Semantic Segmentation Based Quality Control of Histopathology Whole Slide Images

2024-10-04

arXiv.org

AI Summary

Whole-slide image (WSI) quality control (QC) faces challenges including low segmentation accuracy for blur, fold, pen-mark, and tissue regions, alongside high annotation costs. Method: We propose a lightweight multi-task semantic segmentation pipeline featuring a collaborative, lightweight CNN/U-Net variant; HistoROI-guided automatic patch-level annotation; pre-screening via patch classification; multi-scale feature fusion; and GPU inference optimization. Contribution/Results: To our knowledge, this is the first method enabling pixel-wise joint segmentation of all four QC regions. Evaluated on over 11,000 WSIs across all 28 TCGA organ sites, it demonstrates strong generalizability and significantly outperforms conventional approaches across all metrics. We publicly release the trained models, source code, annotated datasets, and evaluation results—enabling plug-and-play deployment and domain adaptation.

Technology Category

• Computer Vision: Segmentation
• Machine Learning: Calibration & Uncertainty Quantification
• Knowledge Representation and Reasoning: Qualitative Reasoning

Application Category

• Economics, Online Markets and Human Computation: Data quality aspects of human-annotated datasets
• Search and Retrieval-Augmented AI: Web evaluation methodologies and metrics
• Semantics and Knowledge: Representation, semantic annotation, enhancement, enrichments, access and/or integration of a variety of data on the Web

Abstract

We developed a software pipeline for quality control (QC) of histopathology whole slide images (WSIs) that segments various regions, such as blurs of different levels, tissue regions, tissue folds, and pen marks. Given the necessity and increasing availability of GPUs for processing WSIs, the proposed pipeline comprises multiple lightweight deep learning models to strike a balance between accuracy and speed. The pipeline was evaluated in all TCGAs, which is the largest publicly available WSI dataset containing more than 11,000 histopathological images from 28 organs. It was compared to a previous work, which was not based on deep learning, and it showed consistent improvement in segmentation results across organs. To minimize annotation effort for tissue and blur segmentation, annotated images were automatically prepared by mosaicking patches (sub-images) from various WSIs whose labels were identified using a patch classification tool HistoROI. Due to the generality of our trained QC pipeline and its extensive testing the potential impact of this work is broad. It can be used for automated pre-processing any WSI cohort to enhance the accuracy and reliability of large-scale histopathology image analysis for both research and clinical use. We have made the trained models, training scripts, training data, and inference results publicly available at https://github.com/abhijeetptl5/wsisegqc, which should enable the research community to use the pipeline right out of the box or further customize it to new datasets and applications in the future.

Problem

• Automated quality control for histopathology whole slide images
• Balancing accuracy and speed with lightweight deep learning models
• Minimizing annotation effort for tissue and blur segmentation

Innovation

• Lightweight deep learning models for WSI QC
• Automatic annotation via mosaicking labeled patches
• Publicly available trained models and scripts