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