ImageALCAPA: A 3D Computed Tomography Image Dataset for Automatic Segmentation of Anomalous Left Coronary Artery from Pulmonary Artery-Reference-Cited by-同舟云学术

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ImageALCAPA: A 3D Computed Tomography Image Dataset for Automatic Segmentation of Anomalous Left Coronary Artery from Pulmonary Artery

Published:2022-12-06 Issue: Volume: Page:
ISSN:
Container-title:2022 IEEE International Conference on Bioinformatics and Biomedicine (BIBM)
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Author:

Zeng An¹,Mi Chenxi¹,Pan Dan²,Lu Qing³,Xu Xiaowei⁴

Affiliation:

1. Guangdong University of Technology,School of Computer Science,Guangzhou,China

2. Guangdong Polytechnic Normal University,Department of Computer Science,Guangzhou,China

3. University of Notre Dame,Department of Computer Science and Engineering,IN,US

4. Guangdong Provincial People’s Hospital, Guangdong Academy of Medical Sciences,Guangzhou,China

Funder

National Natural Science Foundation of China

Publisher

IEEE

Link

http://xplorestaging.ieee.org/ielx7/9994793/9994847/09994951.pdf?arnumber=9994951

Reference17 articles.

1. Brain tumor segmentation and radiomics survival prediction: Contribution to the brats 2017 challenge;isensee;International MICCAI Brainlesion Workshop,2017

2. U-net: Convolutional networks for biomedical image segmentation;ronneberger;International Conference on Medical Image Computing and Computer-Assisted Intervention,2015

3. Automatic coronary artery segmentation algorithm based on deep learning and digital image processing

4. Efficacy evaluation of 2D, 3D U-Net semantic segmentation and atlas-based segmentation of normal lungs excluding the trachea and main bronchi

5. Learning tree-structured representation for 3D coronary artery segmentation

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