Quantitative analysis of hyperpolarized 129Xe MRI, segmentation of the thoracic cavity, a crucial step that is often the bottleneck in an otherwise fully automated pipeline. This problem is attractive to solve using deep learning methods, but they are limited by their large appetite for manually segmented training data. To this end, we propose a method to automatically synthesize both 129Xe ventilation MR images and their corresponding thoracic cavity masks using general adversarial networks. This data augmentation technique can accelerate the training of deep learning segmentation models.
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