Abstract

3D preoperative planning for high tibial osteotomies (HTO) has increasingly replaced 2D planning but is complex, time-consuming and therefore expensive. Several interdependent clinical objectives and constraints have to be considered, which often requires multiple rounds of revisions between surgeons and biomedical engineers. We therefore developed an automated preoperative planning pipeline, which takes imaging data as an input to generate a ready-to-use, patient-specific planning solution. Deep-learning based segmentation and landmark localization was used to enable the fully automated 3D lower limb deformity assessment. A 2D-3D registration algorithm allowed the transformation of the 3D bone models into the weight-bearing state. Finally, an optimization framework was implemented to generate ready-to use preoperative plannings in a fully automated fashion, using a genetic algorithm to solve the multi-objective optimization (MOO) problem based on several clinical requirements and constraints. The entire pipeline was evaluated on a large clinical dataset of 53 patient cases who previously underwent a medial opening-wedge HTO. The pipeline was used to automatically generate preoperative solutions for these patients. Five experts blindly compared the automatically generated solutions to the previously generated manual plannings. The overall mean rating for the algorithm-generated solutions was better than for the manual solutions. In 90% of all comparisons, they were considered to be equally good or better than the manual solution. The combined use of deep learning approaches, registration methods and MOO can reliably produce ready-to-use preoperative solutions that significantly reduce human workload and related health costs.

摘要

胫骨高位截骨术 (HTO) 的 3D 术前规划已逐渐取代 2D 规划，但复杂、耗时且昂贵。必须考虑几个相互依赖的临床目标和限制，这通常需要外科医生和生物医学工程师之间进行多轮修改。因此，我们开发了一个自动化的术前规划流程，它将成像数据作为输入，生成即用型、针对患者的规划解决方案。使用基于深度学习的分割和地标定位来实现全自动 3D 下肢畸形评估。2D-3D 配准算法允许将 3D 骨骼模型转换为负重状态。最后，实施优化框架以完全自动化的方式生成随时可用的术前计划，使用遗传算法根据多个临床要求和约束来解决多目标优化（MOO）问题。整个流程在包含 53 名先前接受过内侧开口楔形 HTO 的患者病例的大型临床数据集上进行了评估。该管道用于自动为这些患者生成术前解决方案。五位专家盲目地将自动生成的解决方案与之前生成的手动规划进行比较。算法生成的解决方案的总体平均评分优于手动解决方案。在 90% 的比较中，它们被认为与手动解决方案同等好或更好。深度学习方法的结合使用，