基于深度学习的心室和心肌心脏MRI语义分割
Cardiac MRI Semantic Segmentation for Ventricles and Myocardium using Deep Learning
AI总结：
针对心脏MRI语义分割任务，提出一种基于U-Net的改进模型，通过下采样提取边缘与上下文信息并在上采样注入，提升左/右心室腔及左心室心肌分割精度，DSC提高2%-11%、HD降低1.6-5.7mm。
摘要：
自动化无创心脏诊断在心脏疾病的早期检测和具有成本效益的临床管理中起着关键作用。自动化诊断涉及心脏图像的自动分割和分析。心脏亚结构的精确描绘及其形态学属性的提取对于评估心脏功能、诊断心肌病、瓣膜病、室间隔穿孔相关异常和血流速率等心血管疾病至关重要。语义分割在像素级别标记心脏磁共振（CMR）图像，并定位其亚成分，以促进异常检测，包括存在肌肉异常、血管异常和瓣膜异常的老化心脏中的室壁运动异常。在本文中，我们描述了一个改进CMR图像语义分割的模型。该模型在U-Net下采样期间提取边缘属性和上下文信息，并在上采样期间注入这些信息，以定位三个主要心脏结构：左心室腔（LV）、右心室腔（RV）和左心室心肌（LMyo）。我们展示了算法和性能结果。通过使用实际图像与分割图像之间的相似性指标，将我们的模型与先前的领先模型进行比较，结果表明我们的方法将Dice相似系数（DSC）提高了2%-11%，并将豪斯多夫距离（HD）降低了1.6至5.7毫米。
英文摘要：
Automated noninvasive cardiac diagnosis plays a critical role in the early detection of cardiac disorders and cost-effective clinical management. Automated diagnosis involves the automated segmentation and analysis of cardiac images. Precise delineation of cardiac substructures and extraction of their morphological attributes are essential for evaluating the cardiac function, and diagnosing cardiovascular disease such as cardiomyopathy, valvular diseases, abnormalities related to septum perforations, and blood-flow rate. Semantic segmentation labels the CMR image at the pixel level, and localizes its subcomponents to facilitate the detection of abnormalities, including abnormalities in cardiac wall motion in an aging heart with muscle abnormalities, vascular abnormalities, and valvular abnormalities. In this paper, we describe a model to improve semantic segmentation of CMR images. The model extracts edge-attributes and context information during down-sampling of the U-Net and infuses this information during up-sampling to localize three major cardiac structures: left ventricle cavity (LV); right ventricle cavity (RV); and LV myocardium (LMyo). We present an algorithm and performance results. A comparison of our model with previous leading models, using similarity metrics between actual image and segmented image, shows that our approach improves Dice similarity coefficient (DSC) by 2%-11% and lowers Hausdorff distance (HD) by 1.6 to 5.7 mm.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
