Rapid Artefact Removal and H&E-Stained Tissue Segmentation

TL;DR

This paper introduces a simple, fast method for segmenting H&E-stained tissue in whole-slide images and removing artefacts effectively, using a single-channel RGB overview and Otsu thresholding, without machine learning.

Contribution

The proposed approach is a novel, parameter-free technique that efficiently segments tissue and eliminates artefacts across diverse WSIs without complex models.

Findings

Successfully removed artefacts in 29 out of 30 WSIs

Outperformed Otsu thresholding and Histolab pen filtering tools

Operates rapidly without machine learning or parameter tuning

Abstract

We present an innovative method for rapidly segmenting hematoxylin and eosin (H&E)-stained tissue in whole-slide images (WSIs) that eliminates a wide range of undesirable artefacts such as pen marks and scanning artefacts. Our method involves taking a single-channel representation of a lowmagnification RGB overview of the WSI in which the pixel values are bimodally distributed such that H&E-stained tissue is easily distinguished from both background and a wide variety of artefacts. We demonstrate our method on 30 WSIs prepared from a wide range of institutions and WSI digital scanners, each containing substantial artefacts, and compare it to segmentations provided by Otsu thresholding and Histolab tissue segmentation and pen filtering tools. We found that our method segmented the tissue and fully removed all artefacts in 29 out of 30 WSIs, whereas Otsu thresholding failed to remove any artefacts, and the Histolab pen filtering tools only partially removed the pen marks. The beauty of our approach lies in its simplicity: manipulating RGB colour space and using Otsu thresholding allows for the segmentation of H&E-stained tissue and the rapid removal of artefacts without the need for machine learning or parameter tuning.