Abstract: A method for refining virtually-stained image annotations. The method includes separating an image of a stained tissue section into an image of a counterstain and an image of immunostaining visual signals via linear transformation. The method includes identifying and segmenting nuclei in the image of the counterstain to produce an image of a refined counterstain with single-cell segments. The method includes comparing immunostaining visual signals in the single-cell segments of the image of the refined counterstain to a user-defined threshold to determine true positive cells. The method includes refining the image of the immunostaining visual signals to produce an image of refined immunostaining visual signals. The refining is based on the stained tissue section protein localization or morphology and the determined true positive cells. The method includes overlaying the image of the counterstain and the image of refined immunostaining visual signals to produce a refined virtually-stained image annotation.

Abstract: A method of generating virtually-stained image annotations. The method includes providing a neural network executed by image processing software. The image processing software runs on a processor of a computing device. The method includes training the neural network with a plurality of chemically stained patterns of endogenous signals to identify virtual staining patterns. The method includes producing and annotating an image of a biological sample for a biomarker. The biological sample includes endogenous signals. The method includes identifying, using the trained neural network, the virtual staining patterns in the image of the biological sample. Lastly, the method includes overlaying the virtual staining patterns in the image of the biological sample with annotations using spatial matching to produce virtually-stained image annotations.