Multimodal RU-Net: A Segmentation Network Based on Multimodal Cardiac CMR Images

Cardiac magnetic resonance imaging (CMR) is crucial for the pathological segmentation of the central muscle in the diagnosis of myocardial infarction (MI) patients. The automatic segmentation technology of medical images has achieved significant success with the development of deep learning technology. However, due to the low contrast of the target area edges, irregular lesion areas, and insufficient medical image data, automatic segmentation of myocardial pathology still has great challenges. In this article, we propose an improved RU-Net model called a Multimodal RU- -Net. Used to segment edema and scar areas in multimodal cardiac CMR data. In this network, we use RU-Net as the basic model, embed our proposed multimodal image feature extraction module (MFF) in the encoding path to enhance the extraction of complementary information between different modal images, and add attention modules in the skip connection and encoding path to enhance attention to the regions of interest in the image. The experiment shows that both modules mentioned above effectively improve segmentation accuracy. In addition, we have adopted methods such as data augmentation, deep supervision, and combination loss to further improve segmentation accuracy. We evaluated multimodal RU-Net on the MyoPS2020 challenge dataset and achieved a Dice score of 64.4% in scar segmentation and 70.7% in edema and scar segmentation. We achieved almost equivalent performance to the most advanced single stage segmentation methods on the MyoPS 2020 ranking, and our proposed method outperformed it in the standard deviation of dice scores, The test results are more stable. This indicates that our proposed method is meaningful for automatic segmentation of myocardial pathology.