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。