A multi-sequence U-Net trained with CycleGAN-synthesized LGE images and rotation-based scar augmentation achieves Dice scores of 0.90 (LV), 0.81 (MYO), and 0.87 (RV) on the MS-CMRSeg test set.
IEEE transactions on pattern analysis and machine intelli- gence (2018)
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Combining Multi-Sequence and Synthetic Images for Improved Segmentation of Late Gadolinium Enhancement Cardiac MRI
A multi-sequence U-Net trained with CycleGAN-synthesized LGE images and rotation-based scar augmentation achieves Dice scores of 0.90 (LV), 0.81 (MYO), and 0.87 (RV) on the MS-CMRSeg test set.