Quantification of coronary artery stenosis on X-ray angiography (XRA) images is of great importance during the intraoperative treatment of coronary artery disease. It serves to quantify the coronary artery stenosis by estimating the clinical morphological indices, which are essential in clinical decision making. However, stenosis quantification is still a challenging task due to the overlapping, diversity and small-size region of the stenosis in the XRA images. While efforts have been devoted to stenosis quantification through low-level features, these methods have difficulty in learning the real mapping from these features to the stenosis indices. These methods are still cumbersome and unreliable for the intraoperative procedures due to their two-phase quantification, which depends on the results of segmentation or reconstruction of the coronary artery. In this work, we are proposing a hierarchical attentive multi-view learning model (HEAL) to achieve a direct quantification of coronary artery stenosis, without the intermediate segmentation or reconstruction. We have designed a multi-view learning model to learn more complementary information of the stenosis from different views. For this purpose, an intra-view hierarchical attentive block is proposed to learn the discriminative information of stenosis. Additionally, a stenosis representation learning module is developed to extract the multi-scale features from the keyframe perspective for considering the clinical workflow. Finally, the morphological indices are directly estimated based on the multi-view feature embedding. Extensive experiment studies on clinical multi-manufacturer dataset consisting of 228 subjects show the superiority of our HEAL against nine comparing methods, including direct quantification methods and multi-view learning methods. The experimental results demonstrate the better clinical agreement between the ground truth and the prediction, which endows our proposed method with a great potential for the efficient intraoperative treatment of coronary artery disease.

中文翻译：

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通过分层专心的多视图学习直接量化冠状动脉狭窄。

在X射线血管造影（XRA）图像上量化冠状动脉狭窄在冠状动脉疾病的术中治疗中非常重要。它通过估计临床形态指标来量化冠状动脉狭窄，这对于临床决策至关重要。但是，由于XRA图像中狭窄的重叠，多样性和狭窄区域，狭窄量化仍然是一项艰巨的任务。尽管已经致力于通过低级特征进行狭窄量化，但是这些方法在学习从这些特征到狭窄指数的真实映射方面有困难。这些方法由于分两阶段进行定量分析，因此在手术过程中仍然很麻烦且不可靠，这取决于冠状动脉分割或重建的结果。在这项工作中，我们提出一种分层的注意力多视图学习模型（HEAL），以实现对冠状动脉狭窄的直接量化，而无需进行中间分割或重建。我们设计了一种多视图学习模型，以从不同的视图中学习更多有关狭窄的补充信息。为此目的，提出了一种视图内分层注意块来学习狭窄的鉴别信息。此外，还开发了狭窄表示学习模块以从关键帧角度提取多尺度特征，以考虑临床工作流程。最后，基于多视图特征嵌入直接估计形态指标。对由228名受试者组成的临床多厂商数据集进行的广泛实验研究表明，我们的HEAL相对于九种比较方法（包括直接定量方法和多视图学习方法）具有优越性。实验结果证明了地面真实性和预测之间更好的临床一致性，这为我们提出的方法提供了有效的术中治疗冠状动脉疾病的巨大潜力。