In cardiology, ultrasound is often used to diagnose heart disease associated with myocardial infarction. This study aims to develop robust segmentation techniques for segmenting the left ventricle (LV) in ultrasound images to check myocardium movement during heartbeat. The proposed technique utilizes machine learning (ML) techniques such as the active contour (AC) and convolutional neural networks (CNNs) for segmentation. Medical experts determine the consistency between the proposed ML approach, which is a state-of-the-art deep learning method, and the manual segmentation approach. These methods are compared in terms of performance indicators such as the ventricular area (VA), ventricular maximum diameter (VMXD), ventricular minimum diameter (VMID), and ventricular long axis angle (AVLA) measurements. Furthermore, the Dice similarity coefficient, Jaccard index, and Hausdorff distance are measured to estimate the agreement of the LV segmented results between the automatic and visual approaches. The obtained results indicate that the proposed techniques for LV segmentation are useful and practical. There is no significant difference between the use of AC and CNN in image segmentation; however, the AC method could obtain comparable accuracy as the CNN method using less training data and less run-time.

在心脏病学中，超声通常用于诊断与心肌梗塞相关的心脏病。这项研究旨在开发强大的分割技术，以分割超声图像中的左心室（LV）以检查心跳期间的心肌运动。所提出的技术利用机器学习（ML）技术（例如活动轮廓（AC）和卷积神经网络（CNN））进行分割。医学专家确定建议的ML方法（一种最先进的深度学习方法）与手动分割方法之间的一致性。比较了这些方法的性能指标，例如心室面积（VA），心室最大直径（VMXD），心室最小直径（VMID）和心室长轴角（AVLA）测量值。此外，骰子相似系数 测量Jaccard指数和Hausdorff距离，以估计自动和可视方法之间的LV分割结果的一致性。获得的结果表明，所提出的用于LV分割的技术是有用和实用的。在图像分割中使用AC和CNN之间没有显着差异。但是，使用较少的训练数据和较少的运行时间，AC方法可以获得与CNN方法相当的准确性。