Kidney shape statistical analysis: associations with disease and anthropometric factors

BACKGROUND

Organ measurements derived from magnetic resonance imaging (MRI) have the potential to enhance our understanding of the precise phenotypic variations underlying many clinical conditions.

METHODS

We applied morphometric methods to study the kidneys by constructing surface meshes from kidney segmentations from abdominal MRI data in 38,868 participants in the UK Biobank. Using mesh-based analysis techniques based on statistical parametric maps (SPMs), we were able to detect variations in specific regions of the kidney and associate those with anthropometric traits as well as disease states including chronic kidney disease (CKD), type-2 diabetes (T2D), and hypertension. Statistical shape analysis (SSA) based on principal component analysis was also used within the disease population and the principal component scores were used to assess the risk of disease events.

RESULTS

We show that CKD, T2D and hypertension were associated with kidney shape. Age was associated with kidney shape consistently across disease groups. Body mass index (BMI) and waist-to-hip ratio (WHR) were also associated with kidney shape for the participants with T2D. Using SSA, we were able to capture kidney shape variations, relative to size, angle, straightness, width, length, and thickness of the kidneys, within disease populations. We identified significant associations between both left and right kidney length and width and incidence of CKD (hazard ratio (HR): 0.74, 95% CI: 0.61-0.90, p < 0.05, in the left kidney; HR: 0.76, 95% CI: 0.63-0.92, p < 0.05, in the right kidney) and hypertension (HR: 1.16, 95% CI: 1.03-1.29, p < 0.05, in the left kidney; HR: 0.87, 95% CI: 0.79-0.96, p < 0.05, in the right kidney).

CONCLUSIONS

The results suggest that shape-based analysis of the kidneys can augment studies aiming at the better categorisation of pathologies associated with chronic kidney conditions.

Statistical shape analysis of maxillary palatal morphology in patients with palatally displaced canines

OBJECTIVE

Maxillary morphology has long been a subject of interest due to its possible impact on palatally and labially displaced canines. This study aims to conduct a comparison of the palate morphology between individuals with palatal and labially displaced canines and control subjects using statistical shape analysis on a coronal cross-sectional of CBCT images.

MATERIALS AND METHODS

Patients aged between 12 and 43 years with palatally or labially displaced canines referred to Hamadan School of Dentistry between 2014 and 2019 were recruited for this retrospective study. The sample included 29 palatally displaced canines (PDC), 20 labially displaced canines (LDC), and 20 control groups (CG). Initially, the maxillary palate coronal section was acquired and landmarked in the region between the right and the left first molar. Procrustes and principal component analyses were used to identify the primary patterns of palatal shape variation. Statistical tests were then performed to examine both shape and size differences.

RESULTS

According to the results of Hotelling's T2 test, there is a significant difference between the mean shape of palate in PDC and CG (P = 0.009), while the difference between the PDC-LDC and LDC-CG groups is not significant. The longest full Procrustes distance was observed between PDC and CG (distance = 0.043), and the shortest full Procrustes distance was observed between LDC and CG (distance = 0.029). The first two principal components accounted for 84.47% of the total variance. The predictive accuracy of the discriminant analysis model showed that 72.46% of cases were correctly classified into the three study groups.

CONCLUSIONS

In terms of centroid size, there was no significant difference in the sectional area between the three groups, but the difference between the mean shape of palate in the PDC and CG groups was significant. The PDC group showed more prominent mid-palatal area in the molar region.

Aortic Dissection is Determined by Specific Shape and Hemodynamic Interactions

The aim of this study was to determine whether specific three-dimensional aortic shape features, extracted via statistical shape analysis (SSA), correlate with the development of thoracic ascending aortic dissection (TAAD) risk and associated aortic hemodynamics. Thirty-one patients followed prospectively with ascending thoracic aortic aneurysm (ATAA), who either did (12 patients) or did not (19 patients) develop TAAD, were included in the study, with aortic arch geometries extracted from computed tomographic angiography (CTA) imaging. Arch geometries were analyzed with SSA, and unsupervised and supervised (linked to dissection outcome) shape features were extracted with principal component analysis (PCA) and partial least squares discriminant analysis (PLS-DA), respectively. We determined PLS-DA to be effective at separating dissection and no-dissection patients ([Formula: see text]), with decreased tortuosity and more equal ascending and descending aortic diameters associated with higher dissection risk. In contrast, neither PCA nor traditional morphometric parameters (maximum diameter, tortuosity, or arch volume) were effective at separating dissection and no-dissection patients. The arch shapes associated with higher dissection probability were supported with hemodynamic insight. Computational fluid dynamics (CFD) simulations revealed a correlation between the PLS-DA shape features and wall shear stress (WSS), with higher maximum WSS in the ascending aorta associated with increased risk of dissection occurrence. Our work highlights the potential importance of incorporating higher dimensional geometric assessment of aortic arch anatomy in TAAD risk assessment, and in considering the interdependent influences of arch shape and hemodynamics as mechanistic contributors to TAAD occurrence.

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.

Feasibility of a longitudinal statistical atlas model to study aortic growth in congenital heart disease

Studying anatomical shape progression over time is of utmost importance to refine our understanding of clinically relevant processes. These include vascular remodeling, such as aortic dilation, which is particularly important in some congenital heart defects (CHD). A novel methodological framework for three-dimensional shape analysis has been applied for the first time in a CHD scenario, i.e., bicuspid aortic valve (BAV) disease, the most common CHD. Three-dimensional aortic shapes (n = 94) reconstructed from cardiovascular magnetic resonance imaging (MRI) data as surface meshes represented the input for a longitudinal atlas model, using multiple scans over time (n = 2-4 per patient). This model relies on diffeomorphism transformations in the absence of point-to-point correspondence, and on the right combination of initialization, estimation and registration parameters. We computed the shape trajectory of an average disease progression in our cohort, as well as time-dependent parameters, geometric variations and the average shape of the population. Results cover a spatiotemporal spectrum of visual and numerical information that can be further used to run clinical associations. This proof-of-concept study demonstrates the feasibility of applying advanced statistical shape models to track disease progression and stratify patients with CHD.

Evaluation of the corpus callosum shape in patients with obstructive sleep apnea

BACKGROUND

The aim of this study was to examine whether or not there was a difference in corpus callosum shape between patients with mild to moderate and severe obstructive sleep apnea (OSA) compared with patients who have simple snoring.

METHODS

The landmark coordinate data was obtained from the mid-sagittal magnetic resonance imaging (MRI) images of 70 patients who underwent polysomnography. For comparisons, mild and moderate OSA groups were combined and analyses were performed on three groups; simple snoring/control group, mild or moderate OSA group, and severe OSA group.

RESULTS

The corpus callosum shape of controls was significantly different from that of the severe OSA group. The most prominent deformities were observed in the genu and rostral body of the corpus callosum for the patients with severe OSA. No significant difference was found between mild/moderate OSA group and simple snoring group in terms of global corpus callosum shape.

CONCLUSION

The data demonstrated that severe OSA patients have structural changes in the corpus callosum and deformities may vary as the severity of disease changes.

Statistical shape analysis of putamen in early-onset Parkinson's disease

OBJECTIVE

To investigate the shape differences in the putamen of early-onset Parkinson's patients compared with healthy controls and to assess and to assess sub-regional brain abnormalities.

METHODS

This study was conducted using the 3-T MRI scans of 23 early-onset Parkinson's patients and age and gender matched control subjects. Landmark coordinate data obtained and Procrustes analysis was used to compare mean shapes. The relationships between the centroid sizes of the left and right putamen, and the durations of disease examined using growth curve models.

RESULTS

While there was a significant difference between the right putamen shape of control and patient groups, there was not found a significant difference in terms of left putamen. Sub-regional analyses showed that for the right putamen, the most prominent deformations were localized in the middle-posterior putamen and minimal deformations were seen in the anterior putamen.

CONCLUSION

Although they were not as pronounced as those in the right putamen, the deformations in the left putamen mimic the deformations in the right putamen which are found mainly in the middle-posterior putamen and at a lesser extend in the anterior putamen.

Rigid motion invariant statistical shape modeling based on discrete fundamental forms data from the osteoarthritis initiative and the Alzheimer' disease neuroimaging initiative

We present a novel approach for nonlinear statistical shape modeling that is invariant under Euclidean motion and thus alignment-free. By analyzing metric distortion and curvature of shapes as elements of Lie groups in a consistent Riemannian setting, we construct a framework that reliably handles large deformations. Due to the explicit character of Lie group operations, our non-Euclidean method is very efficient allowing for fast and numerically robust processing. This facilitates Riemannian analysis of large shape populations accessible through longitudinal and multi-site imaging studies providing increased statistical power. Additionally, as planar configurations form a submanifold in shape space, our representation allows for effective estimation of quasi-isometric surfaces flattenings. We evaluate the performance of our model w.r.t. shape-based classification of hippocampus and femur malformations due to Alzheimer's disease and osteoarthritis, respectively. In particular, we outperform state-of-the-art classifiers based on geometric deep learning as well as statistical shape modeling especially in presence of sparse training data. To provide insight into the model's ability of capturing biological shape variability, we carry out an analysis of specificity and generalization ability.

Fitting unbranching skeletal structures to objects

Representing an object by a skeletal structure can be powerful for statistical shape analysis if there is good correspondence of the representations within a population. Many anatomic objects have a genus-zero boundary and can be represented by a smooth unbranching skeletal structure that can be discretely approximated. We describe how to compute such a discrete skeletal structure ("d-s-rep") for an individual 3D shape with the desired correspondence across cases. The method involves fitting a d-s-rep to an input representation of an object's boundary. A good fit is taken to be one whose skeletally implied boundary well approximates the target surface in terms of low order geometric boundary properties: (1) positions, (2) tangent fields, (3) various curvatures. Our method involves a two-stage framework that first, roughly yet consistently fits a skeletal structure to each object and second, refines the skeletal structure such that the shape of the implied boundary well approximates that of the object. The first stage uses a stratified diffeomorphism to produce topologically non-self-overlapping, smooth and unbranching skeletal structures for each object of a population. The second stage uses loss terms that measure geometric disagreement between the skeletally implied boundary and the target boundary and avoid self-overlaps in the boundary. By minimizing the total loss, we end up with a good d-s-rep for each individual shape. We demonstrate such d-s-reps for various human brain structures. The framework is accessible and extensible by clinical users, researchers and developers as an extension of SlicerSALT, which is based on 3D Slicer.

The Effect of Geometric and Hemodynamic Parameters on Blood Flow Efficiency in Repaired Tetralogy of Fallot Patients

Surgical repair of Tetralogy of Fallot (TOF) involves a series of steps to remove right ventricular outflow tract and pulmonary artery obstruction. However, the large degree of anatomic variability among preoperative TOF patients may impact the effectiveness of different repair strategies and, subsequently, different geometric modifications for different patients. This study investigates the relationships between geometric and hemodynamic parameters and mechanical energy efficiency for a patient-specific dataset of 16 postoperative TOF repairs, using morphometric and statistical shape analyses, as well as computational fluid dynamics simulations with physiologically-relevant inlet and outlet boundary conditions. Quantitatively, negative correlations were found between the right and left pulmonary artery centerline tract cumulative torsion and energy efficiency (r = - 0.65, p = 0.01, for both). A positive correlation was also found for a statistical shape mode associated with skewing of the geometric sub-regions (r = 0.61, p = 0.01). Qualitatively, medium- and low-efficiency geometries exhibit disturbed flow and much more proximal vortex formation as compared to a high-efficiency geometry. Thus, it is recommended, as much as possible, to both relieve and avoid the introduction of torsion into the patient's anatomy during surgical repair of TOF.

Detection of clustered anomalies in single-voxel morphometry as a rapid automated method for identifying intracranial aneurysms

Unruptured intracranial aneurysms (UIAs) are prevalent neurovascular anomalies which, in rare circumstances, rupture to cause a catastrophic subarachnoid haemorrhage. Although surgical management can reduce rupture risk, the majority of UIAs exist undiscovered until rupture. Current clinical practice in the detection of UIAs relies heavily on manual radiological review of standard imaging modalities. Recent computer-aided UIA diagnoses can sensitively detect and measure UIAs within cranial angiograms but remain limited to low specificities whose output also requires considerable radiologist interpretation not amenable to broad screening efforts. To address these limitations, we have developed a novel automatic pipeline algorithm which inputs medical images and outputs detected UIAs by characterising single-voxel morphometry of segmented neurovasculature. Once neurovascular anatomy of a specified resolution is segmented, correlations between voxel-specific morphometries are estimated and spatially-clustered outliers are identified as UIA candidates. Our automated solution detects UIAs within magnetic resonance angiograms (MRA) at unmatched 86% specificity and 81% sensitivity using 3 min on a conventional laptop. Our approach does not rely on interpatient comparisons or training datasets which could be difficult to amass and process for rare incidentally discovered UIAs within large MRA files, and in doing so, is versatile to user-defined segmentation quality, to detection sensitivity, and across a range of imaging resolutions and modalities. We propose this method as a unique tool to aid UIA screening, characterisation of abnormal vasculature in at-risk patients, morphometry-based rupture risk prediction, and identification of other vascular abnormalities.

In vitro chemical and physical toxicities of polystyrene microfragments in human-derived cells

With the increase in plastic production, a variety of toxicological studies on microplastics have been conducted as microplastics can be accumulated in the human body and cause unknown disease. However, previous studies have mainly assessed the toxicity of sphere-type microbeads, which may differ from randomly-shaped microplastics in a real environment. Here, we conducted in vitro toxicology analysis for randomly-shaped microplastics based on the hypotheses that (1) physical cytotoxicity is affected by nano-/micro-size roughness in polystyrene (PS) microfragments and (2) chemical toxicity is caused by chemical reagents from microplastics. We confirmed that the PS microfragments increased the acute inflammation of immune cells 20 times than control, the production of reactive oxygen species, and cell death of fibroblasts and cancer cells by releasing chemical reagents. In addition, when the PS microfragments were in direct contact with fibroblasts and red blood cells, the physical stress caused by them resulted in lactose dehydrogenase and hemoglobin release, respectively, due to cell membrane damage and hemolysis. This phenomenon was amplified when the concentration and roughness of the microfragments increased. Moreover, we quantitatively analyzed roughness differences between microplastics, which revealed a strong relationship between the physical damage of cells and the roughness of microplastics.

Handling confounding variables in statistical shape analysis - application to cardiac remodelling

Statistical shape analysis is a powerful tool to assess organ morphologies and find shape changes associated to a particular disease. However, imbalance in confounding factors, such as demographics might invalidate the analysis if not taken into consideration. Despite the methodological advances in the field, providing new methods that are able to capture complex and regional shape differences, the relationship between non-imaging information and shape variability has been overlooked. We present a linear statistical shape analysis framework that finds shape differences unassociated to a controlled set of confounding variables. It includes two confounding correction methods: confounding deflation and adjustment. We applied our framework to a cardiac magnetic resonance imaging dataset, consisting of the cardiac ventricles of 89 triathletes and 77 controls, to identify cardiac remodelling due to the practice of endurance exercise. To test robustness to confounders, subsets of this dataset were generated by randomly removing controls with low body mass index, thus introducing imbalance. The analysis of the whole dataset indicates an increase of ventricular volumes and myocardial mass in athletes, which is consistent with the clinical literature. However, when confounders are not taken into consideration no increase of myocardial mass is found. Using the downsampled datasets, we find that confounder adjustment methods are needed to find the real remodelling patterns in imbalanced datasets.

Three-dimensional regional bi-ventricular shape remodeling is associated with exercise capacity in endurance athletes

AIMS

Endurance athletes develop cardiac remodeling to cope with increased cardiac output during exercise. This remodeling is both anatomical and functional and shows large interindividual variability. In this study, we quantify local geometric ventricular remodeling related to long-standing endurance training and assess its relationship with cardiovascular performance during exercise.

METHODS

We extracted 3D models of the biventricular shape from end-diastolic cine magnetic resonance images acquired from a cohort of 89 triathlon athletes and 77 healthy sedentary subjects. Additionally, the athletes underwent cardio-pulmonary exercise testing, together with an echocardiographic study at baseline and few minutes after maximal exercise. We used statistical shape analysis to identify regional bi-ventricular shape differences between athletes and non-athletes.

RESULTS

The ventricular shape was significantly different between athletes and controls (p < 1e-6). The observed regional remodeling in the right heart was mainly a shift of the right ventricle (RV) volume distribution towards the right ventricular infundibulum, increasing the overall right ventricular volume. In the left heart, there was an increment of left ventricular mass and a dilation of the left ventricle. Within athletes, the amount of such remodeling was independently associated to higher peak oxygen pulse (p < 0.001) and weakly with greater post-exercise RV free wall longitudinal strain (p = 0.03).

CONCLUSIONS

We were able to identify specific bi-ventricular regional remodeling induced by long-lasting endurance training. The amount of remodeling was associated with better cardiopulmonary performance during an exercise test.

Statistical shape analyses of trigonocephaly patients

PURPOSE

Surgery is the first treatment option for patients with metopic craniosynostosis. Fronto-orbital advancement is the preferred method for correction of isolated trigonocephaly, but it is hard to understand whether surgery has been successful mainly in an early period. We aim to investigate the shape differences in the head shapes of trigonocephaly patients compared between preoperative and postoperative term.

METHODS

Cranial shape data were collected from the two-dimensional digital images. The Generalized Procrustes analysis was used to obtain mean shapes of the preoperative and postoperative term. The shape deformation of the frontal calvarium from preoperative to the postoperative term was evaluated using the thin-plate spline (TPS) method.

RESULTS

There was significant cranial shape difference between preoperative and postoperative term. The high-level deformations for preoperative to postoperative term determined seen in TPS graphic. Highest deformation was observed at the bifrontal dimension especially at nasion and posterior edge of the forehead.

CONCLUSIONS

In this study, we showed that the shape difference and structural deformation of the calvarium were correlated with the metopic craniosynostosis. The present study also shows that preoperative and postoperative head shapes of patients with trigonocephaly can be compared using the landmark-based geometrical morphometric method by taking into consideration the topographic distribution.

An automated workflow based on hip shape improves personalized risk prediction for hip osteoarthritis in the CHECK study

OBJECTIVE

To design an automated workflow for hip radiographs focused on joint shape and tests its prognostic value for future hip osteoarthritis.

DESIGN

We used baseline and 8-year follow-up data from 1,002 participants of the CHECK-study. The primary outcome was definite radiographic hip osteoarthritis (rHOA) (Kellgren-Lawrence grade ≥2 or joint replacement) at 8-year follow-up. We designed a method to automatically segment the hip joint from radiographs. Subsequently, we applied machine learning algorithms (elastic net with automated parameter optimization) to provide the Shape-Score, a single value describing the risk for future rHOA based solely on joint shape. We built and internally validated prediction models using baseline demographics, physical examination, and radiologists scores and tested the added prognostic value of the Shape-Score using Area-Under-the-Curve (AUC). Missing data was imputed by multiple imputation by chained equations. Only hips with pain in the corresponding leg were included.

RESULTS

84% were female, mean age was 56 (±5.1) years, mean BMI 26.3 (±4.2). Of 1,044 hips with pain at baseline and complete follow-up, 143 showed radiographic osteoarthritis and 42 were replaced. 91.5% of the hips had follow-up data available. The Shape-Score was a significant predictor of rHOA (odds ratio per decimal increase 5.21, 95%-CI (3.74-7.24)). The prediction model using demographics, physical examination, and radiologists scores demonstrated an AUC of 0.795, 95%-CI (0.757-0.834). After addition of the Shape-Score the AUC rose to 0.864, 95%-CI (0.833-0.895).

CONCLUSIONS

Our Shape-Score, automatically derived from radiographs using a novel machine learning workflow, may strongly improve risk prediction in hip osteoarthritis.

Statistical Shape Analysis of Subthalamic Nucleus in Patients with Parkinson Disease

OBJECTIVE

Subthalamic nucleus (STN) is the most targeted localization in the treatment of Parkinson disease (PD) with deep brain stimulation. However, no studies have been found in the literature about possible shape changes of STN in the literature. We aimed to investigate possible shape changes in the STN and the relationship between shape changes and disease duration in PD patients by using statistical analysis.

METHODS

Patients who were diagnosed with idiopathic PD and controls were enrolled in this study. Age, sex, and disease duration of all cases were recorded. Turbo-spin-echo T2-weighted axial series parallel to the skull base in each case containing midbrain images were obtained, including the whole STN. Standard anatomic landmarks were selected and marked on each digital image using a special software in all cases. Statistical geometric shape and deformation analysis of STN was performed in 2 groups.

RESULTS

Forty-three patients with PD and 50 age/sex-matched controls were enrolled in this study. There were statistically significant left and right STN shape differences between the groups. Maximum deformation was seen in the dorsolateral parts of both STNs. General shape variability of the STNs was found on the left (0.096) and right (0.049).

CONCLUSIONS

Significant shape differences and remarkable deformation of STN are seen in patients with PD compared with controls. Maximum deformation was observed in the dorsolateral part of the STN, and with the increase in the duration of the PD, shape differences and deformations became more prominent.

A model order reduction approach to create patient-specific mechanical models of human liver in computational medicine applications

BACKGROUND AND OBJECTIVE

This paper focuses on computer simulation aspects of Digital Twin models in the medical framework. In particular, it addresses the need of fast and accurate simulators for the mechanical response at tissue and organ scale and the capability of integrating patient-specific anatomy from medical images to pinpoint the individual variations from standard anatomical models.

METHODS

We propose an automated procedure to create mechanical models of the human liver with patient-specific geometry and real time capabilities. The method hinges on the use of Statistical Shape Analysis to extract the relevant anatomical features from a database of medical images and Model Order Reduction to compute an explicit parametric solution for the mechanical response as a function of such features. The Sparse Subspace Learning, coupled with a Finite Element solver, was chosen to create low-rank solutions using a non-intrusive sparse sampling of the feature space.

RESULTS

In the application presented in the paper, the statistical shape model was trained on a database of 385 three dimensional liver shapes, extracted from medical images, in order to create a parametrized representation of the liver anatomy. This parametrization and an additional parameter describing the breathing motion in linear elasticity were then used as input in the reduced order model. Results show a consistent agreement with the high fidelity Finite Element models built from liver images that were excluded from the training dataset. However, we evidence in the discussion the difficulty of having compact shape parametrizations arising from the extreme variability of the shapes found in the dataset and we propose potential strategies to tackle this issue.

CONCLUSIONS

A method to represent patient-specific real-time liver deformations during breathing is proposed in linear elasticity. Since the proposed method does not require any adaptation to the direct Finite Element solver used in the training phase, the procedure can be easily extended to more complex non-linear constitutive behaviors - such as hyperelasticity - and more general load cases. Therefore it can be integrated with little intrusiveness to generic simulation software including more sophisticated and realistic models.

Statistical Shape Models: Understanding and Mastering Variation in Anatomy

In our chapter we are describing how to reconstruct three-dimensional anatomy from medical image data and how to build Statistical 3D Shape Models out of many such reconstructions yielding a new kind of anatomy that not only allows quantitative analysis of anatomical variation but also a visual exploration and educational visualization. Future digital anatomy atlases will not only show a static (average) anatomy but also its normal or pathological variation in three or even four dimensions, hence, illustrating growth and/or disease progression.Statistical Shape Models (SSMs) are geometric models that describe a collection of semantically similar objects in a very compact way. SSMs represent an average shape of many three-dimensional objects as well as their variation in shape. The creation of SSMs requires a correspondence mapping, which can be achieved e.g. by parameterization with a respective sampling. If a corresponding parameterization over all shapes can be established, variation between individual shape characteristics can be mathematically investigated.We will explain what Statistical Shape Models are and how they are constructed. Extensions of Statistical Shape Models will be motivated for articulated coupled structures. In addition to shape also the appearance of objects will be integrated into the concept. Appearance is a visual feature independent of shape that depends on observers or imaging techniques. Typical appearances are for instance the color and intensity of a visual surface of an object under particular lighting conditions, or measurements of material properties with computed tomography (CT) or magnetic resonance imaging (MRI). A combination of (articulated) Statistical Shape Models with statistical models of appearance lead to articulated Statistical Shape and Appearance Models (a-SSAMs).After giving various examples of SSMs for human organs, skeletal structures, faces, and bodies, we will shortly describe clinical applications where such models have been successfully employed. Statistical Shape Models are the foundation for the analysis of anatomical cohort data, where characteristic shapes are correlated to demographic or epidemiologic data. SSMs consisting of several thousands of objects offer, in combination with statistical methods or machine learning techniques, the possibility to identify characteristic clusters, thus being the foundation for advanced diagnostic disease scoring.

An efficient Riemannian statistical shape model using differential coordinates: With application to the classification of data from the Osteoarthritis Initiative

We propose a novel Riemannian framework for statistical analysis of shapes that is able to account for the nonlinearity in shape variation. By adopting a physical perspective, we introduce a differential representation that puts the local geometric variability into focus. We model these differential coordinates as elements of a Lie group thereby endowing our shape space with a non-Euclidean structure. A key advantage of our framework is that statistics in a manifold shape space becomes numerically tractable improving performance by several orders of magnitude over state-of-the-art. We show that our Riemannian model is well suited for the identification of intra-population variability as well as inter-population differences. In particular, we demonstrate the superiority of the proposed model in experiments on specificity and generalization ability. We further derive a statistical shape descriptor that outperforms the standard Euclidean approach in terms of shape-based classification of morphological disorders.

Statistical shape analysis: From landmarks to diffeomorphisms

We offer a blazingly brief review of evolution of shape analysis methods in medical imaging. As the representations and the statistical models grew more sophisticated, the problem of shape analysis has been gradually redefined to accept images rather than binary segmentations as a starting point. This transformation enabled shape analysis to take its rightful place in the arsenal of tools for extracting and understanding patterns in large clinical image sets. We speculate on the future developments in shape analysis and potential applications that would bring this mathematically rich area to bear on clinical practice.

Statistical shape analysis using 3D Poisson equation--A quantitatively validated approach

Statistical shape analysis has been an important area of research with applications in biology, anatomy, neuroscience, agriculture, paleontology, etc. Unfortunately, the proposed methods are rarely quantitatively evaluated, and as shown in recent studies, when they are evaluated, significant discrepancies exist in their outputs. In this work, we concentrate on the problem of finding the consistent location of deformation between two population of shapes. We propose a new shape analysis algorithm along with a framework to perform a quantitative evaluation of its performance. Specifically, the algorithm constructs a Signed Poisson Map (SPoM) by solving two Poisson equations on the volumetric shapes of arbitrary topology, and statistical analysis is then carried out on the SPoMs. The method is quantitatively evaluated on synthetic shapes and applied on real shape data sets in brain structures.

Statistical shape analysis of temporal lobe in mesial temporal sclerosis patients

BACKGROUND

Surgery is regarded as a common treatment option for patients with mesial temporal lobe epilepsy due to hippocampal sclerosis but sometimes deciding this diagnosis can be very difficult. We aim to investigate the shape differences in the temporal lobe of mesial temporal sclerosis epilepsy patients compared with healthy controls, investigating the side difference and, if present, assessing the clinical application of this situation.

METHOD

The MRI scans of mesial TLE patients and controls were retrospectively reviewed. Temporal lobe data were collected from the two-dimensional digital images. Standard anthropometric landmarks were selected and marked on each digital image using TPSDIG 2.04 software. Eight anatomic landmarks were marked on images. A generalized Procrustes analysis was used to evaluate the shape difference. The shape deformation of the temporal lobe from control to patient was evaluated using the TPS method.

RESULTS

There were statistically significant TL shape differences between groups. High level deformations for the left and right side from the control to patient group were seen in the TPS graphic. The highest deformation was determined at the inferior lateral temporal midpoint of the middle temporal gyri and superior temporal landmark points of both the right and left sides.

CONCLUSION

Our study for the first time demonstrated temporal shape differences in TLE patients using a landmark-based geometrical morphometric method by taking into consideration the topographic distribution of TL.

Examining the Efficiency of Models Using Tangent Coordinates or Principal Component Scores in Allometry Studies

Most of the studies in medical and biological sciences are related to the examination of geometrical properties of an organ or organism. Growth and allometry studies are important in the way of investigating the effects of diseases and the environmental factors effects on the structure of the organ or organism. Thus, statistical shape analysis has recently become more important in the medical and biological sciences. Shape is all geometrical information that remains when location, scale and rotational effects are removed from an object. Allometry, which is a relationship between size and shape, plays an important role in the development of statistical shape analysis. The aim of the present study was to compare two different models for allometry which includes tangent coordinates and principal component scores of tangent coordinates as dependent variables in multivariate regression analysis. The results of the simulation study showed that the model constructed by taking tangent coordinates as dependent variables is more appropriate than the model constructed by taking principal component scores of tangent coordinates as dependent variables, for all sample sizes.

Sizing the shape: understanding morphometrics

PURPOSE

One of the most fundamental limitations associated with the conventional cephalometrics is its inability to delineate size from shape as it depends mainly on linear and angular measurements. However, the biological structures warrant greater description in terms of shape and form for better comparison of variation in a particular population. To overcome these shortcomings, morphometrics are now being employed for describing the biological structures in terms of quantifying the shape and form. Also, statistical analysis is being applied to find the variability of this form in the population. The present paper assesses the use of the Procuste superimposition technique and the subsequent form analysis by the principal component analysis (PCA).

MATERIALS AND METHODS

The lateral cephalograms of 10 adult females were taken from existing records, traced & digitized & then superimposed by means of procuste superimposition. A comparison was made with the conventional superimposition methods based on arbitrary reference planes like cranial base, FHP, SN. The statistical analysis for assessment of shape variability of the structures seen on the lateral cephalogram was done by calculating the principal components for 3 out of these 10 samples.

RESULTS

The conventional superimposition methods do not provide realistic picture of variation in the biological structures as they themselves are prone to variability even in a particular population.

CONCLUSION

Concepts in Morphometrics can be applied for the purpose of orthodontic assessment of a particular patient with regards to his craniofacial morphology. With the help of morphometrics, norms for a population can be determined based on all the kinds of variations present naturally in that particular population & individuals can thus be compared more realistically regarding the morphological variations.

Five year retrospective case series of adnexal torsion

AIMS AND OBJECTIVES

Adnexal torsion is a rare gynaecological emergency that requires an early surgical intervention to save the adnexa from irreversible damage .Our study is about clinical presentation and management approach of adnexal torsion in a tertiary care centre.

STUDY DESIGN

Retrospective study.

MATERIALS AND METHODS

Review of case records during the five years of 2008 November -2013 November in Amrita Institute of Medical Sciences, Kochi, India.

RESULTS

Adnexal torsion was found mainly in the reproductive age. Ultrasound was the most commonly used imaging modality. Benign tumours predispose to torsion. Torsion occurred during postovulatory period in many cases. Polycystic ovaries were a risk factor for unexplained torsion in younger age groups. Diagnosis of adnexal torsion was mostly intra operative by direct visualisation of the rotated adnexa. Laparoscopy was the preferred method of surgical intervention. Ovarian conservation was tried in majority of the child bearing age groups.

CONCLUSION

Adnexal torsion is a rare emergency which requires a high index of clinical suspicion for diagnosis as the symptoms are non specific. Imaging helps in diagnosis but most of them are diagnosed intra operatively. Laparoscopic conservative surgery is the preferred surgical approach especially in younger age groups. An early surgical intervention helps in salvaging the adnexa and prevents further complications.

Effects of sex chromosome dosage on corpus callosum morphology in supernumerary sex chromosome aneuploidies

Background

Supernumerary sex chromosome aneuploidies (sSCA) are characterized by the presence of one or more additional sex chromosomes in an individual’s karyotype; they affect around 1 in 400 individuals. Although there is high variability, each sSCA subtype has a characteristic set of cognitive and physical phenotypes. Here, we investigated the differences in the morphometry of the human corpus callosum (CC) between sex-matched controls 46,XY (N =99), 46,XX (N =93), and six unique sSCA karyotypes: 47,XYY (N =29), 47,XXY (N =58), 48,XXYY (N =20), 47,XXX (N =30), 48,XXXY (N =5), and 49,XXXXY (N =6).

Methods

We investigated CC morphometry using local and global area, local curvature of the CC boundary, and between-landmark distance analysis (BLDA). We hypothesized that CC morphometry would vary differentially along a proposed spectrum of Y:X chromosome ratio with supernumerary Y karyotypes having the largest CC areas and supernumerary X karyotypes having significantly smaller CC areas. To investigate this, we defined an sSCA spectrum based on a descending Y:X karyotype ratio: 47,XYY, 46,XY, 48,XXYY, 47,XXY, 48,XXXY, 49,XXXXY, 46,XX, 47,XXX. We similarly explored the effects of both X and Y chromosome numbers within sex. Results of shape-based metrics were analyzed using permutation tests consisting of 5,000 iterations.

Results

Several subregional areas, local curvature, and BLDs differed between groups.

Moderate associations were found between area and curvature in relation to the spectrum and X and Y chromosome counts. BLD was strongly associated with X chromosome count in both male and female groups.

Conclusions

Our results suggest that X- and Y-linked genes have differential effects on CC morphometry. To our knowledge, this is the first study to compare CC morphometry across these extremely rare groups.

A statistical shape model of trochlear dysplasia of the knee

BACKGROUND

Trochlear dysplasia is known as the primary predisposing factor for patellar dislocation. Current methods to describe trochlear dysplasia are mainly qualitative or based on a limited number of discrete measurements. The purpose of this study is to apply statistical shape analysis to take the full geometrical complexity of trochlear dysplasia into account.

METHODS

Statistical shape analysis was applied to 20 normal and 20 trochlear dysplastic distal femur models, including the cartilage.

RESULTS

This study showed that the trochlea was anteriorized, proximalized and lateralized and that the mediolateral width and the notch width were decreased in the trochlear dysplastic femur compared to the normal femur. The first three principal components of the trochlear dysplastic femurs, accounting for 79.7% of the total variation, were size, sulcus angle and notch width. Automated classification of the trochlear dysplastic and normal femora achieved a sensitivity of 85% and a specificity of 95%.

CONCLUSIONS

This study shows that shape analysis is an outstanding method to visualise the location and magnitude of shape abnormalities. Improvement of automated classification and subtyping within the trochlear dysplastic group are expected when larger training sets are used.

CLINICAL RELEVANCE

Classification of trochlear dysplasia, especially borderline cases may be facilitated by automated classification. Furthermore, the identification of a decreased notch width in association with an increased sulcus angle can also contribute to the diagnosis of trochlear dysplasia.

Statistical shape analysis of clavicular cortical bone with applications to the development of mean and boundary shape models

During car collisions, the shoulder belt exposes the occupant's clavicle to large loading conditions which often leads to a bone fracture. To better understand the geometric variability of clavicular cortical bone which may influence its injury tolerance, twenty human clavicles were evaluated using statistical shape analysis. The interior and exterior clavicular cortical bone surfaces were reconstructed from CT-scan images. Registration between one selected template and the remaining 19 clavicle models was conducted to remove translation and rotation differences. The correspondences of landmarks between the models were then established using coordinates and surface normals. Three registration methods were compared: the LM-ICP method; the global method; and the SHREC method. The LM-ICP registration method showed better performance than the global and SHREC registration methods, in terms of compactness, generalization, and specificity. The first four principal components obtained by using the LM-ICP registration method account for 61% and 67% of the overall anatomical variation for the exterior and interior cortical bone shapes, respectively. The length was found to be the most significant variation mode of the human clavicle. The mean and two boundary shape models were created using the four most significant principal components to investigate the size and shape variation of clavicular cortical bone. In the future, boundary shape models could be used to develop probabilistic finite element models which may help to better understand the variability in biomechanical responses and injuries to the clavicle.

Lateral ventricle morphology analysis via mean latitude axis

Statistical shape analysis has emerged as an insightful method for evaluating brain structures in neuroimaging studies, however most shape frameworks are surface based and thus directly depend on the quality of surface alignment. In contrast, medial descriptions employ thickness information as alignment-independent shape metric. We propose a joint framework that computes local medial thickness information via a mean latitude axis from the well-known spherical harmonic (SPHARM-PDM) shape framework. In this work, we applied SPHARM derived medial representations to the morphological analysis of lateral ventricles in neonates. Mild ventriculomegaly (MVM) subjects are compared to healthy controls to highlight the potential of the methodology. Lateral ventricles were obtained from MRI scans of neonates (9-144 days of age) from 30 MVM subjects as well as age- and sex-matched normal controls (60 total). SPHARM-PDM shape analysis was extended to compute a mean latitude axis directly from the spherical parameterization. Local thickness and area was straightforwardly determined. MVM and healthy controls were compared using local MANOVA and compared with the traditional SPHARM-PDM analysis. Both surface and mean latitude axis findings differentiate successfully MVM and healthy lateral ventricle morphology. Lateral ventricles in MVM neonates show enlarged shapes in tail and head. Mean latitude axis is able to find significant differences all along the lateral ventricle shape, demonstrating that local thickness analysis provides significant insight over traditional SPHARM-PDM. This study is the first to precisely quantify 3D lateral ventricle morphology in MVM neonates using shape analysis.