Statistical Shape Model Approach Research Articles

Statistical shape modeling of multi-organ anatomies with shared boundaries.

Introduction: Statistical shape modeling (SSM) is a valuable and powerful tool to generate a detailed representation of complex anatomy that enables quantitative analysis of shapes and their variations. SSM applies mathematics, statistics, and computing to parse the shape into some quantitative representation (such as correspondence points or landmarks) which can be used to study the covariance patterns of the shapes and answer various questions about the anatomical variations across the population. Complex anatomical structures have many diverse parts with varying interactions or intricate architecture. For example, the heart is a four-chambered organ with several shared boundaries between chambers. Subtle shape changes within the shared boundaries of the heart can indicate potential pathologic changes such as right ventricular overload. Early detection and robust quantification could provide insight into ideal treatment techniques and intervention timing. However, existing SSM methods do not explicitly handle shared boundaries which aid in a better understanding of the anatomy of interest. If shared boundaries are not explicitly modeled, it restricts the capability of the shape model to identify the pathological shape changes occurring at the shared boundary. Hence, this paper presents a general and flexible data-driven approach for building statistical shape models of multi-organ anatomies with shared boundaries that explicitly model contact surfaces. Methods: This work focuses on particle-based shape modeling (PSM), a state-of-art SSM approach for building shape models by optimizing the position of correspondence particles. The proposed PSM strategy for handling shared boundaries entails (a) detecting and extracting the shared boundary surface and contour (outline of the surface mesh/isoline) of the meshes of the two organs, (b) followed by a formulation for a correspondence-based optimization algorithm to build a multi-organ anatomy statistical shape model that captures morphological and alignment changes of individual organs and their shared boundary surfaces throughout the population. Results: We demonstrate the shared boundary pipeline using a toy dataset of parameterized shapes and a clinical dataset of the biventricular heart models. The shared boundary model for the cardiac biventricular data achieves consistent parameterization of the shared surface (interventricular septum) and identifies the curvature of the interventricular septum as pathological shape differences.

Unique shape variations of hind and midfoot bones in flatfoot subjects-A statistical shape modeling approach.

Flatfoot deformity is a prevalent hind- and midfoot disorder. Given its complexity, single-plane radiological measurements omit case-specific joint interaction and bone shape variations. Three-dimensional medical imaging assessment using statistical shape models provides a complete approach in characterizing bone shape variations unique to flatfoot condition. This study used statistical shape models to define specific bone shape variations of the subtalar, talonavicular, and calcaneocuboid joints that characterize flatfoot deformity, that differentiate them from healthy controls. Bones of the aforementioned joints were segmented from computed tomography scans of 40 feet. The three-dimensional hindfoot alignment angle categorized the population into 18 flatfoot subjects (≥7° valgus) and 22 controls. Statistical shape models for each joint were defined using the entire study cohort. For each joint, an average weighted shape parameter was calculated for each mode of variation, and then compared between flatfoot and controls. Significance was set at p < 0.05, with values between 0.05 ≤ p < 0.1 considered trending towards significance. The flatfoot population showed a more adducted talar head, inferiorly inclined talar neck, and posteriorly orientated medial subtalar articulation compare to controls, coupled with more navicular eversion, shallower navicular cup, and more prominent navicular tuberosity. The calcaneocuboid joint presented trends of a more adducted calcaneus, more abducted cuboid, narrower calcaneal roof, and less prominent cuboid beak compared to controls. Statistical shape model analysis identified unique shape variations which may enhance understanding and computer-aided models of the intricacies of flatfoot, leading to better diagnosis and, ultimately, surgical treatment.

3-D Ultrasonographic Quantification of Hand and Calf Muscle Volume: Statistical Shape Modeling Approach.

Accurate acquisition and segmentation of muscles are essential in 3-D freehand ultrasonography (US) to estimate in vivo muscle volume, but the source of segmentation inaccuracy in shape variation has never been the focus. This study was aimed at investigating reliability of 3-D US in the acquisition and segmentation for muscle volume of two muscles of different sizes and in identifying a primary source of measurement difference. The lateral gastrocnemius and flexor pollicis brevis of 12 healthy adults were assessed using freehand 3-D US scans. The motion-tracking data of the probe were synchronized with the B-mode ultrasound scan to reconstruct 3-D muscle volume. Statistical shape modeling was used to provide a spatial segmentation volume difference that further explains the variation around segmentation repeatability. The absolute difference of the flexor pollicis brevis was 3.5 percentage points greater than that for the lateral gastrocnemius. The highest measurement differences were observed when for inter-acquirer analysis. Statistical shape modeling revealed that the primary segmentation volume differences were at the muscle ends and edges, where the muscle interfaces with the surrounding muscles. Three-dimensional US is a reliable tool in the clinical setting, but care must be taken to ensure that acquisition and segmentation are consistent, particularly in a small muscle that interfaces with tendons and other soft tissues.

Glenohumeral joint reconstruction using statistical shape modeling.

Evaluation of the bony anatomy of the glenohumeral joint is frequently required for surgical planning and subject-specific computational modeling and simulation. The three-dimensional geometry of bones is traditionally obtained by segmenting medical image datasets, but this can be time-consuming and may not bepractical in the clinical setting. The aims of this study were twofold. Firstly, to develop and validate a statistical shape modeling approach to rapidly reconstruct the complete scapular and humeral geometries using discrete morphometric measurements that can be quickly and easily measured directly from CT, and secondly, to assess the effectiveness of statistical shape modeling in reconstruction of the entire humerus using just the landmarks in the immediate vicinity of the glenohumeral joint. The most representative shape prediction models presented in this study achieved complete scapular and humeral geometry prediction from seven or fewer morphometric measurements and yielded a mean surface root mean square (RMS) error under 2mm. Reconstruction of the entire humerus was achieved using information of only proximal humerus bony landmarks and yielding mean surface RMS errors under 3mm. The proposed statistical shape modeling facilitates rapid generation of 3D anatomical models of the shoulder, which may be useful in rapid development of personalized musculoskeletal models.

The morphological variation of acetabular defects in revision total hip arthroplasty-A statistical shape modeling approach.

Classification and evaluation of acetabular defects remain challenging and are primarily based on qualitative classification methods. That is because quantitative techniques describing variations of acetabular defects and accompanying bone loss volume are not available. This study introduces a new method based on statistical shape models (SSMs) to quantitively describe acetabular defects. This method is then applied to 87 acetabular defects to objectively describe the variations in acetabular defects typically encountered during revision total hip arthroplasty. The absolute bone loss volume, relative bone loss volume, and relative bone loss surface area with respect to the SSM-based pre-diseased anatomy were used to quantify the acetabular bone defects in different segments of the acetabular surface. The absolute bone loss volume of the average defect shape was equal to 37.0 cm3 . The first three principal modes, accounting for 62% of the total shape variation, were found to represent variations in acetabular defect morphology. The first, second, and third principal modes described, respectively, the size of the bone defects, the difference between superomedially and superolaterally migrated defects, and the degree of involvement of the posterior or anterior column. The developed SSM and the introduced approach could be used to create automated and unbiased classification methods based on quantitative data. Moreover, the proposed model and the underlying data provide the basis for a quantitative design approach where the shape and size of new acetabular implants are determined according to clinical variation present in acetabular defects.

A Statistical Model of Spine Shape and Material for Population-Oriented Biomechanical Simulation

In population-oriented ergonomics product design and musculoskeletal kinetics analysis, digital spine models of different shape, pose and material property are in great demand. The purpose of this study was to construct a parameterized finite element spine model with adjustable spine shape and material property. We used statistical shape model approach to learn inter-subject shape variations from 65 CT images of training subjects. Second order polynomial regression was used to model the age-dependent changes in vertebral material property derived from spatially aligned CT images. Finally, a parametric spine generator was developed to create finite element instances of different shapes and material properties. For quantitative analysis, the generalization ability to emulate spine shapes of different people was evaluated by fitting into 17 test CT images. The median fitting accuracy was 0.8 for Dice coefficient and 0.43 mm for average surface distance. The age-dependent bone density regression curve was also proved to well agree with large population statistics data. Finite element simulation was performed to compare how shape parameters influenced the biomechanics distribution of spine. The proposed parametric finite element whole spine model will assist the design process of new devices and biomechanical simulation towards a wide range of population.

A statistical shape modeling approach for predicting subject-specific human skull from head surface.

Human skull is an important body structure for jaw movement and facial mimic simulations. Surface head can be reconstructed using 3D scanners in a straightforward way. However, internal skull is challenging to be generated when only external information is available. Very few studies in the literature focused on the skull generation from outside head information, especially in a subject-specific manner with a complete skull. Consequently, this present study proposes a novel process for predicting a subject-specific skull with full details from a given head surface using a statistical shape modeling approach. Partial least squared regression (PLSR)-based method was used. A CT image database of 209 subjects (genders-160 males and 49 females; ages-34-88years) was used for learning head-to-skull relationship. Heads and skulls were reconstructed from CT images to extract head/skull feature points, head/skull feature distances, head-skull thickness, and head/skull volume descriptors for the learning process. A hyperparameter turning process was performed to determine the optimal numbers of head/skull feature points, PLSR components, deformation control points, and appropriate learning strategies for our learning problem. Two learning strategies (point-to-thickness with/without volume descriptor and distance-to-thickness with/without volume descriptor) were proposed. Moreover, a 10-fold cross-validation procedure was conducted to evaluate the accuracy of the proposed learning strategies. Finally, the best and worst reconstructed skulls were analyzed based on the best learning strategy with its optimal parameters. The optimal number of head/skull feature points and deformation control points are 2300 and 1300 points, respectively. The optimal number of PLSR components ranges from 4 to 8 for all learning configurations. Cross-validation showed that grand means and standard deviations of the point-to-thickness, point-to-thickness with volumes, distance-to-thickness, and distance-to-thickness with volumes learning configurations are 2.48 ± 0.27mm, 2.46 ± 0.19mm, 2.46 ± 0.15mm, and 2.48 ± 0.22mm, respectively. Thus, the distance-to-thickness is the best learning configuration for our head-to-skull prediction problem. Moreover, the mean Hausdorff distances are 2.09 ± 0.15mm and 2.64 ± 0.26mm for the best and worst predicted skull, respectively. A novel head-to-skull prediction process based on the PLSR method was developed and evaluated. This process allows, for the first time, predicting 3D subject-specific human skulls from head surface information with a very good accuracy level. As perspective, the proposed head-to-skull prediction process will be integrated into our real-time computer-aided vision system for facial animation and rehabilitation. Graphical abstract.

Statistical Shape Modeling Approach to Predict Missing Scapular Bone.

Prediction of complete and premorbid scapular anatomy is an important aspect of successful shoulder arthroplasty surgeries to treat glenohumeral arthritis and which remains elusive in the current literature. We proposed to build a statistical shape model (SSM) of the scapula and use it to build a framework to predict a complete scapular shape from virtually created scapular bone defects. The bone defects were synthetically created to imitate bone loss in the glenoid region and missing bony part in inferior and superior scapular regions. Sixty seven dry scapulae were used to build the SSM while ten external scapular shapes (not used in SSM building) were selected to map scapular shape variability using its anatomical classification. For each external scapula, four virtual bone defects were created in the superior, inferior and glenoid regions by manually removing a part of the original mesh. Using these defective shapes as prior knowledge, original shapes were reconstructed using scapula SSM and Gaussian process regression. Robustness of the scapula SSM was excellent (generality = 0.79mm, specificity = 1.74mm, first 15 principal modes of variations accounted for 95% variability). The validity and quality of the reconstruction of complete scapular bone were evaluated using two methods (1) mesh distances in terms of mean and RMS values and (2) four anatomical measures (three angles: glenoid version, glenoid inclination, and critical shoulder angle, and glenoid center location). The prediction error in the angle measures ranged from 1.0° to 2.2°. For mesh distances, highest mean and RMS error was 0.97mm and 1.30 respectively. DICE similarity coefficient between the original and predicted shapes was excellent (≥ 0.81). This framework provided high reconstruction accuracy and can be effectively embedded in the pre-surgical planning of shoulder arthroplasty or in morphology-based shoulder biomechanics modeling pipelines.

Morphologic variations of the scapula in 3-dimensions: a statistical shape model approach

Morphologic variations of the scapula and acromion have been found to be associated with shoulder pathology. This study used statistical shape modelling to quantify these variations in healthy shoulders. A statistical shape model of the scapula was created using 3-dimensional computed tomography reconstructions of 108 survey-confirmed nonpathologic shoulders of 54 patients. The mean shape and the 95% confidence interval were calculated and analyzed in the first 5 shape modes. The first 5 shape modes consisted of consecutively sized (72% of total variation), rotation of the coracoacromial complex (5%), acromial shape and slope (4%), shape of the scapular spine (2%), and acromial overhang (2%). In healthy shoulders, a certain variation in rotation of the coracoacromial complex and in acromial shape and slope was observed. These new parameters might be correlated with shoulder pathology such as glenohumeral osteoarthritis or rotator cuff tears.

Deformable Head Atlas of Chinese Adults Incorporating Inter-Subject Anatomical Variations

To date, many digital atlases of the human head have been developed, but few incorporate the anatomical variations of the population. In this paper, for the first time, we constructed two digital head atlases (DHAs) of Chinese male and female adults, which can be deformed to simulate the inter-subject anatomical variations. The atlases were developed to assist with personalized medical treatment, population-based radio magnetic simulation, and medical imaging device design. The statistical shape model approach was used to construct the atlases based on a training set of 65 computed tomography (CT) images (including those of 46 males and 19 females). To evaluate the accuracy of anatomy modeling, we compared the atlas morphometry parameters with the reference values of the Chinese population and observed the reasonable agreement between them. Compared with the existing digital human head atlases, a unique feature of the DHA is the capability of emulating individual head anatomy via model deformation. This feature is validated by registering the DHAs to patient CT images. The registered atlases provide complete anatomy of the target subjects, including the structures that are not clearly visible in the CT images.

Patient-specific assessment of dysmorphism of the femoral head-neck junction: a statistical shape model approach.

Objective quantification of anatomical variations about the femur head-neck junction in pre-operative planning for surgical intervention in femoro-acetabular impingement is problematic, as no clear definition of average normal anatomy for a specific subject exists. We have defined the normal-equivalent of a subject's anatomy by using a statistical shape model and geometric shape optimization for finding correspondences, while excluding the femoral head-neck junction during the fitting procedure. The presented technique was evaluated on a cohort of 20 patients. Difference in α-angle measurement between the actual morphology and the predicted normal-equivalent, averaged 1.3° (SD 1.7°) in the control group versus 8° (SD 7.3°) in the patient group (p < 0.05). Defining normal equivalent anatomy is effective in quantifying anatomical dysmorphism of the femoral head-neck junction and as such can improve presurgical analysis of patients diagnosed with femoro-acetabular impingement. Copyright © 2016 John Wiley & Sons, Ltd.

Automatic bone segmentation and bone-cartilage interface extraction for the shoulder joint from magnetic resonance images

We present a statistical shape model approach for automated segmentation of the proximal humerus and scapula with subsequent bone-cartilage interface (BCI) extraction from 3D magnetic resonance (MR) images of the shoulder region. Manual and automated bone segmentations from shoulder MR examinations from 25 healthy subjects acquired using steady-state free precession sequences were compared with the Dice similarity coefficient (DSC). The mean DSC scores between the manual and automated segmentations of the humerus and scapula bone volumes surrounding the BCI region were 0.926 ± 0.050 and 0.837 ± 0.059, respectively. The mean DSC values obtained for BCI extraction were 0.806 ± 0.133 for the humerus and 0.795 ± 0.117 for the scapula. The current model-based approach successfully provided automated bone segmentation and BCI extraction from MR images of the shoulder. In future work, this framework appears to provide a promising avenue for automated segmentation and quantitative analysis of cartilage in the glenohumeral joint.

Virtual reconstruction of pelvic tumor defects based on a gender-specific statistical shape model

Tumors in the pelvic region cause deformation and destruction of bony structures. Because the original pelvic anatomy cannot be adequately assessed at the tumor site, reconstruction with patient-specific implants is required. A widely used strategy for the reconstructive planning is mirroring of the contralateral side. We analyzed the statistical shape model (SSM)-based reconstruction method and compared it with the mirroring approach. Our approach used a gender-specific pelvic SSM (n = 50 for each gender) to generate implant geometries. The main objectives of this study were to analyze and evaluate the virtual anatomical reconstruction of eight tumor-damaged pelvic bones using the SSM approach. We achieved an overall mean deviation distance of 0.89 mm and 1.26 mm for the reconstruction of the equivalent defect in the healthy hemipelvis. Quantitative comparison with the mirroring method showed that the SSM-based reconstruction method reconstructs the defect with the same clinically acceptable accuracy as the mirroring method. The study demonstrates that the presented model can be a valuable tool for the planning of pelvic reconstructive surgery and implant design.