Zhang Dong, Yang Guang, Zhao Shu, Zhang Yanping, Ghista Dhanjoo, Zhang Heye, Li Shuo
IEEE Trans Med Imaging. 2020 Dec;39(12):4322-4334. doi: 10.1109/TMI.2020.3017275. Epub 2020 Nov 30.
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.
在冠心病的术中治疗过程中，对X射线血管造影（XRA）图像上的冠状动脉狭窄进行量化具有重要意义。它通过估计临床形态学指标来量化冠状动脉狭窄，这些指标在临床决策中至关重要。然而，由于XRA图像中狭窄区域的重叠、多样性和小尺寸，狭窄量化仍然是一项具有挑战性的任务。虽然已经致力于通过低级特征进行狭窄量化，但这些方法难以学习从这些特征到狭窄指标的真实映射。由于其两阶段量化依赖于冠状动脉分割或重建的结果，这些方法在术中操作中仍然繁琐且不可靠。在这项工作中，我们提出了一种分层注意力多视图学习模型（HEAL），以实现冠状动脉狭窄的直接量化，而无需中间分割或重建。我们设计了一个多视图学习模型，从不同视图学习更多关于狭窄的互补信息。为此，提出了一个视图内分层注意力块来学习狭窄的判别信息。此外，开发了一个狭窄表征学习模块，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学指标。对包含228名受试者的临床多制造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具有优势，包括直接量化方法和多视图学习方法。实验结果表明，真实情况与预测之间具有更好的临床一致性，这赋予了我们提出的方法在冠状动脉疾病高效术中治疗方面的巨大潜力。
