Promising Deep Semantic Nuclei Segmentation Models for Multi-Institutional Histopathology Images of Different Organs

Author
Keywords
Abstract
Nuclei segmentation in whole-slide imaging (WSI) plays a crucial role in the field of computational pathology. It is a fundamental task for different applications, such as cancer cell type classification, cancer grading, and cancer subtype classification. However, existing nuclei segmentation methods face many challenges, such as color variation in histopathological images, the overlapping and clumped nuclei, and the ambiguous boundary between different cell nuclei, that limit their performance. In this paper, we present promising deep semantic nuclei segmentation models for multi-institutional WSI images (i.e., collected from different scanners) of different organs. Specifically, we study the performance of pertinent deep learning-based models with nuclei segmentation in WSI images of different stains and various organs. We also propose a feasible deep learning nuclei segmentation model formed by combining robust deep learning architectures. A comprehensive comparative study with existing software and related methods in terms of different evaluation metrics and the number of parameters of each model, emphasizes the efficacy of the proposed nuclei segmentation models.
Year of Publication
9998
Journal
International Journal of Interactive Multimedia and Artificial Intelligence
Volume
In Press
Issue
In Press
Number
In Press
Number of Pages
1-11
Date Published
10/2020
ISSN Number
1989-1660
URL
https://www.ijimai.org/journal/sites/default/files/2020-10/ip2020_10_04.pdf
DOI
10.9781/ijimai.2020.10.004
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