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An Articulated Statistical Shape Model for Accurate Hip Joint Segmentation
- Source :
- BASE-Bielefeld Academic Search Engine
-
Abstract
- In this paper we propose a framework for fully automatic, robust and accurate segmentation of the human pelvis and proximal femur in CT data. We propose a composite statistical shape model of femur and pelvis with a flexible hip joint, for which we extend the common definition of statistical shape models as well as the common strategy for their adaptation. We do not analyze the joint flexibility statistically, but model it explicitly by rotational parameters describing the bent in a ball-and-socket joint. A leave-one-out evaluation on 50 CT volumes shows that image driven adaptation of our composite shape model robustly produces accurate segmentations of both proximal femur and pelvis. As a second contribution, we evaluate a fine grain multi-object segmentation method based on graph optimization. It relies on accurate initializations of femur and pelvis, which our composite shape model can generate. Simultaneous optimization of both femur and pelvis yields more accurate results than separate optimizations of each structure. Shape model adaptation and graph based optimization are embedded in a fully automatic framework.
- Subjects :
- Models, Anatomic
Engineering
Physics::Medical Physics
Iterative reconstruction
Sensitivity and Specificity
Pattern Recognition, Automated
Imaging, Three-Dimensional
Artificial Intelligence
Robustness (computer science)
Computer Simulation
Femur
Computer vision
Segmentation
Joint (geology)
Models, Statistical
business.industry
Reproducibility of Results
Image segmentation
Radiographic Image Enhancement
Pattern recognition (psychology)
Radiographic Image Interpretation, Computer-Assisted
Hip Joint
Tomography
Artificial intelligence
Tomography, X-Ray Computed
business
Algorithms
Subjects
Details
- Database :
- OpenAIRE
- Journal :
- BASE-Bielefeld Academic Search Engine
- Accession number :
- edsair.doi.dedup.....755cc1927ebc6ff918cd41e4afcd5866