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.

翻译：我们提出了一种创新方法，可在全切片图像中快速分割苏木精-伊红染色组织，并消除包括笔迹标记和扫描伪影在内的多种不良伪影。该方法基于低倍率RGB概览图像提取单通道表示，其中像素值呈双峰分布，使得H&E染色组织能够清晰区分于背景及各种伪影。我们在30张来自不同机构、由多种WSI数字扫描仪制备且包含大量伪影的全切片图像上验证了该方法，并与大津阈值法、Histolab组织分割及笔迹过滤工具进行了对比。结果表明：我们的方法成功实现了29/30张WSI的组织分割与全伪影去除，而大津阈值法未能消除任何伪影，Histolab笔迹过滤工具仅能部分去除笔迹标记。本方法的精妙之处在于其简洁性：通过操控RGB色彩空间并运用大津阈值法，即可实现H&E染色组织分割与伪影快速消除，无需依赖机器学习或参数调优。