GloFlow: Whole Slide Image Stitching from Video Using Optical Flow and Global Image Alignment

The application of deep learning to pathology assumes the existence of digital whole slide images of pathology slides. However, slide digitization is bottlenecked by the high cost of precise motor stages in slide scanners that are needed for position information used for slide stitching. We propose GloFlow, a two-stage method for creating a whole slide image using optical flow-based image registration with global alignment using a computationally tractable graph-pruning approach. In the first stage, we train an optical flow predictor to predict pairwise translations between successive video frames to approximate a stitch. In the second stage, this approximate stitch is used to create a neighborhood graph to produce a corrected stitch. On datasets of simulated video scans of pathology slides, we find that our method outperforms known approaches to slide-stitching, and stitches images resembling those produced by slide scanners. Our method allows for creation of whole slide images using widely-available low cost microscopes.