In The journal of pathology. Clinical research
Image-based biomarker discovery typically requires accurate segmentation of histologic structures (e.g. cell nuclei, tubules, and epithelial regions) in digital pathology whole slide images (WSIs). Unfortunately, annotating each structure of interest is laborious and often intractable even in moderately sized cohorts. Here, we present an open-source tool, Quick Annotator (QA), designed to improve annotation efficiency of histologic structures by orders of magnitude. While the user annotates regions of interest (ROIs) via an intuitive web interface, a deep learning (DL) model is concurrently optimized using these annotations and applied to the ROI. The user iteratively reviews DL results to either (1) accept accurately annotated regions or (2) correct erroneously segmented structures to improve subsequent model suggestions, before transitioning to other ROIs. We demonstrate the effectiveness of QA over comparable manual efforts via three use cases. These include annotating (1) 337,386 nuclei in 5 pancreatic WSIs, (2) 5,692 tubules in 10 colorectal WSIs, and (3) 14,187 regions of epithelium in 10 breast WSIs. Efficiency gains in terms of annotations per second of 102×, 9×, and 39× were, respectively, witnessed while retaining f-scores >0.95, suggesting that QA may be a valuable tool for efficiently fully annotating WSIs employed in downstream biomarker studies.
Miao Runtian, Toth Robert, Zhou Yu, Madabhushi Anant, Janowczyk Andrew
active learning, annotations, computational pathology, deep learning, digital pathology, efficiency, epithelium, nuclei, open-source tool, tubules