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.

翻译：每年有数千人遭受各种类型的颅骨损伤，需要个性化植入物，而人工设计这些植入物既昂贵又费时。因此，开发一套自动化的专用系统以提高个性化颅骨重建的可及性具有重要意义。颅骨缺损自动重建问题可被形式化为形状补全任务，并通过专用深度网络求解。当前最常用的方法是采用体素表示并应用图像分割专用深度网络。然而，该方法存在若干局限性：既难以扩展至高分辨率体素，也未考虑数据稀疏性。本研究将问题重新表述为点云补全任务，提出一种基于迭代Transformer的方法，能够在训练和推理过程中兼顾速度与资源效率，以任意分辨率重建颅骨缺损。我们将所提方法与当前最先进的体素方法进行对比，结果表明本方法在保持高质量缺损重建的同时，在GPU内存消耗方面具有显著优势。