Fully Automatic Segmentation Of Short-Axis Cardiac MRI Using Modified Deep Layer Aggregation

Abstract

Delineation of right ventricular cavity (RVC), left ventricular myocardium (LVM) and left ventricular cavity (LVC) are common tasks in the clinical diagnosis of cardiac related diseases, especially in the basis of advanced magnetic resonance imaging (MRI) techniques. Recently, despite deep learning techniques being widely employed in solving segmentation tasks in a variety of medical images, the sheer volume and complexity of the data in some applications such as cine cardiac MRI pose significant challenges for the accurate and efficient segmentation. In cine cardiac MRI we need to segment both short and long axis 2D images. In this paper, we focus on the automated segmentation of short-axis cardiac MRI images. We first introduce the deep layer aggregation (DLA) method to augment the standard deep learning architecture with deeper aggregation to better fuse information across layers, which is particularly suitable for the cardiac MRI segmentation, due to the complexity of the cardiac boundaries appearance and acquisition resolution during a cardiac cycle. In our solution, we develop a modified DLA framework by embedding Refinement Residual Block (RRB) and Channel Attention Block (CAB). Experimental results validate the superior performance of our proposed method for the cardiac structures segmentation in comparison with state-of-the-art. Moreover, we demonstrate its potential use case in the quantitative analysis of cardiac dyssynchrony.