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To train a field strength agnostic cardiac segmentation network, we propose two novel augmentation techniques that allow us to transform 3T images to synthetic 7T images: by i) simulating $B_1$ distribution to approximate the 7T bias field and ii) style transfer using an unpaired 3T-to-7T GAN model. Data augmentation with these two methods improved the average Dice score over all classes by 22% and 25% respectively, on our 7T test dataset. Furthermore, the average performance on a 1.5T and 3T dataset were maintained.
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Wednesday 6th July
Poster Session 1.2 - onsite 11:00 - 12:00, virtual 15:20 - 16:20 (UTC+2)
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