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An articulated statistical shape model for accurate hip joint segmentation

Item Type:Conference or Workshop Item
Title:An articulated statistical shape model for accurate hip joint segmentation
Creators Name:Kainmueller, D., Lamecker, H., Zachow, S. and Hege, H.C.
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.
Keywords:Algorithms, Anatomic Models, Artificial Intelligence, Automated Pattern Recognition, Computer-Assisted Radiographic Image Interpretation, Computer Simulation, Hip Joint, Radiographic Image Enhancement, Reproducibility of Results, Sensitivity and Specificity, Statistical Models, Three-Dimensional Imaging, X-Ray Computed Tomography
Source:Conference proceedings : ... Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual Conference
Publisher:Institute of Electrical and Electronics Engineers
Page Range:6345-6351
Date:13 September 2009
Official Publication:https://doi.org/10.1109/IEMBS.2009.5333269
PubMed:View item in PubMed

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