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

arXiv:2404.13106v1 Announce Type: cross
Abstract: 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 …

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