Cargando…

A fast and effective detection framework for whole-slide histopathology image analysis

Pathologists generally pan, focus, zoom and scan tissue biopsies either under microscopes or on digital images for diagnosis. With the rapid development of whole-slide digital scanners for histopathology, computer-assisted digital pathology image analysis has attracted increasing clinical attention....

Descripción completa

Detalles Bibliográficos
Autores principales: Ruan, Jun, Zhu, Zhikui, Wu, Chenchen, Ye, Guanglu, Zhou, Jingfan, Yue, Junqiu
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Public Library of Science 2021
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8115773/
https://www.ncbi.nlm.nih.gov/pubmed/33979398
http://dx.doi.org/10.1371/journal.pone.0251521
_version_ 1783691256687230976
author Ruan, Jun
Zhu, Zhikui
Wu, Chenchen
Ye, Guanglu
Zhou, Jingfan
Yue, Junqiu
author_facet Ruan, Jun
Zhu, Zhikui
Wu, Chenchen
Ye, Guanglu
Zhou, Jingfan
Yue, Junqiu
author_sort Ruan, Jun
collection PubMed
description Pathologists generally pan, focus, zoom and scan tissue biopsies either under microscopes or on digital images for diagnosis. With the rapid development of whole-slide digital scanners for histopathology, computer-assisted digital pathology image analysis has attracted increasing clinical attention. Thus, the working style of pathologists is also beginning to change. Computer-assisted image analysis systems have been developed to help pathologists perform basic examinations. This paper presents a novel lightweight detection framework for automatic tumor detection in whole-slide histopathology images. We develop the Double Magnification Combination (DMC) classifier, which is a modified DenseNet-40 to make patch-level predictions with only 0.3 million parameters. To improve the detection performance of multiple instances, we propose an improved adaptive sampling method with superpixel segmentation and introduce a new heuristic factor, local sampling density, as the convergence condition of iterations. In postprocessing, we use a CNN model with 4 convolutional layers to regulate the patch-level predictions based on the predictions of adjacent sampling points and use linear interpolation to generate a tumor probability heatmap. The entire framework was trained and validated using the dataset from the Camelyon16 Grand Challenge and Hubei Cancer Hospital. In our experiments, the average AUC was 0.95 in the test set for pixel-level detection.
format Online
Article
Text
id pubmed-8115773
institution National Center for Biotechnology Information
language English
publishDate 2021
publisher Public Library of Science
record_format MEDLINE/PubMed
spelling pubmed-81157732021-05-24 A fast and effective detection framework for whole-slide histopathology image analysis Ruan, Jun Zhu, Zhikui Wu, Chenchen Ye, Guanglu Zhou, Jingfan Yue, Junqiu PLoS One Research Article Pathologists generally pan, focus, zoom and scan tissue biopsies either under microscopes or on digital images for diagnosis. With the rapid development of whole-slide digital scanners for histopathology, computer-assisted digital pathology image analysis has attracted increasing clinical attention. Thus, the working style of pathologists is also beginning to change. Computer-assisted image analysis systems have been developed to help pathologists perform basic examinations. This paper presents a novel lightweight detection framework for automatic tumor detection in whole-slide histopathology images. We develop the Double Magnification Combination (DMC) classifier, which is a modified DenseNet-40 to make patch-level predictions with only 0.3 million parameters. To improve the detection performance of multiple instances, we propose an improved adaptive sampling method with superpixel segmentation and introduce a new heuristic factor, local sampling density, as the convergence condition of iterations. In postprocessing, we use a CNN model with 4 convolutional layers to regulate the patch-level predictions based on the predictions of adjacent sampling points and use linear interpolation to generate a tumor probability heatmap. The entire framework was trained and validated using the dataset from the Camelyon16 Grand Challenge and Hubei Cancer Hospital. In our experiments, the average AUC was 0.95 in the test set for pixel-level detection. Public Library of Science 2021-05-12 /pmc/articles/PMC8115773/ /pubmed/33979398 http://dx.doi.org/10.1371/journal.pone.0251521 Text en © 2021 Ruan et al https://creativecommons.org/licenses/by/4.0/This is an open access article distributed under the terms of the Creative Commons Attribution License (https://creativecommons.org/licenses/by/4.0/) , which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
spellingShingle Research Article
Ruan, Jun
Zhu, Zhikui
Wu, Chenchen
Ye, Guanglu
Zhou, Jingfan
Yue, Junqiu
A fast and effective detection framework for whole-slide histopathology image analysis
title A fast and effective detection framework for whole-slide histopathology image analysis
title_full A fast and effective detection framework for whole-slide histopathology image analysis
title_fullStr A fast and effective detection framework for whole-slide histopathology image analysis
title_full_unstemmed A fast and effective detection framework for whole-slide histopathology image analysis
title_short A fast and effective detection framework for whole-slide histopathology image analysis
title_sort fast and effective detection framework for whole-slide histopathology image analysis
topic Research Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8115773/
https://www.ncbi.nlm.nih.gov/pubmed/33979398
http://dx.doi.org/10.1371/journal.pone.0251521
work_keys_str_mv AT ruanjun afastandeffectivedetectionframeworkforwholeslidehistopathologyimageanalysis
AT zhuzhikui afastandeffectivedetectionframeworkforwholeslidehistopathologyimageanalysis
AT wuchenchen afastandeffectivedetectionframeworkforwholeslidehistopathologyimageanalysis
AT yeguanglu afastandeffectivedetectionframeworkforwholeslidehistopathologyimageanalysis
AT zhoujingfan afastandeffectivedetectionframeworkforwholeslidehistopathologyimageanalysis
AT yuejunqiu afastandeffectivedetectionframeworkforwholeslidehistopathologyimageanalysis
AT ruanjun fastandeffectivedetectionframeworkforwholeslidehistopathologyimageanalysis
AT zhuzhikui fastandeffectivedetectionframeworkforwholeslidehistopathologyimageanalysis
AT wuchenchen fastandeffectivedetectionframeworkforwholeslidehistopathologyimageanalysis
AT yeguanglu fastandeffectivedetectionframeworkforwholeslidehistopathologyimageanalysis
AT zhoujingfan fastandeffectivedetectionframeworkforwholeslidehistopathologyimageanalysis
AT yuejunqiu fastandeffectivedetectionframeworkforwholeslidehistopathologyimageanalysis