  Shape information is a strong and valuable prior in segmenting organs in
medical images. However, most current deep learning based segmentation
algorithms have not taken shape information into consideration, which can lead
to bias towards texture. We aim at modeling shape explicitly and using it to
help medical image segmentation. Previous methods proposed Variational
Autoencoder (VAE) based models to learn the distribution of shape for a
particular organ and used it to automatically evaluate the quality of a
segmentation prediction by fitting it into the learned shape distribution.
Based on which we aim at incorporating VAE into current segmentation pipelines.
Specifically, we propose a new unsupervised domain adaptation pipeline based on
a pseudo loss and a VAE reconstruction loss under a teacher-student learning
paradigm. Both losses are optimized simultaneously and, in return, boost the
segmentation task performance. Extensive experiments on three public Pancreas
segmentation datasets as well as two in-house Pancreas segmentation datasets
show consistent improvements with at least 2.8 points gain in the Dice score,
demonstrating the effectiveness of our method in challenging unsupervised
domain adaptation scenarios for medical image segmentation. We hope this work
will advance shape analysis and geometric learning in medical imaging.
