Combining MultiSequence and Synthetic Images for Improved Segmentation of Late Gadolinium Enhancement Cardiac MRI Accurate segmentation of the cardiac boundaries in late gadolinium enhancement magnetic resonance images (LGEMRI) is a fundamental step for accurate quantification of scar tissue. However, while there are many solutions for automatic cardiac segmentation of cine images, the presence of scar tissue can make the correct delineation of the myocardium in LGEMRI challenging even for human experts. As part of the MultiSequence Cardiac MR Segmentation Challenge, we propose a solution for LGEMRI segmentation based on two components. First, a generative adversarial network is trained for the task of modalitytomodality translation between cine and LGEMRI sequences to obtain extra synthetic images for both modalities. Second, a deep learning model is trained for segmentation with different combinations of original, augmented and synthetic sequences. Our results based on three magnetic resonance sequences (LGE, bSSFP and T2) from 45 different patients show that the multisequence model training integrating synthetic images and data augmentation improves in the segmentation over conventional training with real datasets. In conclusion, the accuracy of the segmentation of LGEMRI images can be improved by using complementary information provided by noncontrast MRI sequences.

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.