A knowledge-guided active model method of cortical structure segmentation on pediatric MR images

Determination of sex differences of human cadaveric mandibular condyles using statistical shape and trait modeling

The objective of this study was to elucidate sex differences in the anatomy of human temporomandibular joint mandibular condyles using a statistical shape and trait model (SSTM). Mandibles were obtained from 16 human cadavers (79±13years). The condyles were scanned using micro-computed tomography with 27μm resolution. An image processing algorithm was used to segment the bone, determine the border of the entire mandibular condyle and trabecular bone compartments, and create triangulated meshes of the compartments. One subject was chosen as the template and was registered to the other individuals using a coherence point drift algorithm. This process positioned all vertices at corresponding anatomic locations. For the trabecular bone region, around each vertex position, the average bone image intensity, which is proportional to bone density, and microstructural traits, including trabecular bone volume fraction, thickness, separation, connectivity, and connectivity density were calculated. For the entire mandibular condyle mesh, the surface vertices were extracted to represent the overall anatomy of the condyle. Using a SSTM, the shape and trait information was reduced to a small set of independent and uncorrelated variables for each individual. Wilcoxon rank sum tests were used to test for differences in the variables between sexes. A lasso approach was used to determine a set of variables that differentiate between sexes. Male condyles were on average larger than female condyles, with complex differences in the microstructural traits. Two out of 15 principal components were statistically different between males and females (p<0.1). The lasso approach determined a set of 7 principal components that fully described the complex shape and trait differences between males and females. A SSTM was able to determine sex-dependent differences in the shape of the mandibular condyle. These differences may alter the biomechanics of the joint and contribute to the development of temporomandibular joint disease.

Development of a parametric finite element model of the proximal femur using statistical shape and density modelling

Skeletal fractures associated with bone mass loss are a major clinical problem and economic burden, and lead to significant morbidity and mortality in the ageing population. Clinical image-based measures of bone mass show only moderate correlative strength with bone strength. However, engineering models derived from clinical image data predict bone strength with significantly greater accuracy. Currently, image-based finite element (FE) models are time consuming to construct and are non-parametric. The goal of this study was to develop a parametric proximal femur FE model based on a statistical shape and density model (SSDM) derived from clinical image data. A small number of independent SSDM parameters described the shape and bone density distribution of a set of cadaver femurs and captured the variability affecting proximal femur FE strength predictions. Finally, a three-dimensional FE model of an 'unknown' femur was reconstructed from the SSDM with an average spatial error of 0.016 mm and an average bone density error of 0.037 g/cm(3).

Statistical shape modeling describes variation in tibia and femur surface geometry between Control and Incidence groups from the osteoarthritis initiative database

We hypothesize that variability in knee subchondral bone surface geometry will differentiate between patients at risk and those not at risk for developing osteoarthritis (OA) and suggest that statistical shape modeling (SSM) methods form the basis for developing a diagnostic tool for predicting the onset of OA. Using a subset of clinical knee MRI data from the osteoarthritis initiative (OAI), the objectives of this study were to (1) utilize SSM to compactly and efficiently describe variability in knee subchondral bone surface geometry and (2) determine the efficacy of SSM and rigid body transformations to distinguish between patients who are not expected to develop osteoarthritis (i.e. Control group) and those with clinical risk factors for OA (i.e. Incidence group). Quantitative differences in femur and tibia surface geometry were demonstrated between groups, although differences in knee joint alignment measures were not statistically significant, suggesting that variability in individual bone geometry may play a greater role in determining joint space geometry and mechanics. SSM provides a means of explicitly describing complete articular surface geometry and allows the complex spatial variation in joint surface geometry and joint congruence between healthy subjects and those with clinical risk of developing or existing signs of OA to be statistically demonstrated.

A digital pediatric brain structure atlas from T1-weighted MR images

Human brain atlases are indispensable tools in model-based segmentation and quantitative analysis of brain structures. However, adult brain atlases do not adequately represent the normal maturational patterns of the pediatric brain, and the use of an adult model in pediatric studies may introduce substantial bias. Therefore, we proposed to develop a digital atlas of the pediatric human brain in this study. The atlas was constructed from T1-weighted MR data set of a 9-year old, right-handed girl. Furthermore, we extracted and simplified boundary surfaces of 25 manually defined brain structures (cortical and subcortical) based on surface curvature. We constructed a 3D triangular mesh model for each structure by triangulation of the structure's reference points. Kappa statistics (cortical, 0.97; subcortical, 0.91) indicated substantial similarities between the mesh-defined and the original volumes. Our brain atlas and structural mesh models (www.stjude.org/brainatlas) can be used to plan treatment, to conduct knowledge and model-driven segmentation, and to analyze the shapes of brain structures in pediatric patients.