MMC-Net: Multi-modal network for cardiac MRI segmentation of ventricular structures, and myocardium

Abstract

<p>Automatic segmentation of multi-modal Cardiac Magnetic Resonance Imaging (CMRI) scans is challenging due to the variant intensity distribution and unclear boundaries between the neighbouring tissues and other organs. The deep convolutional neural networks have shown great potential in medical image segmentation tasks. In this paper, we present a deep convolutional neural network model named Multi-Modal Cardiac Network (MMC-Net) for segmenting three cardiac structures namely right ventricle (RV), left ventricle (LV), and left ventricular myocardium (LVM) from multi-modal CMRI’s. The proposed MMC-Net is designed using a densely connected backbone enabling feature reuse, an atrous convolution module for fusing multi-scale features, and a pixel-classification module for generating the segmentation result. This model was evaluated on a publicly available MS-CMRSeg-2019 challenge dataset in segmentation of RV, LV, and LVM from CMRI scans. The segmentation results from extensive experiments demonstrate our MMC-Net can achieve better segmentation performance compared to other state-of-the-art models, and the existing approaches. Additionally, the generalization ability of the proposed MMC-Net is validated on another publicly available ACDC dataset without fine-tuning. The results demonstrate that the proposed MMC-Net shows a powerful generalisation ability of segmenting RV, LV, and LVM with higher performance.</p>

An efficient deep learning algorithm for the segmentation of cardiac ventricles

“…MultiModal Network for Cardiac (MMC-Net) has been implemented by Jignesh Chowdary et al 27 for feature reuse by fusing multi-scale features using the densely connected blocks and segmentation of ventricles is done after pixel-wise classification.…”

“…The average DC metric obtained using SAC ARUNet3+ for the ACDC dataset is compared with the existing methods and is tabulated in Table 4. Compared with the best baseline model by Yogarajah et al, 27 The results of SAC ARUNet3+ for noisy and inhomogeneous CMRI for the SB and ACDC Datasets are shown in Figures 8 and 9, respectively.…”