Institute for AI in Medicine (IKIM), University Medicine Essen, Girardetstraße 2, 45131 Essen, Germany; Institute of Computer Graphics and Vision, Graz University of Technology, Inffeldgasse 16, 8010 Graz, Austria; Computer Algorithms for Medicine Laboratory, Graz, Austria.
Department of Neurosurgery, University of Nebraska Medical Center, Omaha, NE, 68198, USA.
Med Image Anal. 2023 Aug;88:102865. doi: 10.1016/j.media.2023.102865. Epub 2023 Jun 9.
Cranial implants are commonly used for surgical repair of craniectomy-induced skull defects. These implants are usually generated offline and may require days to weeks to be available. An automated implant design process combined with onsite manufacturing facilities can guarantee immediate implant availability and avoid secondary intervention. To address this need, the AutoImplant II challenge was organized in conjunction with MICCAI 2021, catering for the unmet clinical and computational requirements of automatic cranial implant design. The first edition of AutoImplant (AutoImplant I, 2020) demonstrated the general capabilities and effectiveness of data-driven approaches, including deep learning, for a skull shape completion task on synthetic defects. The second AutoImplant challenge (i.e., AutoImplant II, 2021) built upon the first by adding real clinical craniectomy cases as well as additional synthetic imaging data. The AutoImplant II challenge consisted of three tracks. Tracks 1 and 3 used skull images with synthetic defects to evaluate the ability of submitted approaches to generate implants that recreate the original skull shape. Track 3 consisted of the data from the first challenge (i.e., 100 cases for training, and 110 for evaluation), and Track 1 provided 570 training and 100 validation cases aimed at evaluating skull shape completion algorithms at diverse defect patterns. Track 2 also made progress over the first challenge by providing 11 clinically defective skulls and evaluating the submitted implant designs on these clinical cases. The submitted designs were evaluated quantitatively against imaging data from post-craniectomy as well as by an experienced neurosurgeon. Submissions to these challenge tasks made substantial progress in addressing issues such as generalizability, computational efficiency, data augmentation, and implant refinement. This paper serves as a comprehensive summary and comparison of the submissions to the AutoImplant II challenge. Codes and models are available at https://github.com/Jianningli/Autoimplant_II.
颅骨植入物通常用于颅骨切除术引起的颅骨缺损的手术修复。这些植入物通常是离线生成的，可能需要数天到数周的时间才能使用。自动化植入物设计流程与现场制造设施相结合，可以保证植入物的即时可用性，并避免二次干预。为了满足这一需求，AutoImplant II 挑战赛与 2021 年 MICCAI 联合组织，满足了自动颅骨植入物设计的未满足的临床和计算需求。第一版 AutoImplant（AutoImplant I，2020 年）展示了基于数据驱动的方法（包括深度学习）在合成缺陷颅骨形状完成任务上的一般能力和有效性。第二届 AutoImplant 挑战赛（即 AutoImplant II，2021 年）在第一届的基础上增加了真实的临床颅骨切除术病例和额外的合成成像数据。AutoImplant II 挑战赛由三个轨道组成。轨道 1 和 3 使用带有合成缺陷的颅骨图像来评估提交方法生成可重现原始颅骨形状的植入物的能力。轨道 3 包含来自第一挑战赛的数据（即 100 个训练病例和 110 个评估病例），轨道 1 提供了 570 个训练病例和 100 个验证病例，旨在评估不同缺陷模式下的颅骨形状完成算法。轨道 2 还通过提供 11 个临床缺陷颅骨，并在这些临床病例上评估提交的植入物设计，取得了比第一挑战赛更大的进展。提交的设计根据术后颅切除术的影像学数据以及经验丰富的神经外科医生进行了定量评估。这些挑战赛任务的提交在可泛化性、计算效率、数据增强和植入物细化等方面取得了实质性进展。本文全面总结和比较了 AutoImplant II 挑战赛的提交情况。代码和模型可在 https://github.com/Jianningli/Autoimplant_II 上获取。
