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Deep Learning for Accurate Segmentation of Venous Thrombus from Black-Blood Magnetic Resonance Images: A Multicenter Study
OBJECTIVE: Deep vein thrombosis (DVT) is the third-largest cardiovascular disease, and accurate segmentation of venous thrombus from the black-blood magnetic resonance (MR) images can provide additional information for personalized DVT treatment planning. Therefore, a deep learning network is propos...
Autores principales: | , , , , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Hindawi
2021
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8692022/ https://www.ncbi.nlm.nih.gov/pubmed/34950733 http://dx.doi.org/10.1155/2021/4989297 |
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author | Sun, Chuanqi Xiong, Xiangyu Zhang, Tianjing Guan, Xiuhong Mao, Huan Yang, Jing Zhang, Xiaoyong Sun, Yi Chen, Hao Xie, Guoxi |
author_facet | Sun, Chuanqi Xiong, Xiangyu Zhang, Tianjing Guan, Xiuhong Mao, Huan Yang, Jing Zhang, Xiaoyong Sun, Yi Chen, Hao Xie, Guoxi |
author_sort | Sun, Chuanqi |
collection | PubMed |
description | OBJECTIVE: Deep vein thrombosis (DVT) is the third-largest cardiovascular disease, and accurate segmentation of venous thrombus from the black-blood magnetic resonance (MR) images can provide additional information for personalized DVT treatment planning. Therefore, a deep learning network is proposed to automatically segment venous thrombus with high accuracy and reliability. METHODS: In order to train, test, and external test the developed network, total images of 110 subjects are obtained from three different centers with two different black-blood MR techniques (i.e., DANTE-SPACE and DANTE-FLASH). Two experienced radiologists manually contoured each venous thrombus, followed by reediting, to create the ground truth. 5-fold cross-validation strategy is applied for training and testing. The segmentation performance is measured on pixel and vessel segment levels. For the pixel level, the dice similarity coefficient (DSC), average Hausdorff distance (AHD), and absolute volume difference (AVD) of segmented thrombus are calculated. For the vessel segment level, the sensitivity (SE), specificity (SP), accuracy (ACC), and positive and negative predictive values (PPV and NPV) are used. RESULTS: The proposed network generates segmentation results in good agreement with the ground truth. Based on the pixel level, the proposed network achieves excellent results on testing and the other two external testing sets, DSC are 0.76, 0.76, and 0.73, AHD (mm) are 4.11, 6.45, and 6.49, and AVD are 0.16, 0.18, and 0.22. On the vessel segment level, SE are 0.95, 0.93, and 0.81, SP are 0.97, 0.92, and 0.97, ACC are 0.96, 0.94, and 0.95, PPV are 0.97, 0.82, and 0.96, and NPV are 0.97, 0.96, and 0.94. CONCLUSIONS: The proposed deep learning network is effective and stable for fully automatic segmentation of venous thrombus on black blood MR images. |
format | Online Article Text |
id | pubmed-8692022 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2021 |
publisher | Hindawi |
record_format | MEDLINE/PubMed |
spelling | pubmed-86920222021-12-22 Deep Learning for Accurate Segmentation of Venous Thrombus from Black-Blood Magnetic Resonance Images: A Multicenter Study Sun, Chuanqi Xiong, Xiangyu Zhang, Tianjing Guan, Xiuhong Mao, Huan Yang, Jing Zhang, Xiaoyong Sun, Yi Chen, Hao Xie, Guoxi Biomed Res Int Research Article OBJECTIVE: Deep vein thrombosis (DVT) is the third-largest cardiovascular disease, and accurate segmentation of venous thrombus from the black-blood magnetic resonance (MR) images can provide additional information for personalized DVT treatment planning. Therefore, a deep learning network is proposed to automatically segment venous thrombus with high accuracy and reliability. METHODS: In order to train, test, and external test the developed network, total images of 110 subjects are obtained from three different centers with two different black-blood MR techniques (i.e., DANTE-SPACE and DANTE-FLASH). Two experienced radiologists manually contoured each venous thrombus, followed by reediting, to create the ground truth. 5-fold cross-validation strategy is applied for training and testing. The segmentation performance is measured on pixel and vessel segment levels. For the pixel level, the dice similarity coefficient (DSC), average Hausdorff distance (AHD), and absolute volume difference (AVD) of segmented thrombus are calculated. For the vessel segment level, the sensitivity (SE), specificity (SP), accuracy (ACC), and positive and negative predictive values (PPV and NPV) are used. RESULTS: The proposed network generates segmentation results in good agreement with the ground truth. Based on the pixel level, the proposed network achieves excellent results on testing and the other two external testing sets, DSC are 0.76, 0.76, and 0.73, AHD (mm) are 4.11, 6.45, and 6.49, and AVD are 0.16, 0.18, and 0.22. On the vessel segment level, SE are 0.95, 0.93, and 0.81, SP are 0.97, 0.92, and 0.97, ACC are 0.96, 0.94, and 0.95, PPV are 0.97, 0.82, and 0.96, and NPV are 0.97, 0.96, and 0.94. CONCLUSIONS: The proposed deep learning network is effective and stable for fully automatic segmentation of venous thrombus on black blood MR images. Hindawi 2021-12-14 /pmc/articles/PMC8692022/ /pubmed/34950733 http://dx.doi.org/10.1155/2021/4989297 Text en Copyright © 2021 Chuanqi Sun et al. https://creativecommons.org/licenses/by/4.0/This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. |
spellingShingle | Research Article Sun, Chuanqi Xiong, Xiangyu Zhang, Tianjing Guan, Xiuhong Mao, Huan Yang, Jing Zhang, Xiaoyong Sun, Yi Chen, Hao Xie, Guoxi Deep Learning for Accurate Segmentation of Venous Thrombus from Black-Blood Magnetic Resonance Images: A Multicenter Study |
title | Deep Learning for Accurate Segmentation of Venous Thrombus from Black-Blood Magnetic Resonance Images: A Multicenter Study |
title_full | Deep Learning for Accurate Segmentation of Venous Thrombus from Black-Blood Magnetic Resonance Images: A Multicenter Study |
title_fullStr | Deep Learning for Accurate Segmentation of Venous Thrombus from Black-Blood Magnetic Resonance Images: A Multicenter Study |
title_full_unstemmed | Deep Learning for Accurate Segmentation of Venous Thrombus from Black-Blood Magnetic Resonance Images: A Multicenter Study |
title_short | Deep Learning for Accurate Segmentation of Venous Thrombus from Black-Blood Magnetic Resonance Images: A Multicenter Study |
title_sort | deep learning for accurate segmentation of venous thrombus from black-blood magnetic resonance images: a multicenter study |
topic | Research Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8692022/ https://www.ncbi.nlm.nih.gov/pubmed/34950733 http://dx.doi.org/10.1155/2021/4989297 |
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