PURPOSE: Segmentation 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. METHOD: A 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. RESULTS: The 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. CONCLUSION: A 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.
PURPOSE: Segmentation 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. METHOD: A 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. RESULTS: The 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. CONCLUSION: A 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.
Authors: Astrid M Hoving; Joep Kraeima; Rutger H Schepers; Hildebrand Dijkstra; Jan Hendrik Potze; Bart Dorgelo; Max J H Witjes Journal: PLoS One Date: 2018-04-20 Impact factor: 3.240
Authors: Cem M Deniz; Siyuan Xiang; R Spencer Hallyburton; Arakua Welbeck; James S Babb; Stephen Honig; Kyunghyun Cho; Gregory Chang Journal: Sci Rep Date: 2018-11-07 Impact factor: 4.379
Authors: Guodong Zeng; Celia Degonda; Adam Boschung; Florian Schmaranzer; Nicolas Gerber; Klaus A Siebenrock; Simon D Steppacher; Moritz Tannast; Till D Lerch Journal: Orthop J Sports Med Date: 2021-11-24