Supervised learning for bone shape and cortical thickness estimation from CT images for finite element analysis
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Thomas Gerig | Mauricio Reyes | Vimal Chandran | Philippe Zysset | Ghislain Maquer | M. Reyes | P. Zysset | V. Chandran | Thomas Gerig | Ghislain Maquer
[1] Mauricio Reyes,et al. A novel registration-based methodology for prediction of trabecular bone fabric from clinical QCT: A comprehensive analysis , 2017, PloS one.
[2] Alexandre Terrier,et al. Identification of elastic properties of human patellae using micro-finite element analysis. , 2016, Journal of biomechanics.
[3] Dieter H. Pahr,et al. From high-resolution CT data to finite element models: development of an integrated modular framework , 2009 .
[4] F. Deconinck,et al. Information Processing in Medical Imaging , 1984, Springer Netherlands.
[5] Pierre Geurts,et al. Extremely randomized trees , 2006, Machine Learning.
[6] Yohan Payan,et al. A fast and robust patient specific Finite Element mesh registration technique: Application to 60 clinical cases , 2010, Medical Image Anal..
[7] P. Zysset,et al. Validation of an anatomy specific finite element model of Colles' fracture. , 2008, Journal of biomechanics.
[8] Timothy F. Cootes,et al. Learning-Based Shape Model Matching: Training Accurate Models with Minimal Manual Input , 2015, MICCAI.
[9] Thor F Besier,et al. Predictive statistical models of baseline variations in 3-D femoral cortex morphology. , 2016, Medical engineering & physics.
[10] Cyril Flaig,et al. On Smoothing Surfaces in Voxel Based Finite Element Analysis of Trabecular Bone , 2009, LSSC.
[11] Ling Qin,et al. Clinical Use of Quantitative Computed Tomography (QCT) of the Hip in the Management of Osteoporosis in Adults: the 2015 ISCD Official Positions-Part I. , 2015, Journal of clinical densitometry : the official journal of the International Society for Clinical Densitometry.
[12] S. Cummings,et al. BMD at Multiple Sites and Risk of Fracture of Multiple Types: Long‐Term Results From the Study of Osteoporotic Fractures , 2003, Journal of bone and mineral research : the official journal of the American Society for Bone and Mineral Research.
[13] Mauricio Reyes,et al. Statistical analysis of the inter-individual variations of the bone shape, volume fraction and fabric and their correlations in the proximal femur. , 2017, Bone.
[14] Andrew H Gee,et al. Predicting Hip Fracture Type With Cortical Bone Mapping (CBM) in the Osteoporotic Fractures in Men (MrOS) Study , 2015, Journal of bone and mineral research : the official journal of the American Society for Bone and Mineral Research.
[15] Enrico Dall'Ara,et al. Clinical versus pre-clinical FE models for vertebral body strength predictions. , 2014, Journal of the mechanical behavior of biomedical materials.
[16] Gabriel Taubin,et al. Curve and surface smoothing without shrinkage , 1995, Proceedings of IEEE International Conference on Computer Vision.
[17] Robert M. Haralick,et al. Textural Features for Image Classification , 1973, IEEE Trans. Syst. Man Cybern..
[18] F. Kainberger,et al. A nonlinear QCT-based finite element model validation study for the human femur tested in two configurations in vitro. , 2013, Bone.
[19] Yan Kang,et al. A new accurate and precise 3-D segmentation method for skeletal structures in volumetric CT data , 2003, IEEE Transactions on Medical Imaging.
[20] H. Genant,et al. Comparison of proximal femur and vertebral body strength improvements in the FREEDOM trial using an alternative finite element methodology. , 2015, Bone.
[21] Andrew H. Gee,et al. Imaging the femoral cortex: Thickness, density and mass from clinical CT , 2012, Medical Image Anal..
[22] E. Dall’Ara,et al. Orthotropic HR-pQCT-based FE models improve strength predictions for stance but not for side-way fall loading compared to isotropic QCT-based FE models of human femurs. , 2014, Journal of the mechanical behavior of biomedical materials.
[23] Timothy F. Cootes,et al. Active Shape Models-Their Training and Application , 1995, Comput. Vis. Image Underst..
[24] Andrew H. Gee,et al. Independent measurement of femoral cortical thickness and cortical bone density using clinical CT , 2015, Medical Image Anal..
[25] Moritz Tannast,et al. Head-Neck Osteoplasty has Minor Effect on the Strength of an Ovine Cam-FAI Model: In Vitro and Finite Element Analyses , 2016, Clinical orthopaedics and related research.
[26] Gábor Székely,et al. Statistical model based shape prediction from a combination of direct observations and various surrogates: Application to orthopaedic research , 2012, Medical Image Anal..
[27] J. Cauley,et al. Public health impact of osteoporosis. , 2013, The journals of gerontology. Series A, Biological sciences and medical sciences.
[28] Andrew H. Gee,et al. The Effects on the Femoral Cortex of a 24 Month Treatment Compared to an 18 Month Treatment with Teriparatide: A Multi-Trial Retrospective Analysis , 2016, PloS one.
[29] Thomas Gerig,et al. Gaussian Process Morphable Models , 2016, IEEE Transactions on Pattern Analysis and Machine Intelligence.
[30] M. Viceconti,et al. Experimental validation of DXA-based finite element models for prediction of femoral strength. , 2016, Journal of the mechanical behavior of biomedical materials.
[31] R. Harnish,et al. Automatic multi-parametric quantification of the proximal femur with quantitative computed tomography. , 2015, Quantitative imaging in medicine and surgery.
[32] Andrew H. Gee,et al. High resolution cortical bone thickness measurement from clinical CT data , 2010, Medical Image Anal..
[33] Brent C Taylor,et al. Risk Factors for Hip Fracture in Older Men: The Osteoporotic Fractures in Men Study (MrOS) , 2016, Journal of bone and mineral research : the official journal of the American Society for Bone and Mineral Research.
[34] Kate A. Gavaghan,et al. Facial nerve image enhancement from CBCT using supervised learning technique , 2015, 2015 37th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC).
[35] Yutaka Ohtake,et al. A comparison of mesh smoothing methods , 2003 .
[36] F. Taddei,et al. Can CT image deblurring improve finite element predictions at the proximal femur? , 2016, Journal of the mechanical behavior of biomedical materials.
[37] William E. Lorensen,et al. Marching cubes: A high resolution 3D surface construction algorithm , 1987, SIGGRAPH.
[38] Stefan Weber,et al. Highly Accurate Facial Nerve Segmentation Refinement From CBCT/CT Imaging Using a Super-Resolution Classification Approach , 2018, IEEE Transactions on Biomedical Engineering.
[39] Mauricio Reyes,et al. Prediction of Trabecular Bone Anisotropy from Quantitative Computed Tomography Using Supervised Learning and a Novel Morphometric Feature Descriptor , 2015, MICCAI.
[40] D. C. Bauer,et al. Erratum to: Clinical utility of routine laboratory testing to identify possible secondary causes in older men with osteoporosis: the osteoporotic fractures in men (MrOS) study , 2016, Osteoporosis International.
[41] Max A. Viergever,et al. elastix: A Toolbox for Intensity-Based Medical Image Registration , 2010, IEEE Transactions on Medical Imaging.
[42] Richard J. Cook,et al. Risk factors for hip fracture in older home care clients. , 2009, The journals of gerontology. Series A, Biological sciences and medical sciences.
[43] H. Genant,et al. Accuracy of CT-based thickness measurement of thin structures: modeling of limited spatial resolution in all three dimensions. , 2002, Medical physics.
[44] Raphaël Marée,et al. Extremely Randomized Trees and Random Subwindows for Image Classification, Annotation, and Retrieval , 2013 .
[45] Ludovic Humbert,et al. Technical Note: Cortical thickness and density estimation from clinical CT using a prior thickness-density relationship. , 2016, Medical physics.
[46] C. Thomas,et al. Relation between age, femoral neck cortical stability, and hip fracture risk , 2005, The Lancet.
[47] Normand Robert,et al. Generalized method for computation of true thickness and x-ray intensity information in highly blurred sub-millimeter bone features in clinical CT images , 2012, Physics in medicine and biology.
[48] Antonio Criminisi,et al. Image Quality Transfer via Random Forest Regression: Applications in Diffusion MRI , 2014, MICCAI.
[49] Willi A Kalender,et al. An anatomic coordinate system of the femoral neck for highly reproducible BMD measurements using 3D QCT. , 2005, Computerized Medical Imaging and Graphics.
[50] Thomas Vetter,et al. A Unified Approach to Shape Model Fitting and Non-rigid Registration , 2013, MLMI.
[51] Andrew H. Gee,et al. Focal osteoporosis defects play a key role in hip fracture , 2017, Bone.
[52] Philippe C. Cattin,et al. Prediction of Cranio-Maxillofacial Surgical Planning Using an Inverse Soft Tissue Modelling Approach , 2013, MICCAI.
[53] Dorin Comaniciu,et al. Shape Regression Machine , 2007, IPMI.
[54] William H. Press,et al. Numerical Recipes 3rd Edition: The Art of Scientific Computing , 2007 .
[55] Philippe K. Zysset,et al. FEA to Measure Bone Strength: A Review , 2016, Clinical Reviews in Bone and Mineral Metabolism.
[56] Robert C. Bolles,et al. Random sample consensus: a paradigm for model fitting with applications to image analysis and automated cartography , 1981, CACM.
[57] Klaus Engelke,et al. FEA to measure bone strength , 2016 .
[58] W. Kalender,et al. Accuracy limits for the determination of cortical width and density: the influence of object size and CT imaging parameters. , 1999, Physics in medicine and biology.
[59] V. Gudnason,et al. Interactive graph-cut segmentation for fast creation of finite element models from clinical ct data for hip fracture prediction , 2016, Computer methods in biomechanics and biomedical engineering.
[60] Ahmed Bouridane,et al. Classification of Prostatic Tissues using Feature Selection Methods , 2007 .
[61] K. Y. Dai,et al. A Smoothed Finite Element Method for Mechanics Problems , 2007 .
[62] Christophe Geuzaine,et al. Gmsh: A 3‐D finite element mesh generator with built‐in pre‐ and post‐processing facilities , 2009 .
[63] Leo Joskowicz,et al. Can a partial volume edge effect reduction algorithm improve the repeatability of subject-specific finite element models of femurs obtained from CT data? , 2014, Computer methods in biomechanics and biomedical engineering.
[64] Raphaël Marée,et al. Fast Multi-class Image Annotation with Random Subwindows and Multiple Output Randomized Trees , 2009, VISAPP.