Reconnection of fragmented parts of coronary arteries using local geometric features in X-ray angiography images. (February 2022)

• Record Type: Journal Article
• Authors: Han, Kyunghoon; Jeon, Jaeik; Jang, Yeonggul; Jung, Sunghee; Kim, Sekeun; Shim, Hackjoon; Jeon, Byunghwan; Chang, Hyuk-Jae
• Is Part Of: Computers in biology and medicine. Volume 141(2022)
• Publication Date: 2022-02
• Subjects: 2D X-ray -- Coronary artery -- Geometric prior; Medicine -- Data processing -- Periodicals; Biology -- Data processing -- Periodicals; 610.285
• DOI: 10.1016/j.compbiomed.2021.105099
• Languages: English
• ISSNs: 0010-4825

Abstract

Abstract: 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. Highlights: Our method fully automatically and reconnect fragmented parts in X-ray angiography images. We first present and solve the important problem with the new angle to locate the missing fragments in coronary X-ray images. We designed a framework that utilizes deep neural networks and a geometric distribution to reconnect fragmented parts of coronary arteries. The proposed method can be simply integrated with the other vessel segmentation methods.