Fast Regions-of-Interest Detection in Whole Slide Histopathology Images

Abstract

Detecting and localizing pathological region of interest (ROI) over whole slide pathological image (WSI) is a challenging problem. To reduce computational complexity, we introduced a two-stage superpixel-based ROI detection approach. To efficiently construct superpixels with fine details preserved, we utilized a novel superpixel clustering algorithm which cluster blocks of pixel in a hierarchical fashion. The major reduction of complexity is attributed to the combination of boundary update and coarse-to-fine refinement in superpixel clustering. The former maintains the accuracy of segmentation, meanwhile, avoids most of unnecessary revisit to the ‘non-boundary’ pixels. The latter reduces the complexity by faster localizing those boundary blocks. Detector of RoI was trained using handcrafted features extracted from super-pixels of labeled WSIs. Extensive experiments indicates that the introduced superpixel clustering algorithm showed lifted accuracy on lung cancer WSI detection at much less cost, compared to other classic superpixel clustering approaches. Moreover, the clustered superpixels do not only facilitate a fast detection, also deliver a boundary-preserving segmentation of ROI in whole slide images.