Abstract: In a method of operation of an artificial intelligence-equipped specimen scanning and analysis unit to digitally scan and analyze pathological specimen slides, a sample slide with a mounted tissue sample is scanned and analyzed according to one or more user-selected algorithms in order to generate a heatmap visually depicting the presence of one or more user-selected sample attributes of the tissue sample. One or more artificial intelligence modules, including a deep learning computation module, is provided and can be trained by the user for future analysis of new samples. One or more regions of interest may be selected from the heatmap to include in the results of the analysis. A focus window may be used to closely inspect any given region of the whole slide image, and a trail map is generated from the movement of the focus window.

Abstract: In a method of operation of an artificial intelligence-equipped specimen scanning and analysis unit to digitally scan and analyze pathological specimen slides, a sample slide with a mounted tissue sample is scanned and analyzed according to one or more user-selected algorithms in order to generate a heatmap visually depicting the presence of one or more user-selected sample attributes of the tissue sample. One or more artificial intelligence modules, including a deep learning computation module, is provided and can be trained by the user for future analysis of new samples. One or more regions of interest may be selected from the heatmap to include in the results of the analysis. A focus window may be used to closely inspect any given region of the whole slide image, and a trail map is generated from the movement of the focus window.

Abstract: In an automated method of determining potential efficacy of immunotherapy approaches, an automated slide scanning system with an image acquisition unit is provided. A sample slide with a mounted tissue sample is received by the automated slide scanning system. One or more slide images of the tissue sample are acquired with the image acquisition unit. At least one cell segmentation process is executed on the slide image in order to identify tissue cells. The cells are analyzed with at least one cell classification algorithm in order to identify a tumor cell percent positivity value and an immune cell percent positivity value, and a treatment efficacy score is subsequently calculated. Further features provided include pixel to pixel image registration, reconstruction, composite multi-layer viewing, and other image management tools.

Abstract: A tissue area detection method in digital pathology imaging uses an automated method to detect tissue area of interest (AOI detection) on a Whole Slide Analysis (WSA) or Tissue Micro Array (TMA) thumbnail image. The present method may use preprocessing of the image followed by a two-pass segmentation technique for separating tissue areas from non-tissue areas. The present method may further use global and local window statistics for thresholding to overcome variations in staining intensity that may hamper accurate selection of the area of interest. The present method may also have a classifying process of the tissue area based on the staining method used and applying stain specific filters to remove unwanted artifacts.

Abstract: In an automated method of determining potential efficacy of immunotherapy approaches, an automated slide scanning system with an image acquisition unit is provided. A sample slide with a mounted tissue sample is received by the automated slide scanning system. One or more slide images of the tissue sample are acquired with the image acquisition unit. At least one cell segmentation process is executed on the slide image in order to identify tissue cells. The cells are analyzed with at least one cell classification algorithm in order to identify a tumor cell percent positivity value and an immune cell percent positivity value, and a treatment efficacy score is subsequently calculated. Further features provided include pixel to pixel image registration, reconstruction, composite multi-layer viewing, and other image management tools.

Abstract: A tissue area detection method in digital pathology imaging uses an automated method to detect tissue area of interest (AOI detection) on a Whole Slide Analysis (WSA) or Tissue Micro Array (TMA) thumbnail image. The present method may use preprocessing of the image followed by a two-pass segmentation technique for separating tissue areas from non-tissue areas. The present method may further use global and local window statistics for thresholding to overcome variations in staining intensity that may hamper accurate selection of the area of interest. The present method may also have a classifying process of the tissue area based on the staining method used and applying stain specific filters to remove unwanted artifacts.

Abstract: A method and system for automated detection of immunohistochemical (IHC) patterns. The method and system is able to automatically differentiate an epithelial cell part from a non-epithelial cell part of a digital image of a tissue sample to which an IHC compound has been applied. The method and system help to automatically correct errors made in a manual interpretation of a pathological or other manual analysis of tissue samples for diseases such as a human cancer.

Abstract: A method and system for automated detection of immunohistochemical (IHC) patterns. The method and system is able to automatically differentiate an epithelial cell part from a non-epithelial cell part of a digital image of a tissue sample to which an IHC compound has been applied. The method and system help to automatically correct errors made in a manual interpretation of a pathological or other manual analysis of tissue samples for diseases such as a human cancer.

Abstract: A method and system for automated detection of immunohistochemical (IHC) patterns. The method and system is able to automatically differentiate an epithelial cell part from a non-epithelial cell part of a digital image of a tissue sample to which an IHC compound has been applied. The method and system help to automatically correct errors made in a manual interpretation of a pathological or other manual analysis of tissue samples for diseases such as a human cancer.

Abstract: A method and system for automated detection of immunohistochemical (IHC) patterns. The method and system is able to automatically differentiate an epithelial cell part from a non-epithelial cell part of a digital image of a tissue sample to which an IHC compound has been applied. The method and system help to automatically correct errors made in a manual interpretation of a pathological or other manual analysis of tissue samples for diseases such as a human cancer.