Abstract: Methods and systems are provided for processing different-modality digital images of tissue. The method includes, for each image, detecting biological entities in the image and generating an entity graph comprising entity nodes, representing respective biological entities, interconnected by edges representing interactions between entities represented by the entity nodes. The method also includes selecting, from each image, anchor elements comprising elements corresponding to anchor elements of at least one other image, and generating an anchor graph in which anchor nodes, representing respective anchor elements, are interconnected with entity nodes of the entity graph for the image by edges indicating relations between entity nodes and anchor nodes.

Abstract: Computer-implemented methods and systems are provided for interpreting a whole-slide image of a tissue specimen. Such a method includes detecting biological entities in a region of interest independently at each magnification level in a whole-slide image. An entity graph is generated for each magnification level, the entity graph comprising nodes, representing respective biological entities detected at that magnification level, interconnected by edges representing interactions between entities. The method also includes, for each region of interest, generating a hierarchical graph, defining a hierarchy of the entity graphs for that region, in which nodes of different entity graphs are interconnected by hierarchical edges representing hierarchical relations between nodes of the entity graphs.

Abstract: Methods and systems are provided for processing different-modality digital images of tissue. The method includes, for each image, detecting biological entities in the image and generating an entity graph comprising entity nodes, representing respective biological entities, interconnected by edges representing interactions between entities represented by the entity nodes. The method also includes selecting, from each image, anchor elements comprising elements corresponding to anchor elements of at least one other image, and generating an anchor graph in which anchor nodes, representing respective anchor elements, are interconnected with entity nodes of the entity graph for the image by edges indicating relations between entity nodes and anchor nodes.

Abstract: 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.

Abstract: Computer-implemented methods and systems are provided for interpreting a whole-slide image of a tissue specimen. Such a method includes detecting biological entities in a region of interest independently at each magnification level in a whole-slide image. An entity graph is generated for each magnification level, the entity graph comprising nodes, representing respective biological entities detected at that magnification level, interconnected by edges representing interactions between entities. The method also includes, for each region of interest, generating a hierarchical graph, defining a hierarchy of the entity graphs for that region, in which nodes of different entity graphs are interconnected by hierarchical edges representing hierarchical relations between nodes of the entity graphs.

Abstract: The invention relates to the automated determination of the staining quality of an IHC stained biological sample. A plurality of features is extracted from a digital IHC stained tissue image. The features are input into a first classifier configured to identify the extended tissue type of the depicted tissue as a function of the extracted features. An extended tissue type is a tissue type with a defined expression level of the tumor marker. In addition, the extracted features are input into a second classifier configured to identify a contrast level of the depicted tissue as a function of at least some second ones of the extracted features. The contrast level indicates the intensity contrast of pixels of the stained tissue. Then, a staining quality score of the image is computed as a function of the identified extended tissue type and the identified contrast level.

Abstract: 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.

Abstract: The invention relates to the automated determination of the staining quality of an IHC stained biological sample. A plurality of features is extracted from a digital IHC stained tissue image. The features are input into a first classifier configured to identify the extended tissue type of the depicted tissue as a function of the extracted features. An extended tissue type is a tissue type with a defined expression level of the tumor marker. In addition, the extracted features are input into a second classifier configured to identify a contrast level of the depicted tissue as a function of at least some second ones of the extracted features. The contrast level indicates the intensity contrast of pixels of the stained tissue. Then, a staining quality score of the image is computed as a function of the identified extended tissue type and the identified contrast level.