Automatic coronary artery labeling based on point cloud and connectivity features

Automatic coronary artery labeling is of great significance for automatic generation of cardiovascular disease diagnosis reports. Traditional methods include model-based and learning-based methods, but due to the complex and changeable vascular structure, automatic coronary artery segment labeling i...

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書目詳細資料
主要作者: Li, Jianyun
其他作者: Lin Zhiping
格式: Final Year Project
語言:English
出版: Nanyang Technological University 2023
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在線閱讀:https://hdl.handle.net/10356/167795
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機構: Nanyang Technological University
語言: English
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總結:Automatic coronary artery labeling is of great significance for automatic generation of cardiovascular disease diagnosis reports. Traditional methods include model-based and learning-based methods, but due to the complex and changeable vascular structure, automatic coronary artery segment labeling is still very challenging. In this paper, we adapt an innovative connectivity feature in pointnet++ model architecture for automatic coronary artery segment labeling. The inputs are 3D coronary artery centerline points extracted from CTCA images and their connectivity features, and the outputs are the index of coronary artery segments. We evaluated our method on a real-clinical dataset. Experimental results show that the proposed method has high accuracy and short interference time.