Creating high-resolution 3D cranial implant geometry using deep learning techniques - Wu, Chieh-Tsai; Yang, Yao-Hung; Chang, Yau-Zen (Frontiers in Bioengineering and Biotechnology, 2023)
Creating a personalized implant for cranioplasty can be costly and aesthetically challenging, particularly for comminuted fractures that affect a wide area. Despite significant advances in deep learning techniques for 2D image completion, generating a 3D shape inpainting remains challenging due to the higher dimensionality and computational demands for 3D skull models. Here, we present a practical deep-learning approach to generate implant geometry from defective 3D skull models created from CT scans. Our proposed 3D reconstruction system comprises two neural networks that produce high-quality implant models suitable for clinical use while reducing training time. The first network repairs low-resolution defective models, while the second network enhances the volumetric resolution of the repaired model. We have tested our method in simulations and real-life surgical practices, producing implants that fit naturally and precisely match defect boundaries, particularly for skull defects above the Frankfort horizontal plane. This is a provisional file, not the final typeset article [3,4] have used mirrored geometry as a starting point for developing an implant model. However, since most human skulls are asymmetric to the sagittal plane, a unilateral defect may still require significant modification to fit the defect boundary after the mirroring operation, let alone defects spanning both sides.Significant progress has been made in deep learning-based 2D image restoration. For instance, [5] proposed a multi-scale convolutional neural network to provide high-frequency details for defect reconstruction. The image inpainting schemes of [6,7] used an encoder-decoder network structure [8][9][10] for adversarial loss training based on the Generative Adversarial Networks scheme [11,12]. Yan and coauthors [13] also introduced a shift connection layer in the U-Net architecture [14] for repairing defective images with fine details.While deep learning techniques have made noteworthy progress in 2D image completion, 3D shape inpainting remains challenging due to the higher dimensionality and computational requirements to process 3D data [15]. Among the early studies, Morais and coauthors in [16] conducted a pioneering study using an encoder-decoder network to reconstruct defective skull models at a volumetric resolution of up to 120×120×120 by integrating eight equally sized voxel grids of size 60×60×60.