Unsupervised Multimodal Style Transfer for Cardiac MR Segmentation In this work, we present a fully automatic method to segment cardiac structures from lategadolinium enhanced (LGE) images without using labelled LGE data for training, but instead by transferring the anatomical knowledge and features learned on annotated balanced steadystate free precession (bSSFP) images, which are easier to acquire. Our framework mainly consists of two neural networks: a multimodal image translation network for style transfer and a cascaded segmentation network for image segmentation. The multimodal image translation network generates realistic and diverse synthetic LGE images conditioned on a single annotated bSSFP image, forming a synthetic LGE training set. This set is then utilized to finetune the segmentation network pretrained on labelled bSSFP images, achieving the goal of unsupervised LGE image segmentation. In particular, the proposed cascaded segmentation network is able to produce accurate segmentation by taking both shape prior and image appearance into account, achieving an average Dice score of 0.92 for the left ventricle, 0.83 for the myocardium, and 0.88 for the right ventricle on the test set.

Segmentation of Multimodal Myocardial Images Using ShapeTransfer GAN Myocardium segmentation of late gadolinium enhancement (LGE) Cardiac MR images is important for evaluation of infarction regions in clinical practice. The pathological myocardium in LGE images presents distinctive brightness and textures compared with the healthy tissues, making it much more challenging to be segment. Instead, the balancedSteady State Free Precession (bSSFP) cine images show clearly boundaries and can be easily segmented. Given this fact, we propose a novel shapetransfer GAN for LGE images, which can (1) learn to generate realistic LGE images from bSSFP with the anatomical shape preserved, and (2) learn to segment the myocardium of LGE images from these generated images. Itxe2x80x99s worth to note that no segmentation label of the LGE images is used during this procedure. We test our model on dataset from the Multisequence Cardiac MR Segmentation Challenge. The results show that the proposed ShapeTransfer GAN can achieve accurate myocardium masks of LGE images.