Abstract
Cardiac magnetic resonance imaging (CMRI) provides high resolution images ideal for assessing cardiac function and diagnosis of cardiovascular diseases. To assess cardiac function, estimation of ejection fraction, ventricular volume, mass and stroke volume are crucial, and the segmentation of left ventricle from CMRI is the first critical step. Fully convolutional neural network architectures have proved to be very efficient for medical image segmentation, with U-Net inspired architecture as the current state-of-the-art. Generative adversarial networks (GAN) inspired architectures have recently gained popularity in medical image segmentation with one of them being SegAN, a novel end-to-end adversarial neural network architecture. In this paper, we investigate SegAN with three different types of U-Net inspired architectures for left ventricle segmentation from cardiac MRI data. We performed our experiments on the 2017 ACDC segmentation challenge dataset. Our results show that the performance of U-Net architectures is better when trained in the SegAN framework than when trained stand-alone. The mean Dice scores achieved for three different U-Net architectures trained in the SegAN framework was on the order of 93.62%, 92.49% and 94.57%, showing a significant improvement over their Dice scores following stand-alone training - 92.58%), 91.46% and 93.81%, respectively.