Almeida Diogo F, Ruben Rui B, Folgado João, Fernandes Paulo R, Audenaert Emmanuel, Verhegghe Benedict, De Beule Matthieu
IDMEC, Instituto Superior Técnico, Universidade de Lisboa, Av. Rovisco Pais, 1049-001 Lisboa, Portugal; iBiTech-bioMMeda, Ghent University, De Pintelaan 185, B-9000 Ghent, Belgium.
ESTG, CDRSP, Polytechnic Institute of Leiria, Campus 2, Morro do Lena, Alto do Vieiro, 2411-901 Leiria, Portugal.
Med Eng Phys. 2016 Dec;38(12):1474-1480. doi: 10.1016/j.medengphy.2016.09.019. Epub 2016 Oct 15.
Femur segmentation can be an important tool in orthopedic surgical planning. However, in order to overcome the need of an experienced user with extensive knowledge on the techniques, segmentation should be fully automatic. In this paper a new fully automatic femur segmentation method for CT images is presented. This method is also able to define automatically the medullary canal and performs well even in low resolution CT scans. Fully automatic femoral segmentation was performed adapting a template mesh of the femoral volume to medical images. In order to achieve this, an adaptation of the active shape model (ASM) technique based on the statistical shape model (SSM) and local appearance model (LAM) of the femur with a novel initialization method was used, to drive the template mesh deformation in order to fit the in-image femoral shape in a time effective approach. With the proposed method a 98% convergence rate was achieved. For high resolution CT images group the average error is less than 1mm. For the low resolution image group the results are also accurate and the average error is less than 1.5mm. The proposed segmentation pipeline is accurate, robust and completely user free. The method is robust to patient orientation, image artifacts and poorly defined edges. The results excelled even in CT images with a significant slice thickness, i.e., above 5mm. Medullary canal segmentation increases the geometric information that can be used in orthopedic surgical planning or in finite element analysis.
股骨分割可以成为骨科手术规划中的一项重要工具。然而，为了克服对具备该技术丰富知识的经验丰富用户的需求，分割应该是完全自动化的。本文提出了一种用于CT图像的全新全自动股骨分割方法。该方法还能够自动定义髓腔，并且即使在低分辨率CT扫描中也能表现良好。通过使股骨体积的模板网格适应医学图像来进行全自动股骨分割。为了实现这一点，使用了一种基于股骨的统计形状模型（SSM）和局部外观模型（LAM）的主动形状模型（ASM）技术的改进版本，并采用了一种新颖的初始化方法，以驱动模板网格变形，从而以高效的方式拟合图像中的股骨形状。使用所提出的方法实现了98%的收敛率。对于高分辨率CT图像组，平均误差小于1毫米。对于低分辨率图像组，结果也很准确，平均误差小于1.5毫米。所提出的分割流程准确、稳健且完全无需用户操作。该方法对患者体位、图像伪影和边缘定义不清具有鲁棒性。即使在切片厚度显著（即大于5毫米）的CT图像中，结果也很出色。髓腔分割增加了可用于骨科手术规划或有限元分析的几何信息。
