学习修补：用于3D器官分割的域自适应形状补全
Learning to In-paint: Domain Adaptive Shape Completion for 3D Organ Segmentation
AI总结：
本文提出将形状学习建模为MLM修补任务，并通过形状感知自蒸馏迁移形状知识，在五个器官分割数据集上于多种无监督域适应场景中实现一致提升。
摘要：
我们旨在将显式形状信息融入当前的3D器官分割模型。与以往工作不同，我们将形状学习表述为一项修补任务，并将其命名为Masked Label Mask Modeling（MLM，掩码标签掩膜建模）。通过MLM，可学习的掩码标记被输入transformer块，以补全器官的标签掩膜。为了将MLM形状知识迁移到目标域，我们进一步提出了一种新颖的形状感知自蒸馏方法，同时包含修补重建损失和伪损失。在五个公开器官分割数据集上的大量实验表明，该方法相较现有方法取得了一致性提升，Dice分数至少提高1.2个点，证明了我们的方法在具有挑战性的无监督域适应场景中的有效性，这些场景包括：（1）域内器官分割；（2）未见域分割；（3）未见器官分割。我们希望这项工作能够推动医学影像中的形状分析和几何学习。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
