Data Augmentation with Generative Deep Learning for Automatic Bone Segmentation from Fat Fraction MRI

Motivation: Measurements of bone marrow adiposity (BMA) from proton density fat fraction (PDFF) maps could improve our understanding of bone fragility. Automatic analysis requires segmentation of the bone(s) from these images. Goal(s): To develop an accurate deep learning model for automatically segmenting bone from PDFF maps. Approach: We use a cycleGAN to create an image translation network that converts CT images into synthetic fat fraction images. We augment the training set of the segmentation model with this synthetic data. Results: We show that we are able to achieve a lower binary cross entropy loss when segmentation is used with the synthetic data augmentation. Impact: With an automatic bone segmentation algorithm from fat fraction MR images, future work will conduct a thorough investigation into how bone marrow adiposity affects bone fragility.