Audenaert Emmanuel A, Van Houcke Jan, Almeida Diogo F, Paelinck Lena, Peiffer M, Steenackers Gunther, Vandermeulen Dirk
a Department of Orthopedic Surgery and Traumatology , Ghent University Hospital , Ghent , Belgium.
b Department of Trauma and Orthopedics, Addenbrooke's Hospital , Cambridge University Hospitals NHS Foundation Trust , Cambridge , UK.
Comput Methods Biomech Biomed Engin. 2019 May;22(6):644-657. doi: 10.1080/10255842.2019.1577828. Epub 2019 Mar 1.
Image segmentation has become an important tool in orthopedic and biomechanical research. However, it greatly remains a time-consuming and laborious task. In this manuscript, we propose a fully automatic model-based segmentation pipeline for the full lower limb in computed tomography (CT) images. The method relies on prior shape model fitting, followed by a gradient-defined free from deformation. The technique allows for the generation of anatomically corresponding surface meshes, which can subsequently be applied in anatomical and mechanical simulation studies. Starting from an initial, small (n ≤ 10) sample of manual segmentations, the model is continuously updated and refined with newly segmented training samples. Validation of the segmentation pipeline was performed by comparing the automatic segmentations against corresponding manual segmentations. Convergence of the segmentation pipeline was obtained in 250 cases and failed in three samples. The average distance error ranged from 0.53 to 0.76 mm and maximal error ranged from 2.0 to 7.8 mm for the 7 different osteological structures that were investigated. The accuracy of the shape model-based segmentation gradually increased as the number of training shapes in the updated population also increased. When optimized with the free form deformation, however, average segmentation accuracy rapidly plateaued from already as little as 20 training samples on. The maximum segmentation error plateaued from 100 training samples on.
图像分割已成为骨科和生物力学研究中的一项重要工具。然而，它仍然是一项极其耗时费力的任务。在本论文中，我们提出了一种基于模型的全自动分割流程，用于对计算机断层扫描（CT）图像中的整个下肢进行分割。该方法依赖于先验形状模型拟合，随后进行梯度定义的无变形处理。该技术能够生成解剖学上对应的表面网格，这些网格随后可应用于解剖学和力学模拟研究。从最初少量（n≤10）的手动分割样本开始，该模型会随着新分割的训练样本不断更新和完善。通过将自动分割结果与相应的手动分割结果进行比较，对分割流程进行了验证。在250个病例中实现了分割流程的收敛，有3个样本失败。对于所研究的7种不同骨学结构，平均距离误差在0.53至0.76毫米之间，最大误差在2.0至7.8毫米之间。基于形状模型的分割精度随着更新群体中训练形状数量的增加而逐渐提高。然而，当使用自由形式变形进行优化时，平均分割精度从仅20个训练样本时就迅速趋于平稳。最大分割误差从100个训练样本时开始趋于平稳。
