Statistical shape modeling of cam femoroacetabular impingement

Femurs in patients with hip dysplasia have fundamental shape differences compared with cam femoroacetabular impingement

Femoral deformities are common in developmental dysplasia of the hip (DDH), but decisions about how to treat them are not standardized. Of interest are deformities that may be akin to cam femoroacetabular impingement (FAI). We used three-dimensional and two-dimensional measures to clarify the similarities and differences in proximal femur shape variation among female patients with DDH (n = 68) or cam FAI (n = 60). Three-dimensional measures included femoral head asphericity, as well as shape variation using statistical shape modeling and principal component analysis (PCA). Two-dimensional measures included the α-angle, head-neck offset (HNO) and the neck-shaft angle (NSA). Significant shape variations were captured in the first five PCA modes, with the greatest shared variation between groups being the length from the lesser trochanter to the femoral head and greater trochanter height. Variations unique to DDH were irregularities at different areas of the femoral head, but not at the lateral femoral head-neck junction where variation was strong in FAI. The FAI group also had unique variations in greater trochanter shape. DDH femoral heads were less spherical, as indicated by larger sphere-fitting errors (P < 0.001). Radiographically, the DDH group had significantly smaller α-angles (P < 0.001), larger head-neck offsets (P = 0.02) and larger NSAs (P < 0.001). Both the articular and extra-articular regions of the proximal femur have distinct shape features in DDH and cam FAI that can uniquely affect the biomechanics of each disorder. Accordingly, approaches to addressing each disorder should be unique.

Analysis of cam location characteristics in FAI syndrome patients from 3D MR images demonstrates sex-specific differences

Cam femoroacetabular impingement (FAI) syndrome is associated with hip osteoarthritis (OA) development. Hip shape features, derived from statistical shape modeling (SSM), are predictive for OA incidence, progression, and arthroplasty. Currently, no three-dimensional (3D) SSM studies have investigated whether there are cam shape differences between male and female patients, which may be of potential clinical relevance for FAI syndrome assessments. This study analyzed sex-specific cam location and shape in FAI syndrome patients from clinical magnetic resonance examinations (M:F 56:41, age: 16-63 years) using 3D focused shape modeling-based segmentation (CamMorph) and partial least squares regression to obtain shape features (latent variables [LVs]) of cam morphology. Two-way analysis of variance tests were used to assess cam LV data for sex and cam volume severity differences. There was no significant interaction between sex and cam volume severity for the LV data. A sex main effect was significant for LV 1 (cam size) and LV 2 (cam location) with medium to large effect sizes (p < 0.001, d > 0.75). Mean results revealed males presented with a superior-focused cam, whereas females presented with an anterior-focused cam. When stratified by cam volume, cam morphologies were located superiorly in male and anteriorly in female FAI syndrome patients with negligible, mild, or moderate cam volumes. Both male and female FAI syndrome patients with major cam volumes had a global cam distribution. In conclusion, sex-specific cam location differences are present in FAI syndrome patients with negligible, mild, and moderate cam volumes, whereas major cam volumes were globally distributed in both male and female patients.

Quantitative Skeletal Imaging and Image-Based Modeling in Pediatric Orthopaedics

PURPOSE OF REVIEW

Musculoskeletal imaging serves a critical role in clinical care and orthopaedic research. Image-based modeling is also gaining traction as a useful tool in understanding skeletal morphology and mechanics. However, there are fewer studies on advanced imaging and modeling in pediatric populations. The purpose of this review is to provide an overview of recent literature on skeletal imaging modalities and modeling techniques with a special emphasis on current and future uses in pediatric research and clinical care.

RECENT FINDINGS

While many principles of imaging and 3D modeling are relevant across the lifespan, there are special considerations for pediatric musculoskeletal imaging and fewer studies of 3D skeletal modeling in pediatric populations. Improved understanding of bone morphology and growth during childhood in healthy and pathologic patients may provide new insight into the pathophysiology of pediatric-onset skeletal diseases and the biomechanics of bone development. Clinical translation of 3D modeling tools developed in orthopaedic research is limited by the requirement for manual image segmentation and the resources needed for segmentation, modeling, and analysis. This paper highlights the current and future uses of common musculoskeletal imaging modalities and 3D modeling techniques in pediatric orthopaedic clinical care and research.

DeepSSM: A blueprint for image-to-shape deep learning models

Statistical shape modeling (SSM) characterizes anatomical variations in a population of shapes generated from medical images. Statistical analysis of shapes requires consistent shape representation across samples in shape cohort. Establishing this representation entails a processing pipeline that includes anatomy segmentation, image re-sampling, shape-based registration, and non-linear, iterative optimization. These shape representations are then used to extract low-dimensional shape descriptors that are anatomically relevant to facilitate subsequent statistical analyses in different applications. However, the current process of obtaining these shape descriptors from imaging data relies on human and computational resources, requiring domain expertise for segmenting anatomies of interest. Moreover, this same taxing pipeline needs to be repeated to infer shape descriptors for new image data using a pre-trained/existing shape model. Here, we propose DeepSSM, a deep learning-based framework for learning the functional mapping from images to low-dimensional shape descriptors and their associated shape representations, thereby inferring statistical representation of anatomy directly from 3D images. Once trained using an existing shape model, DeepSSM circumvents the heavy and manual pre-processing and segmentation required by classical models and significantly improves the computational time, making it a viable solution for fully end-to-end shape modeling applications. In addition, we introduce a model-based data-augmentation strategy to address data scarcity, a typical scenario in shape modeling applications. Finally, this paper presents and analyzes two different architectural variants of DeepSSM with different loss functions using three medical datasets and their downstream clinical application. Experiments showcase that DeepSSM performs comparably or better to the state-of-the-art SSM both quantitatively and on application-driven downstream tasks. Therefore, DeepSSM aims to provide a comprehensive blueprint for deep learning-based image-to-shape models.

MRI-based synthetic CT in the detection of knee osteoarthritis: Comparison with CT

Magnetic resonance Imaging is the gold standard for assessment of soft tissues; however, X-ray-based techniques are required for evaluating bone-related pathologies. This study evaluated the performance of synthetic computed tomography (sCT), a novel MRI-based bone visualization technique, compared with CT, for the scoring of knee osteoarthritis. sCT images were generated from the 3T T1-weighted gradient-echo MR images using a trained machine learning algorithm. Two readers scored the severity of osteoarthritis in tibiofemoral and patellofemoral joints according to OACT, which enables the evaluation of osteoarthritis, from its characteristics of joint space narrowing, osteophytes, cysts and sclerosis in CT (and sCT) images. Cohen's κ was used to assess the interreader agreement for each modality, and intermodality agreement of CT- and sCT-based scores for each reader. We also compared the confidence level of readers for grading CT and sCT images using confidence scores collected during grading. Inter-reader agreement for tibiofemoral and patellofemoral joints were almost-perfect for both modalities (κ = 0.83-0.88). The intermodality agreement of osteoarthritis scores between CT and sCT was substantial to almost-perfect for tibiofemoral (κ = 0.63 and 0.84 for the two readers) and patellofemoral joints (κ = 0.78 and 0.81 for the two readers). The analysis of diagnosis confidence scores showed comparable visual quality of the two modalities, where both are showing acceptable confidence levels for scoring OA. In conclusion, in this single-center study, sCT and CT were comparable for the scoring of knee OA.

Progressive DeepSSM: Training Methodology for Image-To-Shape Deep Models

Statistical shape modeling (SSM) is an enabling quantitative tool to study anatomical shapes in various medical applications. However, directly using 3D images in these applications still has a long way to go. Recent deep learning methods have paved the way for reducing the substantial preprocessing steps to construct SSMs directly from unsegmented images. Nevertheless, the performance of these models is not up to the mark. Inspired by multiscale/multiresolution learning, we propose a new training strategy, progressive DeepSSM, to train image-to-shape deep learning models. The training is performed in multiple scales, and each scale utilizes the output from the previous scale. This strategy enables the model to learn coarse shape features in the first scales and gradually learn detailed fine shape features in the later scales. We leverage shape priors via segmentation-guided multi-task learning and employ deep supervision loss to ensure learning at each scale. Experiments show the superiority of models trained by the proposed strategy from both quantitative and qualitative perspectives. This training methodology can be employed to improve the stability and accuracy of any deep learning method for inferring statistical representations of anatomies from medical images and can be adopted by existing deep learning methods to improve model accuracy and training stability.

Particle-Based Shape Modeling for Arbitrary Regions-of-Interest

Statistical Shape Modeling (SSM) is a quantitative method for analyzing morphological variations in anatomical structures. These analyses often necessitate building models on targeted anatomical regions of interest to focus on specific morphological features. We propose an extension to particle-based shape modeling (PSM), a widely used SSM framework, to allow shape modeling to arbitrary regions of interest. Existing methods to define regions of interest are computationally expensive and have topological limitations. To address these shortcomings, we use mesh fields to define free-form constraints, which allow for delimiting arbitrary regions of interest on shape surfaces. Furthermore, we add a quadratic penalty method to the model optimization to enable computationally efficient enforcement of any combination of cutting-plane and free-form constraints. We demonstrate the effectiveness of this method on a challenging synthetic dataset and two medical datasets.

ADASSM: Adversarial Data Augmentation in Statistical Shape Models From Images

Statistical shape models (SSM) have been well-established as an excellent tool for identifying variations in the morphology of anatomy across the underlying population. Shape models use consistent shape representation across all the samples in a given cohort, which helps to compare shapes and identify the variations that can detect pathologies and help in formulating treatment plans. In medical imaging, computing these shape representations from CT/MRI scans requires time-intensive preprocessing operations, including but not limited to anatomy segmentation annotations, registration, and texture denoising. Deep learning models have demonstrated exceptional capabilities in learning shape representations directly from volumetric images, giving rise to highly effective and efficient Image-to-SSM networks. Nevertheless, these models are data-hungry and due to the limited availability of medical data, deep learning models tend to overfit. Offline data augmentation techniques, that use kernel density estimation based (KDE) methods for generating shape-augmented samples, have successfully aided Image-to-SSM networks in achieving comparable accuracy to traditional SSM methods. However, these augmentation methods focus on shape augmentation, whereas deep learning models exhibit image-based texture bias resulting in sub-optimal models. This paper introduces a novel strategy for on-the-fly data augmentation for the Image-to-SSM framework by leveraging data-dependent noise generation or texture augmentation. The proposed framework is trained as an adversary to the Image-to-SSM network, augmenting diverse and challenging noisy samples. Our approach achieves improved accuracy by encouraging the model to focus on the underlying geometry rather than relying solely on pixel values.

Above knee socket prosthesis use changes proximal femur morphology

Patients with transfemoral amputation (TFA) are up to six times more likely to develop hip osteoarthritis (OA) in either or both the intact and residual limb, which is primarily attributed to habitually altered joint loading due to compensatory movement patterns. However, joint loading patterns differ between limbs, which confounds the understanding of loading-induced OA etiology across limbs. It remains unknown if altered loading due to amputation results in bony shape changes at the hip, which is a known etiological factor in the development of hip OA. Retrospective computed tomography images were collected of the residual limb for 31 patients with unilateral TFA (13F/18M; age: 51.7 ± 9.9 y/o; time since amputation: 13.7 ± 12.4 years) and proximal femur for a control group of 29 patients (13F/16M; age: 42.0 ± 12.27 years) and used to create 3D geometries of the proximal femur. Femoral 3D geometric variation was quantified using statistical shape modeling (SSM), a computational tool which placed 2048 corresponding particles on each geometry. Independent modes of variation were created using principal component analysis. 2D radiographic measures of the proximal femur, including common measures such as α-angle, head neck offset, and neck shaft angle, were quantified on digitally reconstructed radiographs (DRRs). SSM results were then compared to 2D measures using Pearson correlation coefficients (r). Two-sample t-tests were used to determine if there were significant differences between the TFA and control group means of 2D radiographic measurements (p < 0.05). Patients with TFA had greater femoral head asphericity within the SSM, which was moderately correlated to head-neck offset (r = -0.54) and α-angle (r = 0.63), as well as greater trochanteric torsion, which was strongly correlated to the novel radiographic measure of trochanteric torsion (r = -0.78), compared to controls. For 2D measures, the neck-shaft angle was smaller in the TFA group compared to the control group (p = 0.01) while greater trochanter height was larger in the TFA group compared to the control group (p = 0.04). These results indicate altered loading from transfemoral prosthesis use changes proximal femur bony morphology, including femoral head asphericity and greater trochanter changes. Greater trochanter morphologic changes, though not a known factor to OA, affect moment arm and line of action of the primary hip abductors, the major muscles which contribute to joint loading and hip stability. Thus, chronic altered loading of the amputated limb hip, whether under- or overloading, results in bony changes to the proximal femur which may contribute to the etiological progression and development of OA.

Biomechanical Insights Afforded by Shape Modeling in the Foot and Ankle

Advancements in volumetric imaging makes it possible to generate high-resolution three-dimensional reconstructions of bones in throughout the foot and ankle. The use of weightbearing computed tomography allows for the analysis of joint relationships in a consistent natural position that can be used for statistical shape modeling. Using statistical shape modeling, a population-based statistical model is created that can be used to compare mean bone shape morphology and identify anatomical modes of variation. A review is presented to highlight the current work using statistical shape modeling in the foot and ankle with a future view of the impact on clinical care.

Application of statistical shape modeling to the human hip joint: a scoping review

OBJECTIVE

The objective of this scoping review was to identify all examples of the application of statistical shape models to the human hip joint, with a focus on applications, population, methodology, and validation.

INTRODUCTION

Clinical radiographs are the most common imaging tool for management of hip conditions, but it is unclear whether radiographs can adequately diagnose or predict outcomes of 3D deformity. Statistical shape modeling, a method of describing the variation of a population of shapes using a small number of variables, has been identified as a useful tool to associate 2D images with 3D anatomy. This could allow clinicians and researchers to validate clinical radiographic measures of hip deformity, develop new ones, or predict 3D morphology directly from radiographs. In identifying all previous examples of statistical shape modeling applied to the human hip joint, this review determined the prevalence, strengths, and weaknesses, and identified gaps in the literature.

INCLUSION CRITERIA

Participants included any human population. The concept included development or application of statistical shape models based on discrete landmarks and principal component analysis. The context included sources that exclusively modeled the hip joint. Only peer-reviewed original research journal articles were eligible for inclusion.

METHODS

We searched MEDLINE, Embase, Cochrane CENTRAL, IEEE Xplore, Web of Science Core Collection, OCLC PapersFirst, OCLC Proceedings, Networked Digital Library of Theses and Dissertations, ProQuest Dissertations and Theses Global, and Google Scholar for sources published in English between 1992 and 2021. Two reviewers screened sources against the inclusion criteria independently and in duplicate. Data were extracted by 2 reviewers using a REDCap form designed to answer the review study questions, and are presented in narrative, tabular, and graphical form.

RESULTS

A total of 104 sources were considered eligible based on the inclusion criteria. From these, 122 unique statistical shape models of the human hip were identified based on 86 unique training populations. Models were most often applied as one-off research tools to describe shape in certain populations or to predict outcomes. The demographics of training populations were skewed toward older patients in high-income countries. A mean age between 60 and 79 years was reported in 29 training populations (34%), more than reported in all other age groups combined, and 73 training populations (85%) were reported or inferred to be from Europe and the Americas. Only 4 studies created models in a pediatric population, although 15 articles considered shape variation over time in some way. There were approximately equal numbers of 2D and 3D models. A variety of methods for labeling the training set was observed. Most articles presented some form of validation such as reporting a model's compactness (n = 71), but in-depth validation was rare.

CONCLUSIONS

Despite the high volume of literature concerning statistical shape models of the human hip, there remains a need for further research in key areas. We identified the lack of models in pediatric populations and low- and middle-income countries as a notable limitation to be addressed in future research.

Learning spatiotemporal statistical shape models for non-linear dynamic anatomies

Numerous clinical investigations require understanding changes in anatomical shape over time, such as in dynamic organ cycle characterization or longitudinal analyses (e.g., for disease progression). Spatiotemporal statistical shape modeling (SSM) allows for quantifying and evaluating dynamic shape variation with respect to a cohort or population of interest. Existing data-driven SSM approaches leverage information theory to capture population-level shape variations by learning correspondence-based (landmark) representations of shapes directly from data using entropy-based optimization schemes. These approaches assume sample independence and thus are unsuitable for sequential dynamic shape observations. Previous methods for adapting entropy-based SSM optimization schemes for the spatiotemporal case either utilize a cross-sectional design (ignoring within-subject correlation) or impose other limiting assumptions, such as the linearity of shape dynamics. Here, we present a principled approach to spatiotemporal SSM that relaxes these assumptions to correctly capture population-level shape variation over time. We propose to incorporate modeling the underlying time dependency into correspondence optimization via a regularized principal component polynomial regression. This approach is flexible enough to capture non-linear temporal dynamics while encoding population-specific spatial regularity. We demonstrate our method's efficacy on synthetic data and left atrium segmented from cardiac MRI scans. Our approach better captures the population modes of variation and a statistically significant time dependency than existing methods.

Statistical shape model of the talus bone morphology: A comparison between impinged and nonimpinged ankles

Diagnosis of ankle impingement is performed primarily by clinical examination, whereas medical imaging is used for severity staging and treatment guidance. The association of impingement symptoms with regional three-dimensional (3D) bone shape variaties visible in medical images has not been systematically explored, nor do we know the type and magnitude of this relation. In this cross-sectional case-control study, we hypothesized that 3D talus bone shape could be used to quantitatively formulate the discriminating shape variations between ankles with impingement from ankles without impingement, and we aimed to characterize and quantify these variations. We used statistical shape modeling (SSM) methods to determine the most prevalent modes of shape variations that discriminate between the impinged and nonimpinged ankles. Results of the compactness and parallel analysis test on the statistical shape model identify 8 prominent shape modes of variations (MoVs) representing approximately 78% of the total 3D variations in the population of shapes, among which two modes captured discriminating features between impinged and nonimpinged ankles (p value of 0.023 and 0.042). Visual inspection confirms that these two shape modes, capturing abnormalities in the anterior and posterior parts of talus, represent the two main bony risk factors in anterior and posterior ankle impingement. In conclusion, in this research using SSM we have identified shape MoVs that were found to correlate significantly with bony ankle impingement. We also illustrated potential guidance from SSMs for surgical planning.

Noninvasive shape-fitting method quantifies cam morphology in femoroacetabular impingement syndrome: Implications for diagnosis and surgical planning

There are considerable limitations associated with the standard 2D imaging currently used for the diagnosis and surgical planning of cam-type femoroacetabular impingement syndrome (FAIS). The aim of this study was to determine the accuracy of a new patient-specific shape-fitting method that quantifies cam morphology in 3D based solely on preoperative MRI imaging. Preoperative and postoperative 1.5T MRI scans were performed on n = 15 patients to generate 3D models of the proximal femur, in turn used to create the actual and the virtual cam. The actual cams were reconstructed by subtracting the postoperative from the preoperative 3D model and used as reference, while the virtual cams were generated by subtracting the preoperative 3D model from the virtual shape template produced with the shape-fitting method based solely on preoperative MRI scans. The accuracy of the shape-fitting method was tested on all patients by evaluating the agreement between the metrics of height, surface area, and volume that quantified virtual and actual cams. Accuracy of the shape-fitting method was demonstrated obtaining a 97.8% average level of agreement between these metrics. In conclusion, the shape-fitting technique is a noninvasive and patient-specific tool for the quantification and localization of cam morphology. Future studies will include the implementation of the technique within a clinically based software for diagnosis and surgical planning for cam-type FAIS.

From Images to Probabilistic Anatomical Shapes: A Deep Variational Bottleneck Approach

Statistical shape modeling (SSM) directly from 3D medical images is an underutilized tool for detecting pathology, diagnosing disease, and conducting population-level morphology analysis. Deep learning frameworks have increased the feasibility of adopting SSM in medical practice by reducing the expert-driven manual and computational overhead in traditional SSM workflows. However, translating such frameworks to clinical practice requires calibrated uncertainty measures as neural networks can produce over-confident predictions that cannot be trusted in sensitive clinical decision-making. Existing techniques for predicting shape with aleatoric (data-dependent) uncertainty utilize a principal component analysis (PCA) based shape representation computed in isolation of the model training. This constraint restricts the learning task to solely estimating pre-defined shape descriptors from 3D images and imposes a linear relationship between this shape representation and the output (i.e., shape) space. In this paper, we propose a principled framework based on the variational information bottleneck theory to relax these assumptions while predicting probabilistic shapes of anatomy directly from images without supervised encoding of shape descriptors. Here, the latent representation is learned in the context of the learning task, resulting in a more scalable, flexible model that better captures data non-linearity. Additionally, this model is self-regularized and generalizes better given limited training data. Our experiments demonstrate that the proposed method provides an accuracy improvement and better calibrated aleatoric uncertainty estimates than state-of-the-art methods.

Spatiotemporal Cardiac Statistical Shape Modeling: A Data-Driven Approach

Clinical investigations of anatomy's structural changes over time could greatly benefit from population-level quantification of shape, or spatiotemporal statistic shape modeling (SSM). Such a tool enables characterizing patient organ cycles or disease progression in relation to a cohort of interest. Constructing shape models requires establishing a quantitative shape representation (e.g., corresponding landmarks). Particle-based shape modeling (PSM) is a data-driven SSM approach that captures population-level shape variations by optimizing landmark placement. However, it assumes cross-sectional study designs and hence has limited statistical power in representing shape changes over time. Existing methods for modeling spatiotemporal or longitudinal shape changes require predefined shape atlases and pre-built shape models that are typically constructed cross-sectionally. This paper proposes a data-driven approach inspired by the PSM method to learn population-level spatiotemporal shape changes directly from shape data. We introduce a novel SSM optimization scheme that produces landmarks that are in correspondence both across the population (inter-subject) and across time-series (intra-subject). We apply the proposed method to 4D cardiac data from atrial-fibrillation patients and demonstrate its efficacy in representing the dynamic change of the left atrium. Furthermore, we show that our method outperforms an image-based approach for spatiotemporal SSM with respect to a generative time-series model, the Linear Dynamical System (LDS). LDS fit using a spatiotemporal shape model optimized via our approach provides better generalization and specificity, indicating it accurately captures the underlying time-dependency.

Prediction of femoral head coverage from articulated statistical shape models of patients with developmental dysplasia of the hip

Developmental dysplasia of the hip (DDH) is commonly described as reduced femoral head coverage due to anterolateral acetabular deficiency. Although reduced coverage is the defining trait of DDH, more subtle and localized anatomic features of the joint are also thought to contribute to symptom development and degeneration. These features are challenging to identify using conventional approaches. Herein, we assessed the morphology of the full femur and hemi-pelvis using an articulated statistical shape model (SSM). The model determined the morphological and pose-based variations associated with DDH in a population of Japanese females and established which of these variations predict coverage. Computed tomography (CT) images of 83 hips from 47 patients were segmented for input into a correspondence-based SSM. The dominant modes of variation in the model initially represented scale and pose. After removal of these factors through individual bone alignment, femoral version and neck-shaft angle, pelvic curvature, and acetabular version dominated the observed variation. Femoral head oblateness and prominence of the acetabular rim and various muscle attachment sites of the femur and hemi-pelvis were found to predict 3D CT-based coverage measurements (R2  = 0.5-0.7 for the full bones, R2  = 0.9 for the joint). Statement of Clinical Significance: Currently, clinical measurements of DDH only consider the morphology of the acetabulum. However, the results of this study demonstrated that variability in femoral head shape and several muscle attachment sites were predictive of femoral head coverage. These morphological differences may provide insight into improved clinical diagnosis and surgical planning based on functional adaptations of patients with DDH.

Three-Dimensional Quantification of Cam Resection Using MRI Bone Models: A Comparison of 2 Techniques

Background:

The current clinical standard for the evaluation of cam deformity in femoroacetabular impingement syndrome is based on radiographic measurements, which limit the ability to quantify the complex 3-dimensional (3D) morphology of the proximal femur.

Purpose:

To compare magnetic resonance imaging (MRI)–based metrics for the quantification of cam resection as derived using a best-fit sphere alpha angle (BFS-AA) method and using 3D preoperative-postoperative surface model subtraction (PP-SMS).

Study Design:

Descriptive laboratory study.

Methods:

Seven cadaveric hemipelvises underwent 1.5-T MRI before and after arthroscopic femoral osteochondroplasty, and 3D bone models of the proximal femur were reconstructed from the MRI scans. The alpha angles were measured radially along clockfaces using a BFS-AA method from the literature and plotted as continuous curves for the pre- and postoperative models. The difference between the areas under the curve for the pre- and postoperative models was then introduced in the current study as the BFS-AA–based metric to quantify the cam resection. The cam resection was also quantified using a 3D PP-SMS method, previously described in the literature using the metrics of surface area (FSA), volume (FV), and height (maximum [FH_max] and mean [FH_mean]). Bivariate correlation analyses were performed to compare the metrics quantifying the cam resection as derived from the BFS-AA and PP-SMS methods.

Results:

The mean ± standard deviation maximum pre- and postoperative alpha angle measurements were 59.73° ± 15.38° and 48.02° ± 13.14°, respectively. The mean for each metric quantifying the cam resection with the PP-SMS method was as follows: FSA, 540.9 ± 150.7 mm²; FV, 1019.2 ± 486.2 mm³; FH_max, 3.6 ± 1.0 mm; and FH_mean, 1.8 ± 0.5 mm. Bivariate correlations between the BFS-AA–based and PP-SMS–based metrics were strong: FSA (r = 0.817, P = .012), FV (r = 0.888, P = .004), FH_max (r = 0.786, P = .018), and FH_mean (r = 0.679, P = .047).

Conclusion:

Strong positive correlations were appreciated between the BFS-AA and PP-SMS methods quantifying the cam resection.

Clinical Relevance:

The utility of the BFS-AA technique is primarily during preoperative planning. The utility of the PP-SMS technique is in the postoperative setting when evaluating the adequacy of resection or in patients with persistent hip pain with suspected residual impingement. In combination, the techniques allow surgeons to develop a planned resection while providing a means to evaluate the depth of resection postoperatively.

Benchmarking off-the-shelf statistical shape modeling tools in clinical applications

Statistical shape modeling (SSM) is widely used in biology and medicine as a new generation of morphometric approaches for the quantitative analysis of anatomical shapes. Technological advancements of in vivo imaging have led to the development of open-source computational tools that automate the modeling of anatomical shapes and their population-level variability. However, little work has been done on the evaluation and validation of such tools in clinical applications that rely on morphometric quantifications(e.g., implant design and lesion screening). Here, we systematically assess the outcome of widely used, state-of-the-art SSM tools, namely ShapeWorks, Deformetrica, and SPHARM-PDM. We use both quantitative and qualitative metrics to evaluate shape models from different tools. We propose validation frameworks for anatomical landmark/measurement inference and lesion screening. We also present a lesion screening method to objectively characterize subtle abnormal shape changes with respect to learned population-level statistics of controls. Results demonstrate that SSM tools display different levels of consistencies, where ShapeWorks and Deformetrica models are more consistent compared to models from SPHARM-PDM due to the groupwise approach of estimating surface correspondences. Furthermore, ShapeWorks and Deformetrica shape models are found to capture clinically relevant population-level variability compared to SPHARM-PDM models.

Biomechanics of Cam Femoroacetabular Impingement: A Systematic Review

PURPOSE

To assess how biomechanical gait parameters (kinematics, kinetics, and muscle force estimations) differ between patients with cam-type femoroacetabular impingement (FAI) and healthy controls, through a systematic search.

METHODS

A systematic review of the literature from PubMed, Scopus, and Medline and EMBASE via OVID SP was undertaken from inception to April 2020 using PRISMA guidelines. Studies that described kinematics, kinetics, and/or estimated muscle forces in cam-type FAI were identified and reviewed.

RESULTS

The search strategy identified 404 articles for evaluation. Removal of duplicates and screening of titles and abstracts resulted in full-text review of 37 articles, with 12 meeting inclusion criteria. The 12 studies reported biomechanical data on a total of 173 cam-FAI (151 cam-specific, 22 mixed-type) patients and 177 healthy age-, sex-, and body mass index-matched controls. Patients with cam FAI had reduced hip sagittal plane range of motion (mean difference -3.00° [-4.10, -1.90], P < .001), reduced hip peak extension angles (mean difference -2.05° [-3.58, -0.53] , P = .008), reduced abduction angles in the terminal phase of stance, and reduced iliacus and psoas muscle force production in the terminal phase of stance compared to the control groups. Cam FAI cohorts walked at a slower speed compared with controls.

CONCLUSIONS

In conclusion, patients with cam-type FAI exhibit altered sagittal and frontal plane kinematics as well as altered muscle force production during level gait compared to controls. These findings will help guide future research into gait alterations in FAI and how such alterations may contribute to pathologic progression and furthermore, how such alterations can be modified for therapeutic benefit.

LEVEL OF EVIDENCE

Systematic review of Level III studies.

Three-Dimensional Measures of Bony Resection During Femoral Osteochondroplasty Are Related to Alpha Angle Measures: A Cadaveric Study

Purpose

Methods

Results

Conclusions

Clinical Relevance

Leveraging unsupervised image registration for discovery of landmark shape descriptor

In current biological and medical research, statistical shape modeling (SSM) provides an essential framework for the characterization of anatomy/morphology. Such analysis is often driven by the identification of a relatively small number of geometrically consistent features found across the samples of a population. These features can subsequently provide information about the population shape variation. Dense correspondence models can provide ease of computation and yield an interpretable low-dimensional shape descriptor when followed by dimensionality reduction. However, automatic methods for obtaining such correspondences usually require image segmentation followed by significant preprocessing, which is taxing in terms of both computation as well as human resources. In many cases, the segmentation and subsequent processing require manual guidance and anatomy specific domain expertise. This paper proposes a self-supervised deep learning approach for discovering landmarks from images that can directly be used as a shape descriptor for subsequent analysis. We use landmark-driven image registration as the primary task to force the neural network to discover landmarks that register the images well. We also propose a regularization term that allows for robust optimization of the neural network and ensures that the landmarks uniformly span the image domain. The proposed method circumvents segmentation and preprocessing and directly produces a usable shape descriptor using just 2D or 3D images. In addition, we also propose two variants on the training loss function that allows for prior shape information to be integrated into the model. We apply this framework on several 2D and 3D datasets to obtain their shape descriptors. We analyze these shape descriptors in their efficacy of capturing shape information by performing different shape-driven applications depending on the data ranging from shape clustering to severity prediction to outcome diagnosis.

Statistical shape analysis of the left atrial appendage predicts stroke in atrial fibrillation

The shape of the left atrium (LA) and left atrial appendage (LAA) have been shown to predict stroke in patients with atrial fibrillation (AF). Prior studies rely on qualitative assessment of shape, which limits reproducibility and clinical utility. Statistical shape analysis (SSA) allows for quantitative assessment of shape. We use this method to assess the shape of the LA and LAA and predict stroke in patients with AF. From a database of AF patients who had previously undergone MRI of the LA, we identified 43 patients with AF who subsequently had an ischemic stroke. We also identified a cohort of 201 controls with AF who did not have a stroke after the MRI. We performed SSA of the LA and LAA shape to quantify the shape of these structures. We found three of the candidate LAA shape parameters to be predictive of stroke, while none of the LA shape parameters predicted stroke. When the three predictive LAA shape parameters were added to a logistic regression model that included the CHA₂DS₂-VASc score, the area under the ROC curve increased from 0.640 to 0.778 (p = .003). The shape of the LA and LAA can be assessed quantitatively using SSA. LAA shape predicts stroke in AF patients, while LA shape does not. Additionally, LAA shape predicts stroke independent of CHA₂DS₂-VASc score. SSA for assessment of LAA shape may improve stroke risk stratification and clinical decision making for AF patients.

A novel approach for computerized quantitative image analysis of proximal femur bone shape deformities based on the hip joint symmetry

As a result of most of the bone disorders seen in hip joints, shape deformities occur in the structural form of the hip joint components. Image-based quantitative analysis and assessment of these deformities in bone shapes are very important for the evaluation, treatment, and prognosis of the various hip joint bone disorders. In this article, a novel approach for the image-based computerized quantitative analysis of proximal femur shape deformities is presented. In the proposed approach, shape deformities of the pathological proximal femurs were quantified over the contralateral healthy proximal femur shape structure of the same patient in 2D by taking the hip joint symmetry property of human anatomy into consideration. It is based on the idea that if the right and left proximal femurs in bilateral hip joints are highly symmetrical and also if one of the proximal femurs is healthy and the contralateral one is pathological, the non-overlapping bone shape regions can represent the deformities in pathological proximal femurs when both proximal femurs are registered to overlap each other. In the methodological process of the proposed study, a set of image preprocessing operations was primarily performed on the raw magnetic resonance imaging (MRI) data. Then, the segmented proximal femurs in bilateral hip joint images were automatically aligned with the Iterative Closest Point (ICP) rigid registration method. Following the registration, a set of image postprocessing operations was performed on the images of proximal femurs aligned. In the quantification phase, the bone shape deformities in pathological proximal femurs were quantified simply in terms of the mismatching area in 2D by measuring a shape variation index representing the total bone shape deformity ratio. To evaluate the proposed quantitative shape analysis approach, bilateral hip joints in a total of 13 coronal MRI sections of 13 patients with Legg-Calve-Perthes disease (LCPD) were used. Experimental studies have shown that the proposed approach has quite promising results in the quantitative representation of the pathological proximal femur shape deformities. Furthermore, consistent results have been observed for the Waldenström classification stages of the disease. The shape deformity ratios in pathological proximal femurs were quantified as 9.44% (±1.40), 18.38% (±6.30), 24.73% (±12.42), and 27.66% (±10.41), respectively for the Initial, Fragmentation, Reossification, and Remodelling stages of LCPD with the quantification error rates of 0.29% (±0.16), 0.58% (±0.71), 1.12% (±0.82), and 0.80% (±0.98). Additionally, a mean error rate of 0.65% (±0.68) was observed for the quantified shape deformity ratios of all samples.

Application of statistical shape modeling to the human hip joint: a scoping review protocol

OBJECTIVE

This review aims to identify all examples of the application of statistical shape models to the human hip joint, with a focus on methodology, validation, and applications.

INTRODUCTION

Abnormal hip joint morphology (eg, deformity secondary to Legg-Calvé-Perthes disease) is an important precursor to osteoarthritis. Clinical radiographs are often used to characterize deformity and provide indication for surgical correction, but it is unclear whether radiographs can adequately describe three-dimensional deformity. Statistical shape modeling, a method of describing a population of shapes using a small number of variables, has been identified as a potential tool that will allow clinicians and researchers to validate current and novel radiographic measurements of hip deformity. In identifying all previous examples of statistical shape modeling applied to the hip joint, this review will determine its prevalence, strengths, and weaknesses, and identify gaps in the literature.

INCLUSION CRITERIA

Peer-reviewed and gray literature focusing on the development and/or application of statistical shape models to the human hip joint will be included.

METHODS

Several relevant databases, including Ovid MEDLINE, Embase, and IEEE, will be searched for literature published from 1992, and for a title and abstract that can be searched in English. After removal of duplicates, two reviewers will independently screen papers by title and abstract, then screen the full text of selected or uncertain papers. The same reviewers will then independently chart data from the final selection. At each stage, disagreements will be resolved through discussion or third-party arbitration.

Self-Supervised Discovery of Anatomical Shape Landmarks

Statistical shape analysis is a very useful tool in a wide range of medical and biological applications. However, it typically relies on the ability to produce a relatively small number of features that can capture the relevant variability in a population. State-of-the-art methods for obtaining such anatomical features rely on either extensive preprocessing or segmentation and/or significant tuning and post-processing. These shortcomings limit the widespread use of shape statistics. We propose that effective shape representations should provide sufficient information to align/register images. Using this assumption we propose a self-supervised, neural network approach for automatically positioning and detecting landmarks in images that can be used for subsequent analysis. The network discovers the landmarks corresponding to anatomical shape features that promote good image registration in the context of a particular class of transformations. In addition, we also propose a regularization for the proposed network which allows for a uniform distribution of these discovered landmarks. In this paper, we present a complete framework, which only takes a set of input images and produces landmarks that are immediately usable for statistical shape analysis. We evaluate the performance on a phantom dataset as well as 2D and 3D images.

Combined Estimation of Shape and Pose for Statistical Analysis of Articulating Joints

Quantifying shape variations in articulated joints is of utmost interest to understand the underlying joint biomechanics and associated clinical symptoms. For joint comparisons and analysis, the relative positions of the bones can confound subsequent analysis. Clinicians design specific image acquisition protocols to neutralize the individual pose variations. However, recent studies have shown that even specific acquisition protocols fail to achieve consistent pose. The individual pose variations are largely attributed to the day-to-day functioning of the patient, such as gait during walk, as well as interactions between specific morphologies and joint alignment. This paper presents a novel two-step method to neutralize such patient-specific variations while simultaneously preserving the inherent relationship of the articulated joint. The resulting shape models are then used to discover clinically relevant shape variations in a population of hip joints.

Uncertain-DeepSSM: From Images to Probabilistic Shape Models

Statistical shape modeling (SSM) has recently taken advantage of advances in deep learning to alleviate the need for a time-consuming and expert-driven workflow of anatomy segmentation, shape registration, and the optimization of population-level shape representations. DeepSSM is an end-to-end deep learning approach that extracts statistical shape representation directly from unsegmented images with little manual overhead. It performs comparably with state-of-the-art shape modeling methods for estimating morphologies that are viable for subsequent downstream tasks. Nonetheless, DeepSSM produces an overconfident estimate of shape that cannot be blindly assumed to be accurate. Hence, conveying what DeepSSM does not know, via quantifying granular estimates of uncertainty, is critical for its direct clinical application as an on-demand diagnostic tool to determine how trustworthy the model output is. Here, we propose Uncertain-DeepSSM as a unified model that quantifies both, data-dependent aleatoric uncertainty by adapting the network to predict intrinsic input variance, and model-dependent epistemic uncertainty via a Monte Carlo dropout sampling to approximate a variational distribution over the network parameters. Experiments show an accuracy improvement over DeepSSM while maintaining the same benefits of being end-to-end with little pre-processing.

Thinking outside the glenohumeral box: Hierarchical shape variation of the periarticular anatomy of the scapula using statistical shape modeling

Variation in the shape of the glenoid and periarticular anatomy of the scapula has been associated with shoulder pathology. The goal of this study was to identify the modes of shape variation of periarticular scapular anatomy in relation to the glenoid in nonpathologic shoulders. Computed tomography scans of 31 cadaveric scapulae, verified to be free of pathology, were three-dimensionally reconstructed. Statistical shape modeling and principal component analysis identified the modes of shape variation across the population. Corresponding linear and angular measurements quantified the morphometric variance identified by the modes. Linear measures were normalized to the radius of the inferior glenoid to account for differences in the scaling of the bones. Five modes captured 89.7% of total shape variation of the glenoid and periarticular anatomy. Apart from size differences (mode 1: 33.0%), acromial anatomy accounted for the largest variation (mode 2: 32.0%). Further modes described variation in glenoid inclination (mode 3: 11.8%), coracoid orientation and size (mode 4: 9.0%), and variation in coracoacromial (CA) morphology (mode 5: 3.1%). The average scapula had a mean acromial tilt of 49 ± 7°, scapular spine angle of 61 ± 6°, the glenoid inclination of 84 ± 4°, coracoid deviation angle of 26 ± 4°, coracoid length of 3.7 ± 0.3 glenoid radii, and a CA base length of 5.6 ± 0.5 radii. In this study, the identified shape modes explain almost all of the variance in scapular anatomy. The acromion exhibited the highest variance of all periarticular anatomic structures of the scapula in relation to the glenoid, which may play a role in many shoulder pathologies.

The geometry of arteriovenous fistulas using endothelial nitric oxide synthase mouse models

BACKGROUND

Arteriovenous fistula (AVF) maturation failure is a significant clinical problem in the hemodialysis population. Geometric parameters of human AVFs were associated with AVF development, but causative studies are lacking. We characterized mouse AVF geometry using endothelial nitric oxide synthase (NOS3) mouse models.

METHODS

Carotid-jugular AVFs were created in NOS3 overexpression (OE), knockout (KO), and wild type (WT) mice. At 7 and 21 days postcreation, black-blood magnetic resonance images of AVFs were acquired and used to build three-dimensional reconstructions of AVF lumens. We used these reconstructions to calculate the lumen area, lumen centerline, and centerline-derived parameters: anastomosis angle, tortuosity, nonplanarity angle, and location of maximal distance between the feeding artery and AVF vein. Inter- and intrauser variabilities were also determined.

RESULTS

When all mice were considered, increased minimum AVF venous lumen area was accompanied by increased venous tortuosity and increased distance between the artery and vein, with both remaining in-plane with the anastomosis. At day 7, the lumen area of AVFs from all strains was 1.5- to 2.5-fold larger than native veins. Furthermore, at day 21, AVF lumen in NOS3 OE (4.04±1.43 mm2) was significantly larger than KO (2.74±1.34 mm2) (P<0.001) and WT (2.94±1.30 mm2) mice (p<0.001). At day 21, the location of maximal artery-vein distance on the vein was further away from the anastomosis in OE (4.49±0.66 mm) than KO (2.87±0.38 mm) (p=0.001). Other geometric parameters were not significantly different between mouse strains or time points. Inter- and intrauser variabilities were small, indicating the reliability and reproducibility of our protocol.

CONCLUSIONS

Our study presents a detailed characterization of mouse AVF geometry, and a robust protocol for future mechanistic studies to investigate the role of molecular pathways in AVF geometry. Identifying a geometry related to desired AVF remodeling can help inform surgery to enhance AVF maturation.

Statistical shape modeling of the pelvic floor to evaluate women with obstructed defecation symptoms

Obstructed defecation (OD) is common and may be related to compromised pelvic floor integrity. Magnetic resonance (MR) defecography and statistical shape modeling were used to define pelvic floor shape variations, hypothesizing that State (rest vs peak evacuation) and Group (control vs case) would significantly influence shape. 16 women underwent MR defecography (9 cases vs 7 controls). Midsagittal, 2D pelvic floors were segmented and aligned by corresponding points. Principal component scores were compared using a Two-Way Mixed MANOVA. Three modes described differences between State (p < 0.001) and Group (p = 0.023). The pelvic floor shape differed significantly between women with and without OD and during evacuation.

Computer assisted orthopaedic surgery: Past, present and future

Computer technology is ubiquitous and relied upon in virtually all professional activities including neurosurgery, which is why it is surprising that it is not the case for orthopaedic surgery with fewer than 5% of surgeons using available computer technology in their procedures. In this review, we explore the evolution and background of Computer Assisted Orthopaedic Surgery (CAOS), delving into the basic principles behind the technology and the changes in the discussion on the subject throughout the years and the impact these discussions had on the field. We found evidence that industry had an important role in driving the discussion at least in knee arthroplasty-a leading field of CAOS-with the ratio between patents and publications increased from approximately 1:10 in 2004 to almost 1:3 in 2014. The adoption of CAOS is largely restrained by economics and ergonomics with sceptics challenging the accuracy and precision of navigation during the early years of CAOS moving to patient functional improvements and long term survivorship. Nevertheless, the future of CAOS remains positive with the prospect of new technologies such as improvements in image-guided surgery, enhanced navigation systems, robotics and artificial intelligence.

Evaluation of Statistical Shape Modeling in Quantifying Femoral Morphologic Differences Between Symptomatic and Nonsymptomatic Hips in Patients with Unilateral Femoroacetabular Impingement Syndrome

Purpose

To determine whether statistical shape modeling can detect subtle morphologic differences in the shape of the proximal femur that correlate with clinical findings of unilateral femoroacetabular impingement syndrome.

Methods

Patients who had diagnoses of unilateral femoroacetabular impingement syndrome and who had existing computed tomography scans of their pelvises were included. Three-dimensional shape models in the form of triangle meshes were generated from the computed tomography images. Statistical shapes of cam-type and normal hips were compared to identify structural differences.

Results

The study included 33 hips in 17 subjects. Of the subjects, 7 (41.1%) were male, and 10 (58.9%) were female. The subjects ranged in age from 17-60 years of age (mean 36.3 ± 11.0 years old). The statistical shape modeling found mean shapes and modes after optimizing the groupwise correspondence. Symptomatic hips demonstrated 1 mm of thickening as compared to the femoral necks of asymptomatic hips, corresponding to cam lesions.

Conclusions

Symptomatic cam deformities were an average of 1 mm more prominent in the femoral neck region as compared to the asymptomatic hips when using statistical shape modeling. The present study provides a proof of the concept that statistical shape modeling can be used to examine and help define cam morphology and that subtle morphologic differences may account for developing femoroacetabular impingement syndrome.

Clinical Relevance

Using the methods presented in this study, it would be possible to define cam and pincer morphologies by creating statistical shape models, and this work could potentially lead to the development of a new classification system for femoroacetabular impingement syndrome lesions.

Deformable multisurface segmentation of the spine for orthopedic surgery planning and simulation

Purpose: We describe a shape-aware multisurface simplex deformable model for the segmentation of healthy as well as pathological lumbar spine in medical image data. Approach: This model provides an accurate and robust segmentation scheme for the identification of intervertebral disc pathologies to enable the minimally supervised planning and patient-specific simulation of spine surgery, in a manner that combines multisurface and shape statistics-based variants of the deformable simplex model. Statistical shape variation within the dataset has been captured by application of principal component analysis and incorporated during the segmentation process to refine results. In the case where shape statistics hinder detection of the pathological region, user assistance is allowed to disable the prior shape influence during deformation. Results: Results demonstrate validation against user-assisted expert segmentation, showing excellent boundary agreement and prevention of spatial overlap between neighboring surfaces. This section also plots the characteristics of the statistical shape model, such as compactness, generalizability and specificity, as a function of the number of modes used to represent the family of shapes. Final results demonstrate a proof-of-concept deformation application based on the open-source surgery simulation Simulation Open Framework Architecture toolkit. Conclusions: To summarize, we present a deformable multisurface model that embeds a shape statistics force, with applications to surgery planning and simulation.

Coracoacromial morphology: a contributor to recurrent traumatic anterior glenohumeral instability?

BACKGROUND

Although scapular morphology contributes to glenohumeral osteoarthritis and rotator cuff disease, its role in traumatic glenohumeral instability remains unknown. We hypothesized that coracoacromial and glenoid morphology would differ between healthy subjects and patients with recurrent traumatic anterior shoulder instability.

METHODS

Computed tomography scans of 31 cadaveric control scapulae and 54 scapulae of patients with recurrent traumatic anterior shoulder instability and Hill-Sachs lesions were 3-dimensionally reconstructed. Statistical shape modeling identified the modes of variation between the scapulae of both groups. Corresponding measurements quantified these modes in relation to the glenoid center (linear offset measures), defined by the best-fit circle of the inferior glenoid, or the glenoid center plane (angles), which bisects the glenoid longitudinally. Distances were normalized for glenoid size.

RESULTS

Compared with controls, the unstable coracoids were shorter (P = .004), with a more superior and medial offset of the tip (mean difference [MD], 7 and 3 mm, respectively; P < .001) and an origin closer to the 12-o'clock position (MD, 6°; P < .001). The unstable scapular spines originated closer to the 9-o'clock position (MD, 4°; P = .012), and the unstable acromions were more vertically oriented (MD, 6°; P < .001). The unstable glenoids had an increased height-width index (MD, 0.04; P = .021), had a flatter anterior-posterior radius of curvature (MD, 77 mm; P < .001), and were more anteriorly tilted (MD, 5°; P = .005).

CONCLUSIONS

Coracoacromial and glenoid anatomy differs between individuals with and without recurrent traumatic anterior shoulder instability. This pathologic anatomy is not addressed by current soft-tissue stabilization procedures and may contribute to instability recurrence.

Statistical shape modeling of femur shape variability in female patients with hip dysplasia

Although increasing evidence suggests that abnormal femur geometry in developmental dysplasia of the hip (DDH) may contribute to intra-articular damage and the development of hip osteoarthritis, a comprehensive 3D description of femoral abnormalities in DDH remains incomplete. Statistical shape modeling (SSM) was used to quantify three-dimensional (3D) geometric variation among femurs in female patients with DDH and control subjects. SSM correspondence points (n = 8,192) were placed on each femur using a gradient descent energy function to derive mean DDH and control femoral shapes and principal component analysis (PCA) was then used to describe shape variation. PCA results were associated with common 2D radiographic measures of femur shape using general linear models. For patients with DDH, the first eight principal components (modes) captured 90.9% of the cumulative variance accounted for (VAF). Notably, mode 2 captured 23.6% VAF and described variation in femoral version, the neck-shaft angle, and femoral neck length, while mode 3 captured 16.4% VAF and described variation in femoral version, femoral head size, and femoral offset. SSM captured complex geometric deformities in DDH, which may not be fully described by 2D measures of the acetabulum and proximal femur alone. By determining the primary shape variations among femurs in cases of DDH, SSM may further understanding of pathologies on the femoral side of dysplastic hips, in context with more commonly recognized acetabular deformities. © 2019 Orthopaedic Research Society. Published by Wiley Periodicals, Inc. J Orthop Res.

Novel model for the induction of postnatal murine hip deformity

Acetabular dysplasia is a common, multi-etiological, pre-osteoarthritic (OA) feature that can lead to pain and instability of the young adult hip. Despite the clinical significance of acetabular dysplasia, there is a paucity of small animal models to investigate structural and functional changes that mediate morphology of the dysplastic hip and drive the subsequent OA cascade. Utilizing a novel murine model developed in our laboratory, this study investigated the role of surgically induced unilateral instability of the postnatal hip on the initiation and progression of acetabular dysplasia and impingement up to 8-weeks post-injury. C57BL6 mice were used to develop titrated levels of hip instability (i.e., mild, moderate, and severe instabillity or femoral head resection) at weaning. Joint shape, acetabular coverage, histomorphology, and statistical shape modeling were used to assess quality of the hip following 8 weeks of destabilization. Acetabular coverage was reduced following severe, but not moderate, instability. Moderate instability induced lateralization of the femur without dislocation, whereas severe instability led to complete dislocation and pseudoacetabulae formation. Mild instability did not result in morphological changes to the hip. Removal of the femoral head led to reduced hip joint space volume. These data support the notion that hip instability, driven by mechanical loss-of-function of soft connective tissue, can induce morphometric changes in the growing mouse hip. This work developed a new mouse model to study hip health in the murine adolescent hip and is a useful tool for investigating the mechanical and structural adaptations to hip instability during growth. © 2018 Orthopaedic Research Society. Published by Wiley Periodicals, Inc. J Orthop Res.

Which Two-dimensional Radiographic Measurements of Cam Femoroacetabular Impingement Best Describe the Three-dimensional Shape of the Proximal Femur?

BACKGROUND

Many two-dimensional (2-D) radiographic views are used to help diagnose cam femoroacetabular impingement (FAI), but there is little consensus as to which view or combination of views is most effective at visualizing the magnitude and extent of the cam lesion (ie, severity). Previous studies have used a single image from a sequence of CT or MR images to serve as a reference standard with which to evaluate the ability of 2-D radiographic views and associated measurements to describe the severity of the cam lesion. However, single images from CT or MRI data may fail to capture the apex of the cam lesion. Thus, it may be more appropriate to use measurements of three-dimensional (3-D) surface reconstructions from CT or MRI data to serve as an anatomic reference standard when evaluating radiographic views and associated measurements used in the diagnosis of cam FAI.

QUESTIONS/PURPOSES

The purpose of this study was to use digitally reconstructed radiographs and 3-D statistical shape modeling to (1) determine the correlation between 2-D radiographic measurements of cam FAI and 3-D metrics of proximal femoral shape; and 2) identify the combination of radiographic measurements from plain film projections that were most effective at predicting the 3-D shape of the proximal femur.

METHODS

This study leveraged previously acquired CT images of the femur from a convenience sample of 37 patients (34 males; mean age, 27 years, range, 16-47 years; mean body mass index [BMI], 24.6 kg/m, range, 19.0-30.2 kg/m) diagnosed with cam FAI imaged between February 2005 and January 2016. Patients were diagnosed with cam FAI based on a culmination of clinical examinations, history of hip pain, and imaging findings. The control group consisted of 59 morphologically normal control participants (36 males; mean age, 29 years, range, 15-55 years; mean BMI, 24.4 kg/m, range, 16.3-38.6 kg/m) imaged between April 2008 and September 2014. Of these controls, 30 were cadaveric femurs and 29 were living participants. All controls were screened for evidence of femoral deformities using radiographs. In addition, living control participants had no history of hip pain or previous surgery to the hip or lower limbs. CT images were acquired for each participant and the surface of the proximal femur was segmented and reconstructed. Surfaces were input to our statistical shape modeling pipeline, which objectively calculated 3-D shape scores that described the overall shape of the entire proximal femur and of the region of the femur where the cam lesion is typically located. Digital reconstructions for eight plain film views (AP, Meyer lateral, 45° Dunn, modified 45° Dunn, frog-leg lateral, Espié frog-leg, 90° Dunn, and cross-table lateral) were generated from CT data. For each view, measurements of the α angle and head-neck offset were obtained by two researchers (intraobserver correlation coefficients of 0.80-0.94 for the α angle and 0.42-0.80 for the head-neck offset measurements). The relationships between radiographic measurements from each view and the 3-D shape scores (for the entire proximal femur and for the region specific to the cam lesion) were assessed with linear correlation. Additionally, partial least squares regression was used to determine which combination of views and measurements was the most effective at predicting 3-D shape scores.

RESULTS

Three-dimensional shape scores were most strongly correlated with α angle on the cross-table view when considering the entire proximal femur (r = -0.568; p < 0.001) and on the Meyer lateral view when considering the region of the cam lesion (r = -0.669; p < 0.001). Partial least squares regression demonstrated that measurements from the Meyer lateral and 90° Dunn radiographs produced the optimized regression model for predicting shape scores for the proximal femur (R = 0.405, root mean squared error of prediction [RMSEP] = 1.549) and the region of the cam lesion (R = 0.525, RMSEP = 1.150). Interestingly, views with larger differences in the α angle and head-neck offset between control and cam FAI groups did not have the strongest correlations with 3-D shape.

CONCLUSIONS

Considered together, radiographic measurements from the Meyer lateral and 90° Dunn views provided the most effective predictions of 3-D shape of the proximal femur and the region of the cam lesion as determined using shape modeling metrics.

CLINICAL RELEVANCE

Our results suggest that clinicians should consider using the Meyer lateral and 90° Dunn views to evaluate patients in whom cam FAI is suspected. However, the α angle and head-neck offset measurements from these and other plain film views could describe no more than half of the overall variation in the shape of the proximal femur and cam lesion. Thus, caution should be exercised when evaluating femoral head anatomy using the α angle and head-neck offset measurements from plain film radiographs. Given these findings, we believe there is merit in pursuing research that aims to develop the framework necessary to integrate statistical shape modeling into clinical evaluation, because this could aid in the diagnosis of cam FAI.

Early morphologic changes in trapeziometacarpal joint bones with osteoarthritis

OBJECTIVE

Characterising the morphological differences between healthy and early osteoarthritic (EOA) trapeziometacarpal (TMC) joints is important for understanding osteoarthritis onset, and early detection is important for treatment and disease management. This study has two aims: first, to characterise morphological differences between healthy and EOA TMC bones. The second aim was to determine the efficacy of using a statistical shape model (SSM) to detect early signs of osteoarthritis (OA).

METHODS

CT image data of TMC bones from 22 asymptomatic volunteers and 47 patients with EOA were obtained from an ongoing study and used to generate a SSM. A linear discriminant analysis (LDA) classifier was trained on the principal component (PC) weights to characterise features of each group. Multivariable statistical analysis was performed on the PC to investigate morphologic differences. Leave-one-out classification was performed to evaluate the classifiers performance.

RESULTS

We found that TMC bones of EOA subjects exhibited a lower aspect ratio (P = 0.042) compared with healthy subjects. The LDA classifier predicted that protrusions (up to 1.5 mm) at the volar beak of the first metacarpal were characteristic of EOA subjects. This was accompanied with widening of the articular surface, deepening of the articular surface, and protruding bone growths along the concave margin. These characteristics resulted in a leave-one-out classification accuracy of 73.9% (95% CI [61.9%, 83.8%]), sensitivity of 89.4%, specificity of 40.9%, and precision of 75.9%.

CONCLUSION

Our findings indicate that morphological degeneration is well underway in the EOA TMC joint, and shows promise for a clinical tool that can detect these features automatically.

DeepSSM: A Deep Learning Framework for Statistical Shape Modeling from Raw Images

Statistical shape modeling is an important tool to characterize variation in anatomical morphology. Typical shapes of interest are measured using 3D imaging and a subsequent pipeline of registration, segmentation, and some extraction of shape features or projections onto some lower-dimensional shape space, which facilitates subsequent statistical analysis. Many methods for constructing compact shape representations have been proposed, but are often impractical due to the sequence of image preprocessing operations, which involve significant parameter tuning, manual delineation, and/or quality control by the users. We propose DeepSSM: a deep learning approach to extract a low-dimensional shape representation directly from 3D images, requiring virtually no parameter tuning or user assistance. DeepSSM uses a convolutional neural network (CNN) that simultaneously localizes the biological structure of interest, establishes correspondences, and projects these points onto a low-dimensional shape representation in the form of PCA loadings within a point distribution model. To overcome the challenge of the limited availability of training images with dense correspondences, we present a novel data augmentation procedure that uses existing correspondences on a relatively small set of processed images with shape statistics to create plausible training samples with known shape parameters. In this way, we leverage the limited CT/MRI scans (40-50) into thousands of images needed to train a deep neural net. After the training, the CNN automatically produces accurate low-dimensional shape representations for unseen images. We validate DeepSSM for three different applications pertaining to modeling pediatric cranial CT for characterization of metopic craniosynostosis, femur CT scans identifying morphologic deformities of the hip due to femoroacetabular impingement, and left atrium MRI scans for atrial fibrillation recurrence prediction.

Left atrial shape predicts recurrence after atrial fibrillation catheter ablation

INTRODUCTION

Multiple markers left atrium (LA) remodeling, including LA shape, correlate with outcomes in atrial fibrillation (AF). Catheter ablation is an important treatment of AF, but better tools are needed to determine which patients will benefit. In this study, we use particle-based modeling to quantitatively assess LA shape, and determine to what degree it predicts AF recurrence after catheter ablation.

METHODS AND RESULTS

There were 254 patients enrolled in the DECAAF study who underwent cardiac magnetic resonance imaging of the LA prior to AF ablation and were followed for recurrence for up to 475 days. We performed particle-based shape modeling on each patient's LA shape. We selected shape parameters using the LASSO method and factor analysis, and then added them to a Cox regression model, which included multiple clinical parameters and LA fibrosis. We computed Harrell's C-statistic with and without shape in the model. We used the model to stratify patients into recurrence risk classes by both shape and shape and fibrosis combined. Three shape parameters were selected for inclusion. The C-statistic increased from 0.68 to 0.72 when shape was added to the model (P < 0.05). Visualized shapes showed that a more round LA shape with a shorter, more laterally rotated appendage was predictive of recurrence.

CONCLUSION

LA shape is an independent predictor of recurrence after AF ablation. When combined with LA fibrosis, shape analysis using PBM may improve patient selection for ablation.

3-dimensional metrics of proximal femoral shape deformities in Legg-Calvé-Perthes disease and slipped capital femoral epiphysis

Legg-Calvé-Perthes disease (LCPD) and slipped capital femoral epiphysis (SCFE) are two common pediatric hip disorders that affect the 3-dimensional shape and function of the proximal femur. This study applied the principles of continuum mechanics to statistical shape modeling (SSM) and determined 3-D metrics for the evaluation of shape deformations in normal growth, LCPD, and SCFE. CT scans were obtained from 32 patients with asymptomatic, LCPD, and SCFE hips ((0.5-0.9 mm)² in-plane resolution, 0.63 mm slice thickness). SSM was performed on segmented proximal femoral surfaces, and shape deformations were described by surface displacement, strain, and growth plate angle metrics. Asymptomatic normal femurs underwent coordinated, growth-associated surface displacements and anisotropic strains that were site-specific and highest at the greater trochanter. After size- and age-based shape adjustment, LCPD femurs exhibited large displacements and surface strains in the femoral head and neck, with associated changes in femoral head growth plate angles. Mild SCFE femurs had contracted femoral neck surfaces, and surface displacements in all regions tended to increase with severity of slip. The results of this paper provide new 3-D metrics for characterizing the shape and biomechanics of the proximal femur. Statement of Clinical Significance: Quantitative 3-D metrics of shape may be useful for understanding and monitoring disease progression, identifying target regions for shape modulation therapies, and objectively evaluating the success of such therapies. © 2017 Orthopaedic Research Society. Published by Wiley Periodicals, Inc. J Orthop Res 36:1526-1535, 2018.

Shape analysis of the basioccipital bone in Pax7-deficient mice

We compared the cranial base of newborn Pax7-deficient and wildtype mice using a computational shape modeling technology called particle-based modeling (PBM). We found systematic differences in the morphology of the basiooccipital bone, including a broadening of the basioccipital bone and an antero-inferior inflection of its posterior edge in the Pax7-deficient mice. We show that the Pax7 cell lineage contributes to the basioccipital bone and that the location of the Pax7 lineage correlates with the morphology most effected by Pax7 deficiency. Our results suggest that the Pax7-deficient mouse may be a suitable model for investigating the genetic control of the location and orientation of the foramen magnum, and changes in the breadth of the basioccipital.

The potential of statistical shape modelling for geometric morphometric analysis of human teeth in archaeological research

This paper introduces statistical shape modelling (SSM) for use in osteoarchaeology research. SSM is a full field, multi-material analytical technique, and is presented as a supplementary geometric morphometric (GM) tool. Lower mandibular canines from two archaeological populations and one modern population were sampled, digitised using micro-CT, aligned, registered to a baseline and statistically modelled using principal component analysis (PCA). Sample material properties were incorporated as a binary enamel/dentin parameter. Results were assessed qualitatively and quantitatively using anatomical landmarks. Finally, the technique’s application was demonstrated for inter-sample comparison through analysis of the principal component (PC) weights. It was found that SSM could provide high detail qualitative and quantitative insight with respect to archaeological inter- and intra-sample variability. This technique has value for archaeological, biomechanical and forensic applications including identification, finite element analysis (FEA) and reconstruction from partial datasets.

Three-dimensional assessment of impingement risk in geometrically parameterised hips compared with clinical measures

Abnormal bony morphology is a factor implicated in hip joint soft tissue damage and an increased lifetime risk of osteoarthritis. Standard 2-dimensional radiographic measurements for diagnosis of hip deformities, such as cam deformities on the femoral neck, do not capture the full joint geometry and are not indicative of symptomatic damage. In this study, a 3-dimensional geometric parameterisation system was developed to capture key variations in the femur and acetabulum of subjects with clinically diagnosed cam deformity. The parameterisation was performed for computed tomography scans of 20 patients (10 female and 10 male). Novel quantitative measures of cam deformity were taken and used to assess differences in morphological deformities between males and females. The parametric surfaces matched the more detailed, segmented hip bone geometry with low fitting error. The quantitative severity measures captured both the size and the position of cams and distinguished between cam and control femurs. The precision of the measures was sufficient to identify differences between subjects that could not be seen with the sole use of 2-dimensional imaging. In particular, cams were found to be more superiorly located in males than in females. As well as providing a means to distinguish between subjects more clearly, the new geometric hip parameterisation facilitates the flexible and rapid generation of a range of realistic hip geometries including cams. When combined with material property models, these stratified cam shapes can be used for further assessment of the effect of the geometric variation under impingement conditions.

Quantitative comparison of cortical bone thickness using correspondence-based shape modeling in patients with cam femoroacetabular impingement

The proximal femur is abnormally shaped in patients with cam-type femoroacetabular impingement (FAI). Impingement may elicit bone remodeling at the proximal femur, causing increases in cortical bone thickness. We used correspondence-based shape modeling to quantify and compare cortical thickness between cam patients and controls for the location of the cam lesion and the proximal femur. Computed tomography images were segmented for 45 controls and 28 cam-type FAI patients. The segmentations were input to a correspondence-based shape model to identify the region of the cam lesion. Median cortical thickness data over the region of the cam lesion and the proximal femur were compared between mixed-gender and gender-specific groups. Median [interquartile range] thickness was significantly greater in FAI patients than controls in the cam lesion (1.47 [0.64] vs. 1.13 [0.22] mm, respectively; p < 0.001) and proximal femur (1.28 [0.30] vs. 0.97 [0.22] mm, respectively; p < 0.001). Maximum thickness in the region of the cam lesion was more anterior and less lateral (p < 0.001) in FAI patients. Male FAI patients had increased thickness compared to male controls in the cam lesion (1.47 [0.72] vs. 1.10 [0.19] mm, respectively; p < 0.001) and proximal femur (1.25 [0.29] vs. 0.94 [0.17] mm, respectively; p < 0.001). Thickness was not significantly different between male and female controls.

CLINICAL SIGNIFICANCE

Studies of non-pathologic cadavers have provided guidelines regarding safe surgical resection depth for FAI patients. However, our results suggest impingement induces cortical thickening in cam patients, which may strengthen the proximal femur. Thus, these previously established guidelines may be too conservative. © 2016 Orthopaedic Research Society. Published by Wiley Periodicals, Inc. J Orthop Res 35:1743-1753, 2017.