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.

翻译：每年有数千人遭受颅骨损伤。他们需要在重建手术前设计和制造个性化植入体。手动设计昂贵且耗时，因此人们致力于寻找能够自动化该过程的算法。该问题可表述为三维形状补全任务，并可通过专用于监督式图像分割的深度神经网络解决。然而，此类方法需要对真实缺损进行标注，成本高昂且耗时。通常，该过程被替换为合成缺损生成。但即使合成真实数据也较为耗时，且限制了数据异质性，从而影响了深度模型的泛化能力。在本研究中，我们提出一种替代性简单方案，利用自监督掩码自动编码器解决该问题。该方法通过设计增加了训练集的异质性，可视为一种数据增强形式。我们将所提方法与多种先进深度神经网络进行比较，在SkullBreak和SkullFix数据集上展示了定量与定性改进。所提方法可用于实时高效重建颅骨缺损。