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ImageTBAD: A 3D Computed Tomography Angiography Image Dataset for Automatic Segmentation of Type-B Aortic Dissection
Type-B Aortic Dissection (TBAD) is one of the most serious cardiovascular events characterized by a growing yearly incidence, and the severity of disease prognosis. Currently, computed tomography angiography (CTA) has been widely adopted for the diagnosis and prognosis of TBAD. Accurate segmentation...
Autores principales: | , , , , , , , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Frontiers Media S.A.
2021
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8503642/ https://www.ncbi.nlm.nih.gov/pubmed/34646158 http://dx.doi.org/10.3389/fphys.2021.732711 |
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author | Yao, Zeyang Xie, Wen Zhang, Jiawei Dong, Yuhao Qiu, Hailong Yuan, Haiyun Jia, Qianjun Wang, Tianchen Shi, Yiyi Zhuang, Jian Que, Lifeng Xu, Xiaowei Huang, Meiping |
author_facet | Yao, Zeyang Xie, Wen Zhang, Jiawei Dong, Yuhao Qiu, Hailong Yuan, Haiyun Jia, Qianjun Wang, Tianchen Shi, Yiyi Zhuang, Jian Que, Lifeng Xu, Xiaowei Huang, Meiping |
author_sort | Yao, Zeyang |
collection | PubMed |
description | Type-B Aortic Dissection (TBAD) is one of the most serious cardiovascular events characterized by a growing yearly incidence, and the severity of disease prognosis. Currently, computed tomography angiography (CTA) has been widely adopted for the diagnosis and prognosis of TBAD. Accurate segmentation of true lumen (TL), false lumen (FL), and false lumen thrombus (FLT) in CTA are crucial for the precise quantification of anatomical features. However, existing works only focus on only TL and FL without considering FLT. In this paper, we propose ImageTBAD, the first 3D computed tomography angiography (CTA) image dataset of TBAD with annotation of TL, FL, and FLT. The proposed dataset contains 100 TBAD CTA images, which is of decent size compared with existing medical imaging datasets. As FLT can appear almost anywhere along the aorta with irregular shapes, segmentation of FLT presents a wide class of segmentation problems where targets exist in a variety of positions with irregular shapes. We further propose a baseline method for automatic segmentation of TBAD. Results show that the baseline method can achieve comparable results with existing works on aorta and TL segmentation. However, the segmentation accuracy of FLT is only 52%, which leaves large room for improvement and also shows the challenge of our dataset. To facilitate further research on this challenging problem, our dataset and codes are released to the public (Dataset, 2020). |
format | Online Article Text |
id | pubmed-8503642 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2021 |
publisher | Frontiers Media S.A. |
record_format | MEDLINE/PubMed |
spelling | pubmed-85036422021-10-12 ImageTBAD: A 3D Computed Tomography Angiography Image Dataset for Automatic Segmentation of Type-B Aortic Dissection Yao, Zeyang Xie, Wen Zhang, Jiawei Dong, Yuhao Qiu, Hailong Yuan, Haiyun Jia, Qianjun Wang, Tianchen Shi, Yiyi Zhuang, Jian Que, Lifeng Xu, Xiaowei Huang, Meiping Front Physiol Physiology Type-B Aortic Dissection (TBAD) is one of the most serious cardiovascular events characterized by a growing yearly incidence, and the severity of disease prognosis. Currently, computed tomography angiography (CTA) has been widely adopted for the diagnosis and prognosis of TBAD. Accurate segmentation of true lumen (TL), false lumen (FL), and false lumen thrombus (FLT) in CTA are crucial for the precise quantification of anatomical features. However, existing works only focus on only TL and FL without considering FLT. In this paper, we propose ImageTBAD, the first 3D computed tomography angiography (CTA) image dataset of TBAD with annotation of TL, FL, and FLT. The proposed dataset contains 100 TBAD CTA images, which is of decent size compared with existing medical imaging datasets. As FLT can appear almost anywhere along the aorta with irregular shapes, segmentation of FLT presents a wide class of segmentation problems where targets exist in a variety of positions with irregular shapes. We further propose a baseline method for automatic segmentation of TBAD. Results show that the baseline method can achieve comparable results with existing works on aorta and TL segmentation. However, the segmentation accuracy of FLT is only 52%, which leaves large room for improvement and also shows the challenge of our dataset. To facilitate further research on this challenging problem, our dataset and codes are released to the public (Dataset, 2020). Frontiers Media S.A. 2021-09-27 /pmc/articles/PMC8503642/ /pubmed/34646158 http://dx.doi.org/10.3389/fphys.2021.732711 Text en Copyright © 2021 Yao, Xie, Zhang, Dong, Qiu, Yuan, Jia, Wang, Shi, Zhuang, Que, Xu and Huang. https://creativecommons.org/licenses/by/4.0/This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms. |
spellingShingle | Physiology Yao, Zeyang Xie, Wen Zhang, Jiawei Dong, Yuhao Qiu, Hailong Yuan, Haiyun Jia, Qianjun Wang, Tianchen Shi, Yiyi Zhuang, Jian Que, Lifeng Xu, Xiaowei Huang, Meiping ImageTBAD: A 3D Computed Tomography Angiography Image Dataset for Automatic Segmentation of Type-B Aortic Dissection |
title | ImageTBAD: A 3D Computed Tomography Angiography Image Dataset for Automatic Segmentation of Type-B Aortic Dissection |
title_full | ImageTBAD: A 3D Computed Tomography Angiography Image Dataset for Automatic Segmentation of Type-B Aortic Dissection |
title_fullStr | ImageTBAD: A 3D Computed Tomography Angiography Image Dataset for Automatic Segmentation of Type-B Aortic Dissection |
title_full_unstemmed | ImageTBAD: A 3D Computed Tomography Angiography Image Dataset for Automatic Segmentation of Type-B Aortic Dissection |
title_short | ImageTBAD: A 3D Computed Tomography Angiography Image Dataset for Automatic Segmentation of Type-B Aortic Dissection |
title_sort | imagetbad: a 3d computed tomography angiography image dataset for automatic segmentation of type-b aortic dissection |
topic | Physiology |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8503642/ https://www.ncbi.nlm.nih.gov/pubmed/34646158 http://dx.doi.org/10.3389/fphys.2021.732711 |
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