"RAPID" Regions-of-Interest Detection In Big Histopathological Images

TL;DR

This paper introduces a novel, fast, and accurate ROI detection algorithm for large histopathological images, utilizing superpixel optimization, prediction strategies, information reuse, and parallelization to significantly outperform existing methods in speed without sacrificing accuracy.

Contribution

The paper presents a new ROI detection algorithm that combines superpixel regularity optimization, a focus prediction strategy, information reuse, and parallel processing for improved speed and accuracy.

Findings

13 times speedup over baseline

160 times faster than SLIC

Maintains accuracy despite speed improvements

Abstract

The sheer volume and size of histopathological images (e.g.,10^6 MPixel) underscores the need for faster and more accurate Regions-of-interest (ROI) detection algorithms. In this paper, we propose such an algorithm, which has four main components that help achieve greater accuracy and faster speed: First, while using coarse-to-fine topology preserving segmentation as the baseline, the proposed algorithm uses a superpixel regularity optimization scheme for avoiding irregular and extremely small superpixels. Second, the proposed technique employs a prediction strategy to focus only on important superpixels at finer image levels. Third, the algorithm reuses the information gained from the coarsest image level at other finer image levels. Both the second and the third components drastically lower the complexity. Fourth, the algorithm employs a highly effective parallelization scheme using adap- tive data partitioning, which gains high speedup. Experimental results, conducted on the BSD500 [1] and 500 whole-slide histological images from the National Lung Screening Trial (NLST)1 dataset, confirm that the proposed algorithm gained 13 times speedup compared with the baseline, and around 160 times compared with SLIC [11], without losing accuracy.