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.

本研究旨在将明确的形状信息纳入现有的三维器官分割模型中，并将形状学习作为补全任务来进行，被称为 Masked Label Mask Modeling (MLM)。我们进一步提出了一种新颖的形状感知自蒸馏方法，其中包括补全重建损失和伪损失。在五个公开器官分割数据集上进行的大量实验证明了我们方法在具有挑战性的无监督领域自适应场景中的有效性，包括：（1）领域内器官分割；（2）未见领域分割和（3）未见器官分割。我们希望这项工作能推动医学成像中的形状分析和几何学习。

学习修复：用于三维器官分割的领域自适应形状补全

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

Shape learning, or the ability to leverage shape information, could be a
desirable property of convolutional neural networks (CNNs) when target objects
have specific shapes. While some research on the topic is emerging, there is no
systematic study to conclusively determine whether and under what circumstances
CNNs learn shape. Here, we present such a study in the context of segmentation
networks where shapes are particularly important. We define shape and propose a
new behavioral metric to measure the extent to which a CNN utilizes shape
information. We then execute a set of experiments with synthetic and real-world
data to progressively uncover under which circumstances CNNs learn shape and
what can be done to encourage such behavior. We conclude that (i) CNNs do not
learn shape in typical settings but rather rely on other features available to
identify the objects of interest, (ii) CNNs can learn shape, but only if the
shape is the only feature available to identify the object, (iii) sufficiently
large receptive field size relative to the size of target objects is necessary
for shape learning; (iv) a limited set of augmentations can encourage shape
learning; (v) learning shape is indeed useful in the presence of
out-of-distribution data.

通过一系列实验，发现在典型的设置下 CNN 并不会学习物体的形状，而是依赖其他特征，但只有在形状是识别物体的唯一特征时，CNN 才能学习形状。此外，适当的感受野大小相对于目标物体的大小和有限的扩充能够鼓励形状学习，并且在存在超出分布的数据时，学习形状是确实有用的。

卷积神经网络在语义分割任务中很少学习形状信息

Convolutional Neural Networks Rarely Learn Shape for Semantic  Segmentation

We present Neural Generalized Implicit Functions(Neural-GIF), to animate
people in clothing as a function of the body pose. Given a sequence of scans of
a subject in various poses, we learn to animate the character for new poses.
Existing methods have relied on template-based representations of the human
body (or clothing). However such models usually have fixed and limited
resolutions, require difficult data pre-processing steps and cannot be used
with complex clothing. We draw inspiration from template-based methods, which
factorize motion into articulation and non-rigid deformation, but generalize
this concept for implicit shape learning to obtain a more flexible model. We
learn to map every point in the space to a canonical space, where a learned
deformation field is applied to model non-rigid effects, before evaluating the
signed distance field. Our formulation allows the learning of complex and
non-rigid deformations of clothing and soft tissue, without computing a
template registration as it is common with current approaches. Neural-GIF can
be trained on raw 3D scans and reconstructs detailed complex surface geometry
and deformations. Moreover, the model can generalize to new poses. We evaluate
our method on a variety of characters from different public datasets in diverse
clothing styles and show significant improvements over baseline methods,
quantitatively and qualitatively. We also extend our model to multiple shape
setting. To stimulate further research, we will make the model, code and data
publicly available at: this https URL

利用神经广义隐函数技术（Neural-GIF），通过学习解决了利用模板方法难以处理的服装模拟问题，在非刚性形状学习中，灵活地建立了可以学习服装及软组织的复杂非刚性变形，并且可以通过学习处理 3D 扫描数据，并用于多种不同的形状设置。