Abstract
In recent decades, coronary artery disease (CAD) is the leading cause of death worldwide. Therefore, automatic diagnostic methods are strongly necessary with the progressively increasing number of CAD patients. However, it is difficult for physicians to recognize the lesion from Coronary CT Angiography (CCTA) scans as the coronary plaques have complicated appearance and patterns. Previous studies are mostly based on the single image patch around a lesion, which are often limited by the field of view of the local sample patch. To address this problem, in this paper we propose a novel vessel-wise object detection method. Different with previous approaches, we directly input the whole curved planar reformation (CPR) volume along the coronary artery centerline into our deep learning network, and then predict the plaque type and stenosis degree simultaneously. This enables the network to learn the dependencies between distant locations. In addition, two cascade modules are used to decompose the challenging problem into two simpler tasks and this also yields better interpretability. We evaluated our method on a dataset of 1031 CCTA images. The experimental results demonstrated the efficacy of our presented approach.
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Zhang, Y., Ma, J., Li, J. (2022). Coronary R-CNN: Vessel-Wise Method for Coronary Artery Lesion Detection and Analysis in Coronary CT Angiography. In: Wang, L., Dou, Q., Fletcher, P.T., Speidel, S., Li, S. (eds) Medical Image Computing and Computer Assisted Intervention – MICCAI 2022. MICCAI 2022. Lecture Notes in Computer Science, vol 13433. Springer, Cham. https://doi.org/10.1007/978-3-031-16437-8_20
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