure are directed to accessing one or more images that comprise a non-target region (e.g., a non-tumor region) and a target region (e.g., a tumor region), predicting, by a two-dimensional segmentation model, segmentation maps for the non-target region based on discriminative features encoded from the one or more images, a segmentation mask for the one or more images based on the segmentation maps, applying the segmentation mask to the one or more images to generate non-target region masked images that exclude the non-target region from the one or more images, and classifying, by an image analysis model, a biological material or structure within the target region based on a set of features extracted from the non-target region masked images.

Abstract: The present disclosure is directed, among other things, to automated systems and methods for analyzing, storing, and/or retrieving information associated with biological objects having irregular shapes. In some embodiments, the systems and methods partition an input image into a plurality of sub-regions based on localized colors, textures, and/or intensities in the input image, wherein each sub-region represents biologically meaningful data.

Abstract: The present application provides for systems and methods for detecting and estimating signals corresponding to one or more biomarkers in biological samples stained for the presence of protein and/or nucleic acid biomarkers. On particular aspect is directed to a method of estimating an amount of signal corresponding to at least one biomarker in an image of a biological sample. The method includes detecting isolated spots in a first image, deriving an optical density value of a representative isolated spot based on signal features from the detected isolated spots, estimating a number of predictive spots in signal aggregates in each of a plurality of sub-regions based on the derived optical density value of the representative isolated spot, and storing the estimated number of predictive spots and detected isolated spots in each of the plurality of generated sub-regions in a database.

Abstract: The present disclosure is directed, among other things, to automated systems and methods for analyzing, storing, and/or retrieving information associated with biological objects having irregular shapes. In some embodiments, the systems and methods partition an input image into a plurality of sub-regions based on localized colors, textures, and/or intensities in the input image, wherein each sub-region represents biologically meaningful data.

Abstract: A digital pathology system and associated method and computer program product provide a quantitative analysis of entire tissue slides as well as intuitive, effective, fast, and precise quantification of biomarker expressions across relevant areas of the entire tissue slides. The digital pathology system enables a novel workflow that allows the user to efficiently outline clinically relevant morphology in its entirety, including solid tumor areas. Quantitative analysis results are then efficiently and intuitively provided to the user for all tissue content (i.e., millions of cells) within seconds. This efficiency is made possible by a pre-computation step that computes and stores image analysis results for later retrieval. Visualizing vast amount of data effectively is another component of the system that provides information and confidence to the user about the biomarker expression levels.

Abstract: A digital pathology system and associated method and computer program product provide a quantitative analysis of entire tissue slides as well as intuitive, effective, fast, and precise quantification of biomarker expressions across relevant areas of the entire tissue slides. The digital pathology system enables a novel workflow that allows the user to efficiently outline clinically relevant morphology in its entirety, including solid tumor areas. Quantitative analysis results are then efficiently and intuitively provided to the user for all tissue content (i.e., millions of cells) within seconds. This efficiency is made possible by a pre-computation step that computes and stores image analysis results for later retrieval. Visualizing vast amount of data effectively is another component of the system that provides information and confidence to the user about the biomarker expression levels.