Cargando…

Clinical application of mask region-based convolutional neural network for the automatic detection and segmentation of abnormal liver density based on hepatocellular carcinoma computed tomography datasets

The aim of the study was to use a previously proposed mask region–based convolutional neural network (Mask R-CNN) for automatic abnormal liver density detection and segmentation based on hepatocellular carcinoma (HCC) computed tomography (CT) datasets from a radiological perspective. Training and te...

Descripción completa

Detalles Bibliográficos
Autores principales: Yang, Ching-Juei, Wang, Chien-Kuo, Fang, Yu-Hua Dean, Wang, Jing-Yao, Su, Fong-Chin, Tsai, Hong-Ming, Lin, Yih-Jyh, Tsai, Hung-Wen, Yeh, Lee-Ren
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Public Library of Science 2021
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8354440/
https://www.ncbi.nlm.nih.gov/pubmed/34375365
http://dx.doi.org/10.1371/journal.pone.0255605
_version_ 1783736592387538944
author Yang, Ching-Juei
Wang, Chien-Kuo
Fang, Yu-Hua Dean
Wang, Jing-Yao
Su, Fong-Chin
Tsai, Hong-Ming
Lin, Yih-Jyh
Tsai, Hung-Wen
Yeh, Lee-Ren
author_facet Yang, Ching-Juei
Wang, Chien-Kuo
Fang, Yu-Hua Dean
Wang, Jing-Yao
Su, Fong-Chin
Tsai, Hong-Ming
Lin, Yih-Jyh
Tsai, Hung-Wen
Yeh, Lee-Ren
author_sort Yang, Ching-Juei
collection PubMed
description The aim of the study was to use a previously proposed mask region–based convolutional neural network (Mask R-CNN) for automatic abnormal liver density detection and segmentation based on hepatocellular carcinoma (HCC) computed tomography (CT) datasets from a radiological perspective. Training and testing datasets were acquired retrospectively from two hospitals of Taiwan. The training dataset contained 10,130 images of liver tumor densities of 11,258 regions of interest (ROIs). The positive testing dataset contained 1,833 images of liver tumor densities with 1,874 ROIs, and negative testing data comprised 20,283 images without abnormal densities in liver parenchyma. The Mask R-CNN was used to generate a medical model, and areas under the curve, true positive rates, false positive rates, and Dice coefficients were evaluated. For abnormal liver CT density detection, in each image, we identified the mean area under the curve, true positive rate, and false positive rate, which were 0.9490, 91.99%, and 13.68%, respectively. For segmentation ability, the highest mean Dice coefficient obtained was 0.8041. This study trained a Mask R-CNN on various HCC images to construct a medical model that serves as an auxiliary tool for alerting radiologists to abnormal CT density in liver scans; this model can simultaneously detect liver lesions and perform automatic instance segmentation.
format Online
Article
Text
id pubmed-8354440
institution National Center for Biotechnology Information
language English
publishDate 2021
publisher Public Library of Science
record_format MEDLINE/PubMed
spelling pubmed-83544402021-08-11 Clinical application of mask region-based convolutional neural network for the automatic detection and segmentation of abnormal liver density based on hepatocellular carcinoma computed tomography datasets Yang, Ching-Juei Wang, Chien-Kuo Fang, Yu-Hua Dean Wang, Jing-Yao Su, Fong-Chin Tsai, Hong-Ming Lin, Yih-Jyh Tsai, Hung-Wen Yeh, Lee-Ren PLoS One Research Article The aim of the study was to use a previously proposed mask region–based convolutional neural network (Mask R-CNN) for automatic abnormal liver density detection and segmentation based on hepatocellular carcinoma (HCC) computed tomography (CT) datasets from a radiological perspective. Training and testing datasets were acquired retrospectively from two hospitals of Taiwan. The training dataset contained 10,130 images of liver tumor densities of 11,258 regions of interest (ROIs). The positive testing dataset contained 1,833 images of liver tumor densities with 1,874 ROIs, and negative testing data comprised 20,283 images without abnormal densities in liver parenchyma. The Mask R-CNN was used to generate a medical model, and areas under the curve, true positive rates, false positive rates, and Dice coefficients were evaluated. For abnormal liver CT density detection, in each image, we identified the mean area under the curve, true positive rate, and false positive rate, which were 0.9490, 91.99%, and 13.68%, respectively. For segmentation ability, the highest mean Dice coefficient obtained was 0.8041. This study trained a Mask R-CNN on various HCC images to construct a medical model that serves as an auxiliary tool for alerting radiologists to abnormal CT density in liver scans; this model can simultaneously detect liver lesions and perform automatic instance segmentation. Public Library of Science 2021-08-10 /pmc/articles/PMC8354440/ /pubmed/34375365 http://dx.doi.org/10.1371/journal.pone.0255605 Text en © 2021 Yang 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
Yang, Ching-Juei
Wang, Chien-Kuo
Fang, Yu-Hua Dean
Wang, Jing-Yao
Su, Fong-Chin
Tsai, Hong-Ming
Lin, Yih-Jyh
Tsai, Hung-Wen
Yeh, Lee-Ren
Clinical application of mask region-based convolutional neural network for the automatic detection and segmentation of abnormal liver density based on hepatocellular carcinoma computed tomography datasets
title Clinical application of mask region-based convolutional neural network for the automatic detection and segmentation of abnormal liver density based on hepatocellular carcinoma computed tomography datasets
title_full Clinical application of mask region-based convolutional neural network for the automatic detection and segmentation of abnormal liver density based on hepatocellular carcinoma computed tomography datasets
title_fullStr Clinical application of mask region-based convolutional neural network for the automatic detection and segmentation of abnormal liver density based on hepatocellular carcinoma computed tomography datasets
title_full_unstemmed Clinical application of mask region-based convolutional neural network for the automatic detection and segmentation of abnormal liver density based on hepatocellular carcinoma computed tomography datasets
title_short Clinical application of mask region-based convolutional neural network for the automatic detection and segmentation of abnormal liver density based on hepatocellular carcinoma computed tomography datasets
title_sort clinical application of mask region-based convolutional neural network for the automatic detection and segmentation of abnormal liver density based on hepatocellular carcinoma computed tomography datasets
topic Research Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8354440/
https://www.ncbi.nlm.nih.gov/pubmed/34375365
http://dx.doi.org/10.1371/journal.pone.0255605
work_keys_str_mv AT yangchingjuei clinicalapplicationofmaskregionbasedconvolutionalneuralnetworkfortheautomaticdetectionandsegmentationofabnormalliverdensitybasedonhepatocellularcarcinomacomputedtomographydatasets
AT wangchienkuo clinicalapplicationofmaskregionbasedconvolutionalneuralnetworkfortheautomaticdetectionandsegmentationofabnormalliverdensitybasedonhepatocellularcarcinomacomputedtomographydatasets
AT fangyuhuadean clinicalapplicationofmaskregionbasedconvolutionalneuralnetworkfortheautomaticdetectionandsegmentationofabnormalliverdensitybasedonhepatocellularcarcinomacomputedtomographydatasets
AT wangjingyao clinicalapplicationofmaskregionbasedconvolutionalneuralnetworkfortheautomaticdetectionandsegmentationofabnormalliverdensitybasedonhepatocellularcarcinomacomputedtomographydatasets
AT sufongchin clinicalapplicationofmaskregionbasedconvolutionalneuralnetworkfortheautomaticdetectionandsegmentationofabnormalliverdensitybasedonhepatocellularcarcinomacomputedtomographydatasets
AT tsaihongming clinicalapplicationofmaskregionbasedconvolutionalneuralnetworkfortheautomaticdetectionandsegmentationofabnormalliverdensitybasedonhepatocellularcarcinomacomputedtomographydatasets
AT linyihjyh clinicalapplicationofmaskregionbasedconvolutionalneuralnetworkfortheautomaticdetectionandsegmentationofabnormalliverdensitybasedonhepatocellularcarcinomacomputedtomographydatasets
AT tsaihungwen clinicalapplicationofmaskregionbasedconvolutionalneuralnetworkfortheautomaticdetectionandsegmentationofabnormalliverdensitybasedonhepatocellularcarcinomacomputedtomographydatasets
AT yehleeren clinicalapplicationofmaskregionbasedconvolutionalneuralnetworkfortheautomaticdetectionandsegmentationofabnormalliverdensitybasedonhepatocellularcarcinomacomputedtomographydatasets