Reconnection of fragmented parts of coronary arteries using local geometric features in X-ray angiography images

Kyunghoon Han, Jaeik Jeon, Yeonggul Jang, Sunghee Jung, Sekeun Kim, Hackjoon Shim, Byunghwan Jeon, Hyuk Jae Chang

Research output: Contribution to journalLetterpeer-review

2 Citations (Scopus)


The segmentation of coronary arteries in X-ray images is essential for image-based guiding procedures and the diagnosis of cardiovascular disease. However, owing to the complex and thin structures of the coronary arteries, it is challenging to accurately segment arteries in X-ray images using only a single neural network model. Consequently, coronary artery images obtained by segmentation with a single model are often fragmented, with parts of the arteries missing. Sophisticated post-processing is then required to identify and reconnect the fragmented regions. In this paper, we propose a method to reconstruct the missing regions of coronary arteries using X-ray angiography images. Method: We apply an independent convolutional neural network model considering local details, as well as a local geometric prior, for reconnecting the disconnected fragments. We implemented and compared the proposed method with several convolutional neural networks with customized encoding backbones as baseline models. Results: When integrated with our method, existing models improved considerably in terms of similarity with ground truth, with a mean increase of 0.330 of the Dice similarity coefficient in local regions of disconnected arteries. The method is efficient and is able to recover missing fragments in a short number of iterations. Conclusion and Significance: Owing to the restoration of missing fragments of coronary arteries, the proposed method enables a significant enhancement of clinical impact. The method is general and can simply be integrated into other existing methods for coronary artery segmentation.

Original languageEnglish
Article number105099
JournalComputers in Biology and Medicine
Publication statusPublished - 2022 Feb

Bibliographical note

Publisher Copyright:
© 2021 Elsevier Ltd

All Science Journal Classification (ASJC) codes

  • Computer Science Applications
  • Health Informatics


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