Abstract
Automated anatomical labeling plays an essential role in the effort of developing a computer-aided diagnosing system for coronary artery diseases. The large variation between individuals, presence of vascular stenosis or blockages, and possible image degradations makes the problem extremely challenging. In this paper, we propose a hybrid approach that combines the strength of traditional rule-based methods and recent deep learning methods, ensuring interpretability and consistency of the labeling process while being capable of learning parameters in a data-driven scheme. Our method is composed of two major components. First, we present our method of artery tree building from possibly noisy initial segmentation. The proposed algorithm links missing vessel segments while reducing noise, resulting in a complete and clean centerline based artery tree representation. Next, our labeling algorithm combining gated graph convolutional network (GGCN) and logical rules is elaborated. Experiments have demonstrated encouraging results both for completeness of artery tree building and accuracy of anatomical artery labeling.
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Disclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.