Upendra Roshan Reddy, Dangi Shusil, Linte Cristian A
Center for Imaging Science, Rochester Institute of Technology, Rochester, NY, USA.
Biomedical Engineering, Rochester Institute of Technology, Rochester, NY, USA.
Proc SPIE Int Soc Opt Eng. 2020 Feb;11315. doi: 10.1117/12.2550656. Epub 2020 Mar 16.
Cine cardiac magnetic resonance imaging (CMRI), the current gold standard for cardiac function analysis, provides images with high spatio-temporal resolution. Computing clinical cardiac parameters like ventricular blood-pool volumes, ejection fraction and myocardial mass from these high resolution images is an important step in cardiac disease diagnosis, therapy planning and monitoring cardiac health. An accurate segmentation of left ventricle blood-pool, myocardium and right ventricle blood-pool is crucial for computing these clinical cardiac parameters. U-Net inspired models are the current state-of-the-art for medical image segmentation. SegAN, a novel adversarial network architecture with multi-scale loss function, has shown superior segmentation performance over U-Net models with single-scale loss function. In this paper, we compare the performance of stand-alone U-Net models and U-Net models in SegAN framework for segmentation of left ventricle blood-pool, myocardium and right ventricle blood-pool from the 2017 ACDC segmentation challenge dataset. The mean Dice scores achieved by training U-Net models was on the order of 89.03%, 89.32% and 88.71% for left ventricle blood-pool, myocardium and right ventricle blood-pool, respectively. The mean Dice scores achieved by training the U-Net models in SegAN framework are 91.31%, 88.68% and 90.93% for left ventricle blood-pool, myocardium and right ventricle blood-pool, respectively.
心脏磁共振成像（CMRI）是目前心脏功能分析的金标准，可提供具有高时空分辨率的图像。从这些高分辨率图像中计算诸如心室血池容积、射血分数和心肌质量等临床心脏参数，是心脏病诊断、治疗规划和心脏健康监测中的重要步骤。准确分割左心室血池、心肌和右心室血池对于计算这些临床心脏参数至关重要。受U-Net启发的模型是目前医学图像分割的最先进技术。SegAN是一种具有多尺度损失函数的新型对抗网络架构，已显示出比具有单尺度损失函数的U-Net模型更优的分割性能。在本文中，我们比较了独立U-Net模型和SegAN框架中的U-Net模型在从2017年ACDC分割挑战数据集中分割左心室血池、心肌和右心室血池方面的性能。训练U-Net模型分别在左心室血池、心肌和右心室血池上获得的平均Dice分数约为89.03%、89.32%和88.71%。在SegAN框架中训练U-Net模型分别在左心室血池、心肌和右心室血池上获得的平均Dice分数为91.31%、88.68%和90.93%。
