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.
How to access this content:
For one year after publication, abstracts and videos are only open to registrants of this annual meeting. Registrants should use their existing login information. Non-registrant access can be purchased via the ISMRM E-Library.
After one year, current ISMRM & ISMRT members get free access to both the abstracts and videos. Non-members and non-registrants must purchase access via the ISMRM E-Library.
After two years, the meeting proceedings (abstracts) are opened to the public and require no login information. Videos remain behind password for access by members, registrants and E-Library customers.
Keywords