Segmentation of Late Gadolinium-Enhanced Cardiac Magnetic Resonance Images Based on Residual Attention Mechanisms

Accurate automated segmentation of late gadolinium-enhanced cardiac magnetic resonance (LGE-CMR) images is crucial for personalized cardiac modeling, particularly for myocardial boundaries. This study aims to validate the accuracy of our proposed deep learning segmentation method for the left ventricular blood cavity (LV) and left ventricular myocardium (Myo).We propose a residual attention-based nnUNetv2 to automatically segment the LV and Myo.The residual attention mechanism enables the network to focus on more informative channels, enhancing discriminative learning and increasing the network's ability to segment the LV and Myo boundary.We also propose an image interpolation method that interpolates the voxel spacing of the original image and increases the number of image layers.Our proposed method achieved a Dice similarity coefficient (DSC) of 85.19% for LV and 72.12% for Myo.The lower DSC score for Myo is primarily due to the poor image quality of the LGE-CMR.The results demonstrate that our proposed residual attention-based nnUNetv2 network can effectively improve the network's ability to segment LV and Myo in LGE-CMR images.