Segmentation and Quantification of Bi-Ventricles and Myocardium Using 3D SERes-U-Net

Abstract

Automatic cardiac MRI segmentation, including left and right ventricular endocardium and epicardium, has an essential role in clinical diagnosis by providing crucial information about cardiac function. Determining heart chamber properties, such as volume or ejection fraction, directly relies on their accurate segmentation. In this work, we propose a new automatic method for the segmentation of myocardium, left, and right ventricles from MRI images. We introduce a new architecture that incorporates SERes blocks into 3D U-net architecture (3D SERes-U-Net). The SERes blocks incorporate squeeze-and-excitation operations into residual learning. The adaptive feature recalibration ability of squeeze-and-excitation operations boosts the network's representational power while feature reuse utilizes effective learning of the features, which improves segmentation performance. We evaluate the proposed method on the testing dataset of the MICCAI Automated Cardiac Diagnosis Challenge (ACDC) dataset and obtain highly comparable results to the state-of-the-art methods.