  We aim at incorporating explicit shape information into current 3D organ
segmentation models. Different from previous works, we formulate shape learning
as an in-painting task, which is named Masked Label Mask Modeling (MLM).
Through MLM, learnable mask tokens are fed into transformer blocks to complete
the label mask of organ. To transfer MLM shape knowledge to target, we further
propose a novel shape-aware self-distillation with both in-painting
reconstruction loss and pseudo loss. Extensive experiments on five public organ
segmentation datasets show consistent improvements over prior arts with at
least 1.2 points gain in the Dice score, demonstrating the effectiveness of our
method in challenging unsupervised domain adaptation scenarios including: (1)
In-domain organ segmentation; (2) Unseen domain segmentation and (3) Unseen
organ segmentation. We hope this work will advance shape analysis and geometric
learning in medical imaging.
