论文标题
通过虚拟颅骨切除术和形状先验的颅骨植入物设计
Cranial Implant Design via Virtual Craniectomy with Shape Priors
论文作者
论文摘要
颅内植入物设计是一项具有挑战性的任务，在颅骨成形术程序的背景下，其准确性至关重要。该任务通常由专家使用计算机辅助设计软件手动执行。在这项工作中，我们建议并评估CT图像中颅植入物重建的替代自动深度学习模型。使用Autoimplant Challenge发布的数据库对模型进行了培训和评估，并与组织者实施的基线进行了比较。我们使用模拟的虚拟颅骨切除术使用完整的头骨来训练我们的模型，并比较了使用此过程训练的两种不同的方法。第一个是基于UNET体系结构的直接估计方法。第二种方法结合了形状先验，以增加分布外植入物形状时的鲁棒性。我们的直接估计方法优于组织者提供的基线，而具有形状先验的模型在处理脱离外案例时表现出卓越的性能。总体而言，我们的方法在颅内植入物设计的艰巨任务中显示出令人鼓舞的结果。
Cranial implant design is a challenging task, whose accuracy is crucial in the context of cranioplasty procedures. This task is usually performed manually by experts using computer-assisted design software. In this work, we propose and evaluate alternative automatic deep learning models for cranial implant reconstruction from CT images. The models are trained and evaluated using the database released by the AutoImplant challenge, and compared to a baseline implemented by the organizers. We employ a simulated virtual craniectomy to train our models using complete skulls, and compare two different approaches trained with this procedure. The first one is a direct estimation method based on the UNet architecture. The second method incorporates shape priors to increase the robustness when dealing with out-of-distribution implant shapes. Our direct estimation method outperforms the baselines provided by the organizers, while the model with shape priors shows superior performance when dealing with out-of-distribution cases. Overall, our methods show promising results in the difficult task of cranial implant design.
