  We developed a new joint probabilistic segmentation and image distribution
matching generative adversarial network (PSIGAN) for unsupervised domain
adaptation (UDA) and multi-organ segmentation from magnetic resonance (MRI)
images. Our UDA approach models the co-dependency between images and their
segmentation as a joint probability distribution using a new structure
discriminator. The structure discriminator computes structure of interest
focused adversarial loss by combining the generated pseudo MRI with
probabilistic segmentations produced by a simultaneously trained segmentation
sub-network. The segmentation sub-network is trained using the pseudo MRI
produced by the generator sub-network. This leads to a cyclical optimization of
both the generator and segmentation sub-networks that are jointly trained as
part of an end-to-end network. Extensive experiments and comparisons against
multiple state-of-the-art methods were done on four different MRI sequences
totalling 257 scans for generating multi-organ and tumor segmentation. The
experiments included, (a) 20 T1-weighted (T1w) in-phase mdixon and (b) 20
T2-weighted (T2w) abdominal MRI for segmenting liver, spleen, left and right
kidneys, (c) 162 T2-weighted fat suppressed head and neck MRI (T2wFS) for
parotid gland segmentation, and (d) 75 T2w MRI for lung tumor segmentation. Our
method achieved an overall average DSC of 0.87 on T1w and 0.90 on T2w for the
abdominal organs, 0.82 on T2wFS for the parotid glands, and 0.77 on T2w MRI for
lung tumors.
