Authors:
Michal Marczyk
1
;
2
;
Agata Wrobel
3
;
Julia Merta
3
and
Joanna Polanska
1
Affiliations:
1
Department of Data Science and Engineering, Silesian University of Technology, 44-100 Gliwice, Poland
;
2
Yale Cancer Center, Yale School of Medicine, 06511 New Haven, CT, U.S.A.
;
3
Faculty of Automatic Control, Electronics and Computer Science, Silesian University of Technology, 44-100 Gliwice, Poland
Keyword(s):
Histopathology, Whole-Slide Image, Tissue Staining, Tissue Segmentation, Post-Processing.
Abstract:
Digital pathology allows for the efficient storage and advanced computational analysis of stained histopathological slides of various tissues. Tissue segmentation is a crucial first step of digital pathology aimed at eliminating background, pen markings, and other artifacts, reducing image size, and increasing the efficiency of further analysis. In most cases, color thresholding or deep learning models are used, but their effectiveness is reduced due to complex artifacts and huge color variations between slides. We propose a post-processing method to increase the tissue segmentation performance of any initial segmentation algorithm. Using a set of 197 manually annotated histopathological images of breast cancer patients and 63 images of endometrial cancer patients, we tested our method with 3 thresholding techniques and 3 deep learning-based algorithms by calculating the Dice index, Jaccard index, precision, and recall. In both datasets, applying post-processing increased precision a
nd recall for thresholding methods and mostly precision for deep learning models. Overall, applying post-processing gave better tissue segmentation performance than initial segmentation methods, significantly increasing Dice and Jaccard indices. Our results proved that thanks to post-processing, the tissue segmentation pipeline is more robust to noises and artifacts commonly present in histopathological images.
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