Abstract
Coronary stenosis is a major risk factor for ischemic heart events leading to increased mortality, and medical treatments for this condition require meticulous, labor-intensive analysis. Coronary angiography provides critical visual cues for assessing stenosis, supporting clinicians in making informed decisions for diagnosis and treatment. Recent advances in deep learning have shown great potential for automated localization and severity measurement of stenosis. In real-world scenarios, however, the success of these competent approaches is often hindered by challenges such as limited labeled data and class imbalance. In this study, we propose a novel data augmentation approach that uses an inpainting method based on a diffusion model to generate realistic lesions, allowing user-guided control of severity. Extensive evaluation on lesion detection and severity classification across various synthetic dataset sizes shows superior performance of our method on both a large-scale in-house dataset and a public coronary angiography dataset. Furthermore, our approach maintains high detection and classification performance even when trained with limited data, highlighting its clinical importance in improving the assessment of severity of stenosis and optimizing data utilization for more reliable decision support.
Abstract (translated)
冠状动脉狭窄是导致缺血性心脏病事件并增加死亡率的主要风险因素，治疗这种状况的医疗方法需要仔细且耗时的分析。冠状动脉造影为评估狭窄提供了关键的视觉线索，支持临床医生做出明智的诊断和治疗决策。近年来，深度学习的进步在自动定位和测量狭窄程度方面显示出巨大的潜力。然而，在实际应用中，由于标注数据有限及类别不平衡等问题，这些方法的成功往往受到阻碍。 在此研究中，我们提出了一种新的数据增强方法，该方法利用基于扩散模型的修复技术生成逼真的病变，并允许用户根据严重程度进行引导控制。在不同合成数据集大小下的病灶检测和严重度分类广泛评估表明，在大规模内部数据集以及公开冠状动脉造影数据集中，我们的方法表现出了优越性能。此外，即使使用有限的数据训练时，我们这种方法仍能保持高水平的检出率和分类精度，突显其在改善狭窄严重程度评估中的临床重要性，并优化数据利用以提供更可靠的决策支持。
URL
https://arxiv.org/abs/2508.00438
