Fully automatic segmentation of femurs with medullary canal definition in high and in low resolution CT scans.


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.

DOI: 10.1016/j.medengphy.2016.09.019

Cite this paper

@article{Almeida2016FullyAS, title={Fully automatic segmentation of femurs with medullary canal definition in high and in low resolution CT scans.}, author={Diogo Moreira Campos Ferreira de Almeida and Rui Miguel Barreiros Ruben and Jo{\~a}o Orlando Marques Gameiro Folgado and Paulo R. Fernandes and E. Audenaert and Benedict Verhegghe and Matthieu de Beule}, journal={Medical engineering & physics}, year={2016}, volume={38 12}, pages={1474-1480} }