CraNeXt: Automatic Reconstruction of Skull Implants With Skull Categorization Technique

Abstract

Automatic cranial implant design aims to design a patient-specific implant where various machine-learning-based skull reconstruction techniques have been introduced to predict the implant. Despite the significant progress made in the previous research, the existing techniques often struggle to generalize to diverse clinical cases and may not fully leverage the latest advancements in deep learning architectures. Moreover, the limited availability of large-scale clinical datasets hinders the development of the models. In this paper, we represent a novel skull reconstruction model, CraNeXt, which utilizes a ConvNeXt backbone to achieve a 5.8x reduction in size when compared to 3DUNetCNN without sacrificing reconstruction quality. In addition, we introduce a novel method, skull categorization, to classify unlabeled skulls and determine the location of defects and the distribution of skull areas. We expand the training dataset by incorporating a larger collection of 328 in-house clinical cases, enabling the model to better capture the diversity of real-world cranial defects. CraNeXt demonstrates superior results with the skull categorization technique, achieving a dice score of $0.7969\pm 0.13$ on both public and in-house data. We perform a qualitative assessment of the predicted implants and discuss potential improvements to the skull reconstruction toward clinical use cases.

Keywords

• Skull reconstruction
• deep learning
• skull categorization
• autoimplant
• volumetric shape completion