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...
محفوظ في:
| المؤلف الرئيسي: | |
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| مؤلفون آخرون: | |
| التنسيق: | Final Year Project |
| اللغة: | English |
| منشور في: |
Nanyang Technological University
2023
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| الموضوعات: | |
| الوصول للمادة أونلاين: | https://hdl.handle.net/10356/167795 |
| الوسوم: |
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| المؤسسة: | Nanyang Technological University |
| اللغة: | English |
| الملخص: | 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. |
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