Deep learning based detection of collateral circulation in coronary angiographies

• URL: http://arxiv.org/abs/2403.12055v1
• Date: Mon, 8 Jan 2024 11:25:42 GMT
• Title: Deep learning based detection of collateral circulation in coronary angiographies
• Authors: Cosmin-Andrei Hatfaludi, Daniel Bunescu, Costin Florian Ciusdel, Alex Serban, Karl Bose, Marc Oppel, Stephanie Schroder, Christopher Seehase, Harald F. Langer, Jeanette Erdmann, Henry Nording, Lucian Mihai Itu,
• Abstract summary: Coronary artery disease (CAD) is the dominant cause of death and hospitalization across the globe. We propose a novel deep learning based method to detect coronary collateral circulation (CCC) in angiographic images. Our method relies on a convolutional backbone to extract features from each frame of an angiography sequence.
• Score: 0.0
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Coronary artery disease (CAD) is the dominant cause of death and hospitalization across the globe. Atherosclerosis, an inflammatory condition that gradually narrows arteries and has potentially fatal effects, is the most frequent cause of CAD. Nonetheless, the circulation regularly adapts in the presence of atherosclerosis, through the formation of collateral arteries, resulting in significant long-term health benefits. Therefore, timely detection of coronary collateral circulation (CCC) is crucial for CAD personalized medicine. We propose a novel deep learning based method to detect CCC in angiographic images. Our method relies on a convolutional backbone to extract spatial features from each frame of an angiography sequence. The features are then concatenated, and subsequently processed by another convolutional layer that processes embeddings temporally. Due to scarcity of data, we also experiment with pretraining the backbone on coronary artery segmentation, which improves the results consistently. Moreover, we experiment with few-shot learning to further improve performance, given our low data regime. We present our results together with subgroup analyses based on Rentrop grading, collateral flow, and collateral grading, which provide valuable insights into model performance. Overall, the proposed method shows promising results in detecting CCC, and can be further extended to perform landmark based CCC detection and CCC quantification.

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