eDIMES Lab - Laboratory of Bioengineering, Department of Medical and Surgical Sciences, University of Bologna, Bologna, Italy.
Department of Computer Science and Engineering (DISI), University of Bologna, Bologna, Italy.
Sci Rep. 2024 Aug 27;14(1):19810. doi: 10.1038/s41598-024-68084-5.
In this paper, we investigate the effectiveness of shape completion neural networks as clinical aids in maxillofacial surgery planning. We present a pipeline to apply shape completion networks to automatically reconstruct complete eumorphic 3D meshes starting from a partial input mesh, easily obtained from CT data routinely acquired for surgery planning. Most of the existing works introduced solutions to aid the design of implants for cranioplasty, i.e. all the defects are located in the neurocranium. In this work, we focus on reconstructing defects localized on both neurocranium and splanchnocranium. To this end, we introduce a new dataset, specifically designed for this task, derived from publicly available CT scans and subjected to a comprehensive pre-processing procedure. All the scans in the dataset have been manually cleaned and aligned to a common reference system. In addition, we devised a pre-processing stage to automatically extract point clouds from the scans and enrich them with virtual defects. We experimentally compare several state-of-the-art point cloud completion networks and identify the two most promising models. Finally, expert surgeons evaluated the best-performing network on a clinical case. Our results show how casting the creation of personalized implants as a problem of shape completion is a promising approach for automatizing this complex task.
在本文中，我们研究了形状完成神经网络作为颌面外科手术规划临床辅助的有效性。我们提出了一种应用形状完成网络的流程，从手术计划中常规获取的 CT 数据中容易获得的部分输入网格开始，自动重建完整的正形 3D 网格。现有的大多数工作都提出了辅助颅骨成形术设计植入物的解决方案，即所有的缺陷都位于颅盖骨中。在这项工作中，我们专注于重建位于颅盖骨和颅底骨上的缺陷。为此，我们引入了一个新的数据集，该数据集专门为此任务设计，源自公开的 CT 扫描，并经过了全面的预处理过程。数据集中的所有扫描都经过了手动清理并对齐到一个共同的参考系统。此外，我们设计了一个预处理阶段，可以从扫描中自动提取点云，并使用虚拟缺陷对其进行丰富。我们在实验中比较了几种最先进的点云完成网络，并确定了两个最有前途的模型。最后，专家外科医生对临床病例中表现最佳的网络进行了评估。我们的结果表明，将个性化植入物的创建视为形状完成问题是自动化这一复杂任务的一种很有前途的方法。
