  Brain network analysis for traumatic brain injury (TBI) patients is critical
for its consciousness level assessment and prognosis evaluation, which requires
the segmentation of certain consciousness-related brain regions. However, it is
difficult to construct a TBI segmentation model as manually annotated MR scans
of TBI patients are hard to collect. Data augmentation techniques can be
applied to alleviate the issue of data scarcity. However, conventional data
augmentation strategies such as spatial and intensity transformation are unable
to mimic the deformation and lesions in traumatic brains, which limits the
performance of the subsequent segmentation task. To address these issues, we
propose a novel medical image inpainting model named TBI-GAN to synthesize TBI
MR scans with paired brain label maps. The main strength of our TBI-GAN method
is that it can generate TBI images and corresponding label maps simultaneously,
which has not been achieved in the previous inpainting methods for medical
images. We first generate the inpainted image under the guidance of edge
information following a coarse-to-fine manner, and then the synthesized
intensity image is used as the prior for label inpainting. Furthermore, we
introduce a registration-based template augmentation pipeline to increase the
diversity of the synthesized image pairs and enhance the capacity of data
augmentation. Experimental results show that the proposed TBI-GAN method can
produce sufficient synthesized TBI images with high quality and valid label
maps, which can greatly improve the 2D and 3D traumatic brain segmentation
performance compared with the alternatives.
