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.

提出了一种基于深度学习的方法用于检测冠状动脉侧支循环 (CCC) 在血管造影图像中的方法，并取得了有希望的结果，可以进一步扩展为基于标志点的 CCC 检测和 CCC 定量化。

基于深度学习的冠状动脉血运背侧循环检测

Deep learning based detection of collateral circulation in coronary  angiographies

Coronary Artery Diseases(CADs) though preventable are one of the leading
causes of death and disability. Diagnosis of these diseases is often difficult
and resource intensive. Segmentation of arteries in angiographic images has
evolved as a tool for assistance, helping clinicians in making accurate
diagnosis. However, due to the limited amount of data and the difficulty in
curating a dataset, the task of segmentation has proven challenging. In this
study, we introduce the idea of using pseudolabels as a data augmentation
technique to improve the performance of the baseline Yolo model. This method
increases the F1 score of the baseline by 9% in the validation dataset and by
3% in the test dataset.

冠状动脉疾病（CAD）是导致死亡和残疾的主要原因之一，其诊断常具有困难和资源密集性。本研究提出了使用伪标签作为数据增强技术来改进基线 Yolo 模型性能的想法，在验证数据集中将基线的 F1 分数提高了 9％，在测试数据集中提高了 3％。