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.
KEYWORDS: Image segmentation, Bone, 3D modeling, Computed tomography, Data modeling, 3D image processing, Surgery, Prototyping, Interfaces, Image processing algorithms and systems
Osteophyte is an additional bony growth on a normal bone surface limiting or stopping motion in a deteriorating joint.
Detection and quantification of osteophytes from CT images is helpful in assessing disease status as well as treatment and
surgery planning. However, it is difficult to segment osteophytes from healthy bones using simple thresholding or
edge/texture features in CT imaging. Here, we present a new method, based on active shape model (ASM), to solve this
problem and evaluate its application to ex vivo μCT images in an ACLT rabbit femur model. The common idea behind
most ASM based segmentation methods is to first build a parametric shape model from a training dataset and during
application, find a shape instance from the model that optimally fits to target image. However, it poses a fundamental
difficulty for the current application because a diseased bone shape is significantly altered at regions with osteophyte
deposition misguiding an ASM method that eventually leads to suboptimum segmentation results. Here, we introduce a
new partial ASM method that uses bone shape over healthy regions and extrapolate its shape over diseased region
following the underlying shape model. Once the healthy bone region is detected, osteophyte is segmented by subtracting
partial-ASM derived shape from the overall diseased shape. Also, a new semi-automatic method is presented in this paper
for efficiently building a 3D shape model for rabbit femur. The method has been applied to μCT images of 2-, 4-, and
8-week post ACLT and sham-treated rabbit femurs and results of reproducibility and sensitivity analyses of the new
osteophyte segmentation method are presented.
Trabecular bone (TB) is a complex quasi-random network of interconnected struts and plates. TB constantly remodels to adapt dynamically to the stresses to which it is subjected (Wolff's Law). In osteoporosis, this dynamic equilibrium between bone formation and resorption is perturbed, leading to bone loss and structural deterioration, both increasing fracture risk. Bone's mechanical competence can only be partly explained by variations in bone mineral density, which led to the notion of bone structural quality. Previously, we developed digital topological analysis or DTA which classifies plates, rods, profiles, edges and junctions in a TB skeletal representation. Although the method has become quite popular, a major limitation is that DTA produces hard classifications only, failing to distinguish between narrow and wide plates. Here, we present a new method called volumetric topological analysis or VTA for quantification of regional topology in complex quasi-random TB networks. At each TB voxel, the method uniquely classifies the topology on the continuum between perfect plates and rods. Therefore, the method is capable of detecting early alterations of trabeculae from plates to rods according to the known etiology of osteoporotic bone loss. Here, novel ideas of geodesic distance transform, geodesic scale and feature propagation have been introduced and combined with DTA and fuzzy distance transform methods conceiving the new VTA technology. The method has been applied to MDCT and μCT images of a cadaveric distal tibia specimen and the results have been quantitatively evaluated. Specifically, intra- and inter-modality reproducibility of the method has been examined and the results are found very promising.
tomographic angiography (CTA) being noninvasive, economical and informative, has become a common modality for
monitoring disease status and treatment effects. Here, we present a new method for detecting and quantifying coronary
arterial stenosis via CTA using fuzzy distance transform (FDT) approach. FDT computes local depth at each image point
in the presence of partial voluming. Coronary arterial stenoses are detected and their severities are quantified by
analyzing FDT values along the medial axis of an artery obtained by skeletonization. Also, we have developed a new
skeletal pruning algorithm toward improving quality of medial axes and therefore, enhancing the accuracy of stenosis
detection and quantification. The method is completed using the following steps - (1) fuzzy segmentation of coronary
artery via CTA, (2) FDT computation of coronary arteries, (3) medial axis computation, (4) estimation of local diameter
along arteries and (5) stenosis detection and quantification of arterial blockage. Performance of the method has been
quantitatively evaluated on a realistic coronary artery phantom dataset with randomly simulated stenoses and the results
are compared with a classical binary algorithm. The method has also been applied on a clinical CTA dataset from
thirteen patients with 59 stenoses and the results are compared with an expert's quantitative assessment of stenoses.
Results of the phantom experiment indicate that the new method is significantly more accurate as compared to the
conventional binary method. Also, the results of the clinical study indicate that the computerized method is highly in
agreement with the expert's assessments.
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