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Fast Segmentation of Metastatic Foci in H&E Whole-Slide Images for Breast Cancer Diagnosis

Breast cancer is the leading cause of death for women globally. In clinical practice, pathologists visually scan over enormous amounts of gigapixel microscopic tissue slide images, which is a tedious and challenging task. In breast cancer diagnosis, micro-metastases and especially isolated tumor cel...

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Autores principales: Khalil, Muhammad-Adil, Lee, Yu-Ching, Lien, Huang-Chun, Jeng, Yung-Ming, Wang, Ching-Wei
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2022
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9030573/
https://www.ncbi.nlm.nih.gov/pubmed/35454038
http://dx.doi.org/10.3390/diagnostics12040990
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author Khalil, Muhammad-Adil
Lee, Yu-Ching
Lien, Huang-Chun
Jeng, Yung-Ming
Wang, Ching-Wei
author_facet Khalil, Muhammad-Adil
Lee, Yu-Ching
Lien, Huang-Chun
Jeng, Yung-Ming
Wang, Ching-Wei
author_sort Khalil, Muhammad-Adil
collection PubMed
description Breast cancer is the leading cause of death for women globally. In clinical practice, pathologists visually scan over enormous amounts of gigapixel microscopic tissue slide images, which is a tedious and challenging task. In breast cancer diagnosis, micro-metastases and especially isolated tumor cells are extremely difficult to detect and are easily neglected because tiny metastatic foci might be missed in visual examinations by medical doctors. However, the literature poorly explores the detection of isolated tumor cells, which could be recognized as a viable marker to determine the prognosis for T1NoMo breast cancer patients. To address these issues, we present a deep learning-based framework for efficient and robust lymph node metastasis segmentation in routinely used histopathological hematoxylin–eosin-stained (H–E) whole-slide images (WSI) in minutes, and a quantitative evaluation is conducted using 188 WSIs, containing 94 pairs of H–E-stained WSIs and immunohistochemical CK(AE1/AE3)-stained WSIs, which are used to produce a reliable and objective reference standard. The quantitative results demonstrate that the proposed method achieves 89.6% precision, 83.8% recall, 84.4% F1-score, and 74.9% mIoU, and that it performs significantly better than eight deep learning approaches, including two recently published models (v3_DCNN and Xception-65), and three variants of Deeplabv3+ with three different backbones, namely, U-Net, SegNet, and FCN, in precision, recall, F1-score, and mIoU ([Formula: see text]). Importantly, the proposed system is shown to be capable of identifying tiny metastatic foci in challenging cases, for which there are high probabilities of misdiagnosis in visual inspection, while the baseline approaches tend to fail in detecting tiny metastatic foci. For computational time comparison, the proposed method takes 2.4 min for processing a WSI utilizing four NVIDIA Geforce GTX 1080Ti GPU cards and 9.6 min using a single NVIDIA Geforce GTX 1080Ti GPU card, and is notably faster than the baseline methods (4-times faster than U-Net and SegNet, 5-times faster than FCN, 2-times faster than the 3 different variants of Deeplabv3+, 1.4-times faster than v3_DCNN, and 41-times faster than Xception-65).
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spelling pubmed-90305732022-04-23 Fast Segmentation of Metastatic Foci in H&E Whole-Slide Images for Breast Cancer Diagnosis Khalil, Muhammad-Adil Lee, Yu-Ching Lien, Huang-Chun Jeng, Yung-Ming Wang, Ching-Wei Diagnostics (Basel) Article Breast cancer is the leading cause of death for women globally. In clinical practice, pathologists visually scan over enormous amounts of gigapixel microscopic tissue slide images, which is a tedious and challenging task. In breast cancer diagnosis, micro-metastases and especially isolated tumor cells are extremely difficult to detect and are easily neglected because tiny metastatic foci might be missed in visual examinations by medical doctors. However, the literature poorly explores the detection of isolated tumor cells, which could be recognized as a viable marker to determine the prognosis for T1NoMo breast cancer patients. To address these issues, we present a deep learning-based framework for efficient and robust lymph node metastasis segmentation in routinely used histopathological hematoxylin–eosin-stained (H–E) whole-slide images (WSI) in minutes, and a quantitative evaluation is conducted using 188 WSIs, containing 94 pairs of H–E-stained WSIs and immunohistochemical CK(AE1/AE3)-stained WSIs, which are used to produce a reliable and objective reference standard. The quantitative results demonstrate that the proposed method achieves 89.6% precision, 83.8% recall, 84.4% F1-score, and 74.9% mIoU, and that it performs significantly better than eight deep learning approaches, including two recently published models (v3_DCNN and Xception-65), and three variants of Deeplabv3+ with three different backbones, namely, U-Net, SegNet, and FCN, in precision, recall, F1-score, and mIoU ([Formula: see text]). Importantly, the proposed system is shown to be capable of identifying tiny metastatic foci in challenging cases, for which there are high probabilities of misdiagnosis in visual inspection, while the baseline approaches tend to fail in detecting tiny metastatic foci. For computational time comparison, the proposed method takes 2.4 min for processing a WSI utilizing four NVIDIA Geforce GTX 1080Ti GPU cards and 9.6 min using a single NVIDIA Geforce GTX 1080Ti GPU card, and is notably faster than the baseline methods (4-times faster than U-Net and SegNet, 5-times faster than FCN, 2-times faster than the 3 different variants of Deeplabv3+, 1.4-times faster than v3_DCNN, and 41-times faster than Xception-65). MDPI 2022-04-14 /pmc/articles/PMC9030573/ /pubmed/35454038 http://dx.doi.org/10.3390/diagnostics12040990 Text en © 2022 by the authors. https://creativecommons.org/licenses/by/4.0/Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/).
spellingShingle Article
Khalil, Muhammad-Adil
Lee, Yu-Ching
Lien, Huang-Chun
Jeng, Yung-Ming
Wang, Ching-Wei
Fast Segmentation of Metastatic Foci in H&E Whole-Slide Images for Breast Cancer Diagnosis
title Fast Segmentation of Metastatic Foci in H&E Whole-Slide Images for Breast Cancer Diagnosis
title_full Fast Segmentation of Metastatic Foci in H&E Whole-Slide Images for Breast Cancer Diagnosis
title_fullStr Fast Segmentation of Metastatic Foci in H&E Whole-Slide Images for Breast Cancer Diagnosis
title_full_unstemmed Fast Segmentation of Metastatic Foci in H&E Whole-Slide Images for Breast Cancer Diagnosis
title_short Fast Segmentation of Metastatic Foci in H&E Whole-Slide Images for Breast Cancer Diagnosis
title_sort fast segmentation of metastatic foci in h&e whole-slide images for breast cancer diagnosis
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9030573/
https://www.ncbi.nlm.nih.gov/pubmed/35454038
http://dx.doi.org/10.3390/diagnostics12040990
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