Annu Int Conf IEEE Eng Med Biol Soc. 2022 Jul;2022:603-608. doi: 10.1109/EMBC48229.2022.9871069.
Automatizing cranial implant design has become an increasingly important avenue in biomedical research. Benefits in terms of financial resources, time and patient safety necessitate the formulation of an efficient and accurate procedure for the same. This paper attempts to provide a new research direction to this problem, through an adversarial deep learning solution. Specifically, in this work, we present CranGAN - a 3D Conditional Generative Adversarial Network designed to reconstruct a 3D representation of a complete skull given its defective counterpart. A novel solution of employing point cloud representations instead of conventional 3D meshes and voxel grids is proposed. We provide both qualitative and quantitative analysis of our experiments with three separate GAN objectives, and compare the utility of two 3D reconstruction loss functions viz. Hausdorff Distance and Chamfer Distance. We hope that our work inspires further research in this direction. Clinical relevance- This paper establishes a new research direction to assist in automated implant design for cranioplasty.
颅植入物设计的自动化已经成为生物医学研究中一个日益重要的途径。从财务资源、时间和患者安全方面来看，都需要为颅修复术制定一种高效准确的方法。本文试图通过对抗性深度学习解决方案为这个问题提供一个新的研究方向。具体来说，在这项工作中，我们提出了 CranGAN——一种 3D 条件生成对抗网络，旨在根据缺陷的颅骨重建完整颅骨的 3D 表示。提出了一种新颖的解决方案，即使用点云表示代替传统的 3D 网格和体素网格。我们提供了三个单独的 GAN 目标的实验的定性和定量分析，并比较了两种 3D 重建损失函数的效用，即 Hausdorff 距离和 Chamfer 距离。我们希望我们的工作能够为这一方向的进一步研究提供启示。临床相关性——本文为自动设计颅植入物建立了一个新的研究方向，以协助颅骨修复术。
