Statistical model-based segmentation of the proximal femur in digital antero-posterior (AP) pelvic radiographs View Full Text


Ontology type: schema:ScholarlyArticle     


Article Info

DATE

2014-03

AUTHORS

Weiguo Xie, Jochen Franke, Cheng Chen, Paul A. Grützner, Steffen Schumann, Lutz-P. Nolte, Guoyan Zheng

ABSTRACT

PURPOSE: Segmentation of the proximal femur in digital antero-posterior (AP) pelvic radiographs is required to create a three-dimensional model of the hip joint for use in planning and treatment. However, manually extracting the femoral contour is tedious and prone to subjective bias, while automatic segmentation must accommodate poor image quality, anatomical structure overlap, and femur deformity. A new method was developed for femur segmentation in AP pelvic radiographs. METHODS: Using manual annotations on 100 AP pelvic radiographs, a statistical shape model (SSM) and a statistical appearance model (SAM) of the femur contour were constructed. The SSM and SAM were used to segment new AP pelvic radiographs with a three-stage approach. At initialization, the mean SSM model is coarsely registered to the femur in the AP radiograph through a scaled rigid registration. Mahalanobis distance defined on the SAM is employed as the search criteria for each annotated suggested landmark location. Dynamic programming was used to eliminate ambiguities. After all landmarks are assigned, a regularized non-rigid registration method deforms the current mean shape of SSM to produce a new segmentation of proximal femur. The second and third stages are iteratively executed to convergence. RESULTS: A set of 100 clinical AP pelvic radiographs (not used for training) were evaluated. The mean segmentation error was 0.96 mm ± 0.35 mm, requiring <5 s per case when implemented with Matlab. The influence of the initialization on segmentation results was tested by six clinicians, demonstrating no significance difference. CONCLUSIONS: A fast, robust and accurate method for femur segmentation in digital AP pelvic radiographs was developed by combining SSM and SAM with dynamic programming. This method can be extended to segmentation of other bony structures such as the pelvis. More... »

PAGES

165-176

References to SciGraph publications

  • 2005. Automatic Extraction of Femur Contours from Hip X-Ray Images in COMPUTER VISION FOR BIOMEDICAL IMAGE APPLICATIONS
  • 2010-07. An integrated platform for hip joint osteoarthritis analysis: design, implementation and results in INTERNATIONAL JOURNAL OF COMPUTER ASSISTED RADIOLOGY AND SURGERY
  • 2012. Finding Deformable Shapes by Correspondence-Free Instantiation and Registration of Statistical Shape Models in MACHINE LEARNING IN MEDICAL IMAGING
  • 2010-09. Statistically Deformable 2D/3D Registration for Estimating Post-operative Cup Orientation from a Single Standard AP X-ray Radiograph in ANNALS OF BIOMEDICAL ENGINEERING
  • 2012-03. Validation of a statistical shape model-based 2D/3D reconstruction method for determination of cup orientation after THA in INTERNATIONAL JOURNAL OF COMPUTER ASSISTED RADIOLOGY AND SURGERY
  • 2007. Automatic Segmentation of Femur Bones in Anterior-Posterior Pelvis X-Ray Images in COMPUTER ANALYSIS OF IMAGES AND PATTERNS
  • 2011-09. Functional and Anatomic Orientation of the Femoral Head in CLINICAL ORTHOPAEDICS AND RELATED RESEARCH®
  • 1999. Active Shape Model-Based Segmentation of Digital X-ray Images in MEDICAL IMAGE COMPUTING AND COMPUTER-ASSISTED INTERVENTION – MICCAI’99
  • 2010-09. Segmentation of radiographic images under topological constraints: application to the femur in INTERNATIONAL JOURNAL OF COMPUTER ASSISTED RADIOLOGY AND SURGERY
  • 2012. Accurate Fully Automatic Femur Segmentation in Pelvic Radiographs Using Regression Voting in MEDICAL IMAGE COMPUTING AND COMPUTER-ASSISTED INTERVENTION – MICCAI 2012
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    http://scigraph.springernature.com/pub.10.1007/s11548-013-0932-5

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    http://dx.doi.org/10.1007/s11548-013-0932-5

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    PUBMED

    https://www.ncbi.nlm.nih.gov/pubmed/23900851


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    35 schema:description PURPOSE: Segmentation of the proximal femur in digital antero-posterior (AP) pelvic radiographs is required to create a three-dimensional model of the hip joint for use in planning and treatment. However, manually extracting the femoral contour is tedious and prone to subjective bias, while automatic segmentation must accommodate poor image quality, anatomical structure overlap, and femur deformity. A new method was developed for femur segmentation in AP pelvic radiographs. METHODS: Using manual annotations on 100 AP pelvic radiographs, a statistical shape model (SSM) and a statistical appearance model (SAM) of the femur contour were constructed. The SSM and SAM were used to segment new AP pelvic radiographs with a three-stage approach. At initialization, the mean SSM model is coarsely registered to the femur in the AP radiograph through a scaled rigid registration. Mahalanobis distance defined on the SAM is employed as the search criteria for each annotated suggested landmark location. Dynamic programming was used to eliminate ambiguities. After all landmarks are assigned, a regularized non-rigid registration method deforms the current mean shape of SSM to produce a new segmentation of proximal femur. The second and third stages are iteratively executed to convergence. RESULTS: A set of 100 clinical AP pelvic radiographs (not used for training) were evaluated. The mean segmentation error was 0.96 mm ± 0.35 mm, requiring <5 s per case when implemented with Matlab. The influence of the initialization on segmentation results was tested by six clinicians, demonstrating no significance difference. CONCLUSIONS: A fast, robust and accurate method for femur segmentation in digital AP pelvic radiographs was developed by combining SSM and SAM with dynamic programming. This method can be extended to segmentation of other bony structures such as the pelvis.
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