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

Learning to In-paint: Domain Adaptive Shape Completion for 3D Organ Segmentation