Multi-sample whole slide image processing in digital pathology via multi-resolution registration and machine learning

摘要

When reviewing digital pathology tissue specimens, multiple slides may be created from thin, sequential slices of tissue. These slices may then be prepared with various stains and digitized to generate a Whole Slide Image (WSI). Review of multiple WSIs is challenging because of the lack of homogeneity across the images. In embodiments, to facilitate review, WSIs are aligned with a multi-resolution registration algorithm, normalized for improved processing, annotated by an expert user, and divided into image patches. The image patches may be used to train a Machine Learning model to identify features useful for detection and classification of regions of interest (ROIs) in images. The trained model may be applied to other images to detect and classify ROIs in the other images, which can aid in navigating the WSIs. When the resulting ROIs are presented to the user, the user may easily navigate and provide feedback through a display layer.