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The application of deep learning to pathology assumes the existence ofdigital whole slide images of pathology slides. However, slide digitization isbottlenecked by the high cost of precise motor stages in slide scanners thatare needed for position information used for slide stitching. We proposeGloFlow, a two-stage method for creating a whole slide image using opticalflow-based image registration with global alignment using a computationallytractable graph-pruning approach. In the first stage, we train an optical flowpredictor to predict pairwise translations between successive video frames toapproximate a stitch. In the second stage, this approximate stitch is used tocreate a neighborhood graph to produce a corrected stitch. On a simulateddataset of video scans of WSIs, we find that our method outperforms knownapproaches to slide-stitching, and stitches WSIs resembling those produced byslide scanners.

GloFlow: Global Image Alignment for Creation of Whole Slide Images for Pathology from Video