  Although supervised deep-learning has achieved promising performance in
medical image segmentation, many methods cannot generalize well on unseen data,
limiting their real-world applicability. To address this problem, we propose a
deep learning-based Bayesian framework, which jointly models image and label
statistics, utilizing the domain-irrelevant contour of a medical image for
segmentation. Specifically, we first decompose an image into components of
contour and basis. Then, we model the expected label as a variable only related
to the contour. Finally, we develop a variational Bayesian framework to infer
the posterior distributions of these variables, including the contour, the
basis, and the label. The framework is implemented with neural networks, thus
is referred to as deep Bayesian segmentation. Results on the task of
cross-sequence cardiac MRI segmentation show that our method set a new state of
the art for model generalizability. Particularly, the BayeSeg model trained
with LGE MRI generalized well on T2 images and outperformed other models with
great margins, i.e., over 0.47 in terms of average Dice. Our code is available
at https://zmiclab.github.io/projects.html.
