Cardiac Magnetic Resonance Imaging Segmentation using Ensemble of 2D and 3D Deep Residual U-Net

Abstract

The domain of deep learning stimulates medical image analysis, which is a catalyst of scientific research and an essential element of healthcare. Since semantic segmentation techniques empower image processing and quantitative determination in various applications, designing a dedicated solution is challenging and heavily reliant on input data characteristics and hardware constraints. Cardiac magnetic resonance imaging segmentation provides three essential heart structures, left ventricle (LV) cavity, myocardium (MYO), and right ventricle (RV) cavity. In clinical applications, manual contouring is frequently utilized to perform semantic segmentation. A fully automated cardiac magnetic resonance image (CMRI) segmentation technique is becoming more desirable as deep learning-based frameworks advance. Motivated by the power of U-Net, residual network, and deep supervision, this paper proposes a Deep Residual U-Net to achieve better LV, MYO, and RV segmentation in short-axis CMRI. The model is formed with residual connections and has a similar layout to U-Net. It provides three advantages: First, residual connections make deep network training easier. Second, the network's rich skip connections enable feature propagation, allowing for network creation with lower complexity but more remarkable performance. Third, Deep supervision facilitates the loss computation at every feature dimension except at least two, empowering gradients to be implanted more deeply in the network and improving each layer's training in Deep Residual U-Net. Automated Cardiac Diagnosis Challenge (ACDC) 2017 dataset has been used to assess the segmentation efficiency of proposed model. The proposed approach significantly outperformed all other methods, evidencing its supremacy over recent state-of-the-art U-Net-based techniques.