title: Fully automatic segmentation of femurs with medullary canal definition in high and in low resolution CT scans.
abstract: 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.
authors.affiliation: 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. Electronic address: diogo@dem.ist.utl.pt.
authors.forename: Diogo F
authors.initials: DF
authors.lastname: Almeida
authors.affiliation: ESTG, CDRSP, Polytechnic Institute of Leiria, Campus 2, Morro do Lena, Alto do Vieiro, 2411-901 Leiria, Portugal. Electronic address: rui.ruben@ipleiria.pt.
authors.forename: Rui B
authors.initials: RB
authors.lastname: Ruben
authors.affiliation: IDMEC, Instituto Superior Técnico, Universidade de Lisboa, Av. Rovisco Pais, 1049-001 Lisboa, Portugal. Electronic address: jfolgado@dem.ist.utl.pt.
authors.forename: João
authors.initials: J
authors.lastname: Folgado
authors.affiliation: IDMEC, Instituto Superior Técnico, Universidade de Lisboa, Av. Rovisco Pais, 1049-001 Lisboa, Portugal. Electronic address: prfernan@dem.ist.utl.pt.
authors.forename: Paulo R
authors.initials: PR
authors.lastname: Fernandes
authors.affiliation: Department of Orthopaedic Surgery and Traumatology, Ghent University Hospital, De Pintelaan 185, B-9000 Ghent, Belgium. Electronic address: Emmanuel.Audenaert@ugent.be.
authors.forename: Emmanuel
authors.initials: E
authors.lastname: Audenaert
authors.affiliation: iBiTech-bioMMeda, Ghent University, De Pintelaan 185, B-9000 Ghent, Belgium. Electronic address: benedict.verhegghe@ugent.be.
authors.forename: Benedict
authors.initials: B
authors.lastname: Verhegghe
authors.affiliation: iBiTech-bioMMeda, Ghent University, De Pintelaan 185, B-9000 Ghent, Belgium. Electronic address: matthieu.debeule@ugent.be.
authors.forename: Matthieu
authors.initials: M
authors.lastname: De Beule
mesh_terms: D001331:Automation; D005269:Femur / Q000000981:diagnostic imaging*; D006801:Humans; D007091:Image Processing, Computer-Assisted; D059629:Signal-To-Noise Ratio; D014057:Tomography, X-Ray Computed
keywords: 3D femur segmentation; Active shape model (ASM); CT image; Statistical shape model (SSM); Total hip arthroplasty