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...
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| Vydáno v: | Frontiers in physiology Ročník 12; s. 732711 |
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Frontiers Media S.A
27.09.2021
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| Abstract | 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
). |
|---|---|
| AbstractList | 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).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). 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). 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 ). |
| Author | Dong, Yuhao Huang, Meiping Qiu, Hailong Zhang, Jiawei Zhuang, Jian Shi, Yiyi Xie, Wen Wang, Tianchen Xu, Xiaowei Que, Lifeng Yuan, Haiyun Jia, Qianjun Yao, Zeyang |
| AuthorAffiliation | 5 Medical Imaging Center, Shenzhen Hospital, Southern Medical University , Shenzhen , China 4 Department of Computer Science and Engineering, University of Notre Dame , Notre Dame, IN , United States 2 Guangdong Provincial Key Laboratory of South China Structural Heart Disease, Guangdong Cardiovascular Institute, Guangdong Provincial People's Hospital, Guangdong Academy of Medical Sciences , Guangzhou , China 1 School of Medicine, South China University of Technology , Guangzhou , China 3 School of Computer Science, Fudan University , Shanghai , China |
| AuthorAffiliation_xml | – name: 3 School of Computer Science, Fudan University , Shanghai , China – name: 1 School of Medicine, South China University of Technology , Guangzhou , China – name: 4 Department of Computer Science and Engineering, University of Notre Dame , Notre Dame, IN , United States – name: 2 Guangdong Provincial Key Laboratory of South China Structural Heart Disease, Guangdong Cardiovascular Institute, Guangdong Provincial People's Hospital, Guangdong Academy of Medical Sciences , Guangzhou , China – name: 5 Medical Imaging Center, Shenzhen Hospital, Southern Medical University , Shenzhen , China |
| Author_xml | – sequence: 1 givenname: Zeyang surname: Yao fullname: Yao, Zeyang – sequence: 2 givenname: Wen surname: Xie fullname: Xie, Wen – sequence: 3 givenname: Jiawei surname: Zhang fullname: Zhang, Jiawei – sequence: 4 givenname: Yuhao surname: Dong fullname: Dong, Yuhao – sequence: 5 givenname: Hailong surname: Qiu fullname: Qiu, Hailong – sequence: 6 givenname: Haiyun surname: Yuan fullname: Yuan, Haiyun – sequence: 7 givenname: Qianjun surname: Jia fullname: Jia, Qianjun – sequence: 8 givenname: Tianchen surname: Wang fullname: Wang, Tianchen – sequence: 9 givenname: Yiyi surname: Shi fullname: Shi, Yiyi – sequence: 10 givenname: Jian surname: Zhuang fullname: Zhuang, Jian – sequence: 11 givenname: Lifeng surname: Que fullname: Que, Lifeng – sequence: 12 givenname: Xiaowei surname: Xu fullname: Xu, Xiaowei – sequence: 13 givenname: Meiping surname: Huang fullname: Huang, Meiping |
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| CitedBy_id | crossref_primary_10_1016_j_compmedimag_2024_102460 crossref_primary_10_1016_j_compmedimag_2024_102470 crossref_primary_10_1016_j_compbiomed_2025_110994 crossref_primary_10_1007_s10278_024_01164_0 crossref_primary_10_1186_s41747_023_00342_z crossref_primary_10_1109_JBHI_2025_3547744 crossref_primary_10_1007_s11760_022_02288_y crossref_primary_10_1109_TMI_2023_3339142 crossref_primary_10_1007_s40134_022_00407_8 crossref_primary_10_3390_diagnostics14131332 crossref_primary_10_1016_j_media_2025_103512 crossref_primary_10_1109_TRO_2023_3267694 crossref_primary_10_1142_S0219519425400718 crossref_primary_10_1007_s13239_025_00787_w |
| Cites_doi | 10.5220/0006114600240033 10.1016/j.jtcvs.2019.10.074 10.1161/01.cir.0000087386.07204.09 10.1117/1.JBO.22.12.126005 10.1093/ejcts/ezx236 10.1016/j.ejrad.2019.108713 10.1001/jama.283.7.897 10.1007/s00330-018-5931-z 10.1177/1538574410362118 10.1016/j.media.2018.03.010 10.1016/j.jtcvs.2012.11.048 |
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| Copyright | Copyright © 2021 Yao, Xie, Zhang, Dong, Qiu, Yuan, Jia, Wang, Shi, Zhuang, Que, Xu and Huang. Copyright © 2021 Yao, Xie, Zhang, Dong, Qiu, Yuan, Jia, Wang, Shi, Zhuang, Que, Xu and Huang. 2021 Yao, Xie, Zhang, Dong, Qiu, Yuan, Jia, Wang, Shi, Zhuang, Que, Xu and Huang |
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| Title | ImageTBAD: A 3D Computed Tomography Angiography Image Dataset for Automatic Segmentation of Type-B Aortic Dissection |
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