ArticleInternational journal of computer assisted radiology and surgery2015
A 3D active model framework for segmentation of proximal femur in MR images.
Article in International journal of computer assisted radiology and surgery, 2015. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 12 papers.
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Who cites it
12 citing papers in PubMed.
- Accuracy and Reliability of Synthetic Computed Tomography for Model-Based Tracking of Biplane Videoradiography Data.Annals of biomedical engineering · 2025Article
- A Two stage deep learning network for automated femoral segmentation in bilateral lower limb CT scans.Scientific reports · 2025Article
- ST-V-Net: incorporating shape prior into convolutional neural networks for proximal femur segmentation.Complex & intelligent systems · 2023Article
- Automated volumetric and statistical shape assessment of cam-type morphology of the femoral head-neck region from clinical 3D magnetic resonance images.Quantitative imaging in medicine and surgery · 2022Article
- A deep learning-based approach to automatic proximal femur segmentation in quantitative CT images.Medical & biological engineering & computing · 2022Review
- The use of 3D printed models for the pre-operative planning of surgical correction of pediatric hip deformities: a case series and concise review of the literature.Acta bio-medica : Atenei Parmensis · 2022Review
- Three-Dimensional Magnetic Resonance Imaging Bone Models of the Hip Joint Using Deep Learning: Dynamic Simulation of Hip Impingement for Diagnosis of Intra- and Extra-articular Hip Impingement.Orthopaedic journal of sports medicine · 2021Article
- Osteoporosis Recognition in Rats under Low-Power Lens Based on Convexity Optimization Feature Fusion.Scientific reports · 2019Article
- Cam-type femoroacetabular impingement-correlations between alpha angle versus volumetric measurements and surgical findings.European radiology · 2019Article
- Segmentation of the proximal femur in radial MR scans using a random forest classifier and deformable model registration.International journal of computer assisted radiology and surgery · 2019Article
- Segmentation of the Proximal Femur from MR Images using Deep Convolutional Neural Networks.Scientific reports · 2018Article
- Optimisation of three-dimensional lower jaw resection margin planning using a novel Black Bone magnetic resonance imaging protocol.PloS one · 2018Article
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Authors and funding
4 authors.
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Abstract
purposeSegmentation of osseous structures from clinical MR images is difficult due to acquisition artifacts and variable signal intensity of bones. Segmentation of femoral head is required for evaluation of hip joint abnormalities such as cam- type femoroacetabular impingement. A parametric deformable model (PDM) framework was developed for segmentation of 3D magnetic resonance (MR) images of the hip.
methodA two-phase segmentation scheme was implemented: (i) Radial basis function interpolation was performed for semi-automatic piecewise registration of a proximal femur atlas model to an MRI scan region of interest. User-defined control points on the mesh model were registered to the corresponding landmarks on the image. (ii) An active PDM was then used for coarse-to-fine level segmentation. The segmentation technique was tested using 3D synthetic image data and clinical MR scans of the hip with varying resolution.
resultsThe segmentation method provided a mean target overlap of 0.95 and misclassification error of 0.035 for the synthetic data. The average target overlap was 0.88, and misclassification error rate was 0.12 for the clinical MRI data sets.
conclusionA framework for segmentation of proximal femur in hip MRI scans was developed and tested. This method is robust to artifacts and intensity inhomogeneity and resistant to leakage into adjacent tissues. In comparison with slicewise segmentation techniques, this method features inter-slice consistency, which results in a smooth model of the proximal femur in hip MRI scans.
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