多视图超声心动图早期心肌梗死的检测(CS)

心肌梗死(MI)是世界上因供应心肌的冠状动脉堵塞而发生的主要死亡原因。心肌梗死的早期诊断和定位有助于早期治疗干预，从而减轻心肌损害的程度。冠状动脉阻塞后，缺血心肌段的局部壁运动异常(RWMA)是最早出现的改变。超声心动图是评估RWMA的基本工具。仅从单个超声心动图视图评估左心室(LV)壁的运动可能会导致错过心肌梗死的诊断，因为RWMA可能在该特定视图上看不到。因此，在本研究中，我们建议融合根尖4室(A4C)和根尖2室(A2C)视图，共可分析11个心肌节段来检测MI。该方法首先利用主动多项式(Active polynomial, APs)估计左室壁的运动，提取并跟踪心内膜边界，计算心肌段位移。A4C和A2C视图的特性提取位移,融合和送入分类器来检测心肌梗死。本研究的主要贡献是1)创建一个新的基准数据集包括A4C和A2C意见260超声心动图录音,公开与研究社区共享,2)利用基于机器学习的方法提高阈值ap的性能;3)利用融合A4C和A2C视图信息的多视图超声心动图MI检测方法。实验结果表明，该方法在多视图超声心动图上检测心肌梗死的灵敏度为90.91%，精度为86.36%。

原文题目：Early Myocardial Infarction Detection over Multi-view Echocardiography

原文：Myocardial infarction (MI) is the leading cause of mortality in the world that occurs due to a blockage of the coronary arteries feeding the myocardium. An early diagnosis of MI and its localization can mitigate the extent of myocardial damage by facilitating early therapeutic interventions. Following the blockage of a coronary artery, the regional wall motion abnormality (RWMA) of the ischemic myocardial segments is the earliest change to set in. Echocardiography is the fundamental tool to assess any RWMA. Assessing the motion of the left ventricle (LV) wall only from a single echocardiography view may lead to missing the diagnosis of MI as the RWMA may not be visible on that specific view. Therefore, in this study, we propose to fuse apical 4-chamber (A4C) and apical 2-chamber (A2C) views in which a total of 11 myocardial segments can be analyzed for MI detection. The proposed method first estimates the motion of the LV wall by Active Polynomials (APs), which extract and track the endocardial boundary to compute myocardial segment displacements. The features are extracted from the A4C and A2C view displacements, which are fused and fed into the classifiers to detect MI. The main contributions of this study are 1) creation of a new benchmark dataset by including both A4C and A2C views in a total of 260 echocardiography recordings, which is publicly shared with the research community, 2) improving the performance of the prior work of threshold-based APs by a Machine Learning based approach, and 3) a pioneer MI detection approach via multi-view echocardiography by fusing the information of A4C and A2C views. Experimental results show that the proposed method achieves 90.91% sensitivity and 86.36% precision for MI detection over multi-view echocardiography.