Thousands of people suffer from cranial injuries every year. They require
personalized implants that need to be designed and manufactured before the
reconstruction surgery. The manual design is expensive and time-consuming
leading to searching for algorithms whose goal is to automatize the process.
The problem can be formulated as volumetric shape completion and solved by deep
neural networks dedicated to supervised image segmentation. However, such an
approach requires annotating the ground-truth defects which is costly and
time-consuming. Usually, the process is replaced with synthetic defect
generation. However, even the synthetic ground-truth generation is
time-consuming and limits the data heterogeneity, thus the deep models'
generalizability. In our work, we propose an alternative and simple approach to
use a self-supervised masked autoencoder to solve the problem. This approach by
design increases the heterogeneity of the training set and can be seen as a
form of data augmentation. We compare the proposed method with several
state-of-the-art deep neural networks and show both the quantitative and
qualitative improvement on the SkullBreak and SkullFix datasets. The proposed
method can be used to efficiently reconstruct the cranial defects in real time.

使用自监督遮蔽自编码器解决颅骨缺陷的问题，通过对比先进的深度神经网络，在 SkullBreak 和 SkullFix 数据集上展示了定量和定性的改进，实现实时有效重建颅骨缺陷。

自主训练的深度可变形蒙版自编码器在颅骨缺陷重建中的自动应用

Automatic Cranial Defect Reconstruction with Self-Supervised Deep  Deformable Masked Autoencoders

Each year thousands of people suffer from various types of cranial injuries
and require personalized implants whose manual design is expensive and
time-consuming. Therefore, an automatic, dedicated system to increase the
availability of personalized cranial reconstruction is highly desirable. The
problem of the automatic cranial defect reconstruction can be formulated as the
shape completion task and solved using dedicated deep networks. Currently, the
most common approach is to use the volumetric representation and apply deep
networks dedicated to image segmentation. However, this approach has several
limitations and does not scale well into high-resolution volumes, nor takes
into account the data sparsity. In our work, we reformulate the problem into a
point cloud completion task. We propose an iterative, transformer-based method
to reconstruct the cranial defect at any resolution while also being fast and
resource-efficient during training and inference. We compare the proposed
methods to the state-of-the-art volumetric approaches and show superior
performance in terms of GPU memory consumption while maintaining high-quality
of the reconstructed defects.

每年有数千人遭受不同类型的颅骨损伤，需要费时费力的手动设计个性化植入物。因此，迫切需要一种自动化、专用的系统来增加个性化颅骨重建的可用性。我们提出了一种基于迭代的变换器方法，将问题重新定义为点云完成任务，使其能够以任何分辨率快速高效地重建颅骨缺陷，同时在训练和推断过程中节省 GPU 内存，并与最先进的容积方法相比，表现出更优秀的性能和高质量的缺陷重建。