Transthoracic echo is one of the most common means of cardiac studies in the clinical routines. During the echo exam, the sonographer captures a set of standard cross sections (echo views) of the heart. Each 2D echo view cuts through the 3D cardiac geometry via a unique plane. Consequently, different views share some limited information. In this work, we investigate the feasibility of generating a 2D echo view using another view based on adversarial generative models. The objective optimized to train the view-conversion model is based on the ideas introduced by LSGAN, PatchGAN and Conditional GAN (cGAN). The size and length of the left ventricle in the generated target echo view is compared against that of the target ground-truth to assess the validity of the echo view conversion. Results show that there is a correlation of 0.50 between the LV areas and 0.49 between the LV lengths of the generated target frames and the real target frames.
翻译：切换式回声是临床常规中最常见的心脏研究手段之一。在回声测试期间,声学学家捕捉了心脏的一组标准交叉部分(生态视图)。每个二维回声视图通过一个独特的平面通过三维心脏几何法截断。因此,不同观点共享一些有限的信息。在这项工作中,我们利用基于对抗性基因化模型的另一种观点调查产生二维回声视图的可行性。培训视图转换模型的优化目标是基于LSGAN、PatchGAN和Conditional GAN(cGAN)提出的想法。生成的目标回声视图中左心室的大小和长度与目标地面图的反响视图对比,以评估回声转换的有效性。结果显示,产生的目标框架的LV区域与LV长度之间有0.50的关联,实际目标框架之间的LV长度为0.49。
相关内容
微信扫码咨询专知VIP会员