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
Hlavní autoři: 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
Médium: Journal Article
Jazyk:angličtina
Vydáno: Frontiers Media S.A 27.09.2021
Témata:
automatic segmentation
computed tomography
dataset
deep neural networks
Physiology
type-B aortic dissection
ISSN:1664-042X, 1664-042X
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Shrnutí: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 ).
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Reviewed by: Guang Yang, Imperial College London, United Kingdom; Alessio Gizzi, Campus Bio-Medico University, Italy; Ulas Bagci, Northwestern University, United States
Edited by: Linwei Wang, Rochester Institute of Technology, United States
These authors have contributed equally to this work
This article was submitted to Computational Physiology and Medicine, a section of the journal Frontiers in Physiology
ISSN:1664-042X
1664-042X
DOI:10.3389/fphys.2021.732711