Combining Multiple Style Transfer Networks and Transfer Learning For LGE-CMR Segmentation

This paper presents an algorithm for segmenting late gadolinium enhancement cardiac magnetic resonance (LGE-CMR) in the absence of labeled training data. The proposed method includes a data augmentation part and a segmentation network. Multiple style transfer networks are employed for data augmentation to increase the data diversity, and then the synthetic images are used for training an improved U-Net. Finally, the trained model is fine-tuned with a few LGE images and labels. Experiment results demonstrate the effectiveness and advantages of the proposed method.