HistoSegNet: Semantic Segmentation of Histological Tissue Type in Whole Slide Images
Lyndon Chan, Mahdi S. Hosseini, Corwyn Rowsell, Konstantinos N. Plataniotis, Savvas Damaskinos
Abstract
In digital pathology, tissue slides are scanned into Whole Slide Images (WSI) and pathologists first screen for diagnostically-relevant Regions of Interest (ROIs) before reviewing them. Screening for ROIs is a tedious and time-consuming visual recognition task which can be exhausting. The cognitive workload could be reduced by developing a visual aid to narrow down the visual search area by highlighting (or segmenting) regions of diagnostic relevance, enabling pathologists to spend more time diagnosing relevant ROIs. In this paper, we propose HistoSegNet, a method for semantic segmentation of histological tissue type (HTT). Using the HTT-annotated Atlas of Digital Pathology (ADP) database, we train a Convolutional Neural Network on the patch annotations, infer Gradient-Weighted Class Activation Maps, average overlapping predictions, and post-process the segmentation with a fully-connected Conditional Random Field. Our method out-performs more complicated weakly-supervised semantic segmentation methods and can generalize to other datasets without retraining.
BibTeX
@inproceedings{iccv2019_histosegnetseman,
title = {HistoSegNet: Semantic Segmentation of Histological Tissue Type in Whole Slide Images},
author = {Lyndon Chan and Mahdi S. Hosseini and Corwyn Rowsell and Konstantinos N. Plataniotis and Savvas Damaskinos},
booktitle = {ICCV 2019},
year = {2019}
}