Currently, 3T hip MRI can be used to estimate femur strength and cortical bone thickness. One of the major hurdles in this application is that objects (osseous structures) are manually segmented which involves significant human labor. In this study, we propose an automatic and accurate algorithm for osseous structure segmentation from hip 3T MRI by using a deep convolutional neural network. The approach includes two stages: 1) automatic localization of acetabulum and femur by using the femoral head as a reference, and 2) 2D bounding box (BB) set up for each object based on the localization information from femoral head followed by a UNet to segment the target object within the BB. 90 3T hip MRI image data sets were utilized in this study that were divided into training, validating, and testing groups (60%:20%:20%), and a 5-fold cross-validation was adopted in the procedure. The study showed that automated segmentation results were comparable to the reference standard from manual segmentation. The average Dice Coefficient for acetabular and femoral (i.e., cortical and medullary bone plus bone marrow) segmentation was 0.93 and 0.96, respectively. Segmentations of acetabular and femoral medullary cavity (i.e., medullary bone plus bone marrow) had Dice Coefficient of 0.89 and 0.95, respectively. Acetabular and femoral cortical bone segmentations were more challenging with lower Dice Coefficient of around 0.7. The proposed approach is automatic and effective without any interaction from humans. The idea of using local salient anatomy to guide object localization approaches is heuristic and can be easily generalized to other localization problems in practice.
Osteoporosis is a common age-related disease associated with increased bone loss causing reduced bone strength and enhanced fracture-risk. Finite element (FE) modelling is used to estimate bone strength from high-resolution threedimensional (3-D) imaging modalities including micro-CT, MRI, and HR-pQCT. Emerging technologies of multi-row detector CT (MDCT) imaging offer spatial image resolution comparable to human trabecular thickness. However, at the current MDCT resolution regime, FE modelling based on segmented trabecular bone (Tb) microstructure suffers from noise and other imaging artifacts. In this paper, we present a bone mineral density (BMD)-adjusted FE modeling method of Tb microstructure from MDCT imaging without requiring Tb segmentation. The method spatially varies mechanical stiffness based on local ash-density estimated from MDCT-derived calcium hydroxyapatite (CHA) density and, thus, models the hypothesis that stress-flow is primarily absorbed by Tb microstructure as compared to marrow space under mechanical compression. Specifically, an MDCT-based linear FE analysis method was developed using a voxel-mesh and the above model of space-varying stiffness, and the performance of the method was examined. For FE analysis, an axial cylindrical image core of 8mm diameter from 4-6% of distal tibia was extracted after aligning the tibial bone axis with the coordinate z-axis of the image space. Intra-class correlation coefficient (ICC) of 0.98 was observed in a repeat MDCT scan reproducibility experiment using cadaveric distal tibia specimens (n = 10). Also, high linear correlation (r = 0.87) was found between von Mises stress values and MDCT based CHA at individual voxels supporting the central hypothesis of our method.
Osteoarthritis (OA) is a chronic degenerative disorder of joints and is the most common reason leading to total knee joint replacement (TKR). In this paper, we implemented a semi-supervised learning approach based on Unsupervised Data Augmentation (UDA) along with valid perturbations for radiographs to enhance the performance of supervised TKR outcome prediction model. Our results suggest that the use of semi-supervised approach provides superior results compared to the supervised approach (AUC of 0.79 ± 0.04 vs 0.74 ± 0.04).
Osteoporosis, associated with reduced bone mineral density and structural degeneration, greatly increases the risk of fragility fracture. A major challenge of volumetric bone imaging of the hip is the selection of regions of interest (ROIs) for computation of regional bone measurements. Here, we develop an MRI-based active shape model (ASM) of the human proximal femur used to automatically generate ROIs. Major challenges in developing the ASM of a complex three-dimensional (3-D) shape lie in determining a large number of anatomically consistent landmarks for a set of training shapes. In this paper, we develop a new method of generating the proximal femur ASM, where two types of landmarks, namely fiducial and secondary landmarks, are used. The method of computing the MRI-based proximal femur ASM consists of—(1) segmentation of the proximal femur bone volume, (2) smoothing the bone surface, (3) drawing fiducial landmark lines on training shapes, (4) drawing secondary landmarks on a reference shape, (5) landmark mesh generation on the reference shape using both fiducial and secondary landmarks, (6) generation of secondary landmarks on other training shapes using the correspondence of fiducial landmarks and an elastic deformation of the landmark mesh, (7) computation of the active shape model. An MRI-based shape model of the human proximalfemur has been developed using hip MR scans of 45 post-menopausal women. The results of secondary landmark generation were visually satisfactory and no topology violation or notable geometric distortion artifacts were observed. Performance of the method was examined in terms of shape representation errors in a leave-one-out test. The mean and standard deviation of leave-one-out shape representation errors were 2.27 and 0.61 voxels respectively. The experimental results suggest that the framework of fiducial and secondary landmark allows reliable computation statistical shape models for complex 3-D anatomic structures.
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