Statistical Shape Research Articles

Poster 354: Tibiofemoral Bone Shape Features Are Associated with Re-Tear after Anterior Cruciate Ligament Reconstruction

Introduction: Knee bony morphology is linked to anterior cruciate ligament (ACL) injury, ACL graft failure, and osteoarthritis (OA) risk, and employing tools such as statistical shape modeling could identify individuals prone to these joint conditions. Re-tear after ACL reconstruction remains a common and devastating complication, and therefore, recognizing patients at high risk of graft failure before primary ACL reconstruction is crucial for potential intervention to reduce failure rates. Magnetic resonance imaging (MRI) is most commonly obtained in the setting of possible ACL injury and before ACL reconstruction surgery, yet currently available MRI-based bone shape assessment methods require high-resolution sequences or manual segmentation, limiting feasibility of large-scale analysis. The development of automated tools for bone shape extraction from clinical MRI could allow for three-dimensional statistical shape modeling across large datasets. We established an automated pipeline for bone segmentation from proton density (PD)-weighted MRI scans, aiming to extract knee bony morphology from preoperative clinical MRIs in patients with ACL injuries. We hypothesized that our segmentation pipeline could uncover specific tibiofemoral joint bone shape features associated with ACL re-tear after reconstruction. Objective: To identify bone shape features prior to initial primary ACL reconstruction in patients with eventual ACL graft re-tear compared to those with a known, intact ACL graft. Methods: Patients who retrospectively experienced a subsequent re-tear after reconstruction within three years of initial injury were compared to those without a re-tear within three years (20 without subsequent re-tear, 22 with subsequent re-tear for distal femur; 22 without subsequent re-tear, 19 with subsequent re-tear for proximal tibia). The control group had MRI scans at 3 years after reconstruction that confirmed an intact ACL graft. An automatic femur and tibia segmentation framework was developed by training two deep convolutional neural networks using clinical, two-dimensional PD-weighted knee MRI sequences (3.5 mm slice thickness) from patients with ACL tears. Using the segmentation pipeline, bone was automatically segmented from preoperative clinical PD-weighted knee MRI sequence (3 mm slice thickness), three-dimensional triangulated meshes were generated, scaled to a reference, and aligned and matched to an atlas using Iterative Closest Point algorithm. Modes of shape variance were extracted from principal component analysis (PCA). The effect of each mode is modeled by three-dimensional surfaces for each specific shape feature. Modes were considered statistically significant if the mean principal component score between groups was p<0.05 using Students T-test. Results: Statistical shape modeling of the femoral condyles using 15 PCA modes reveals significant differences (p < 0.05) in Modes 1 and 4 at the initial injury in patients who subsequently experienced ACL re-tear after reconstruction compared to those without re-tear. Femur Mode 1 is characterized by a narrower intercondylar notch width, a wider medial condylar width, and an increased ratio of femoral condylar offset in patients with ACL re-tear. Femur Mode 4 illustrates a more prominent trochlear sulcus and increased surface area along the lateral femoral condyle relative to the medial condyle in patients with ACL re-tear. The initial 15 PCA modes of the distal femur collectively account for 75.04% of the normalized accumulated total variance. Statistical shape modeling of the tibial plateau with 15 PCA modes reveals that Mode 7 is significantly different (p < 0.05) at the initial injury in patients who experienced subsequent ACL re-tear after reconstruction compared to those without re-tear. Tibia Mode 7 indicates diminished ACL facet prominence, a squared lateral tibial plateau, and a broader, flatter tibial spine in patients with subsequent ACL re-tear. The first 15 PCA modes of the proximal tibia collectively explain 73.60% of the normalized accumulated total variance. Conclusions: Using an automatic bone segmentation pipeline on preoperative clinical MRIs, we identify specific shape features in the tibiofemoral joint associated with ACL re-tear after reconstruction. Patients with subsequent ACL re-tear exhibit a variety of distinct three-dimensional morphology differences, including reduced intercondylar notch width, wider medial condylar width, increased lateral tibial plateau convexity, and broader, lateralized tibial spine, uncovered using statistical shape modeling. Statistical shape modeling identifies three-dimensional morphology patterns, thus capturing differences otherwise unaccounted for by traditional two-dimensional measurements. This approach offers predictive potential for patients at risk of ACL re-tear, improving treatment decisions for those with high-risk morphologic features.

Improving radiological quantification of levator hiatus features with measures informed by statistical shape modeling.

The measures that traditionally describe the levator hiatus (LH) are straightforward and reliable; however, they were not specifically designed to capture significant differences. Statistical shape modeling (SSM) was used to quantify LH shape variation across reproductive-age women and identify novel variables associated with LH size and shape. A retrospective study of pelvic MRIs from 19 nulliparous, 32 parous, and 12 pregnant women was performed. The LH was segmented in the plane of minimal LH dimensions. SSM was implemented. LH size was defined by the cross-sectional area, maximal transverse diameter, and anterior-posterior (A-P) diameter. Novel SSM-guided variables were defined by regions of greatest variation. Multivariate analysis of variance (MANOVA) evaluated group differences, and correlations determined relationships between size and shape variables. Overall shape ( ), SSM mode 2 (oval to -shape, ), mode 3 (rounder to broader anterior shape, ), and maximal transverse diameter ( ) significantly differed between groups. Novel anterior and posterior transverse diameters were identified at 14% and 79% of the A-P length. Anterior transverse diameter and maximal transverse diameter were strongly correlated ( , ), while posterior transverse diameter and maximal transverse diameter were weakly correlated ( , ). The traditional maximal transverse diameter generally corresponded with SSM findings but cannot describe anterior and posterior variation independently. The novel anterior and posterior transverse diameters represent both size and shape variation, can be easily calculated alongside traditional measures, and are more sensitive to subtle and local LH variation. Thus, they have a greater ability to serve as predictive and diagnostic parameters.

Facial Masculinization Surgery Using Polyetheretherketone Alloplasty: Statistical Shape Modeling-based Implant Designs.

The face is the initial feature used to judge gender in public spaces; it is also a source of significant gender dysphoria. Surgical techniques are available for non-cisgender male patients who desire a more masculine face by augmenting certain features to change the bony framework of the skull. Augmentation using virtually designed patient-specific polyetheretherketone implants has now become a more widely applied method in maxillofacial surgery. When designing implants for augmentation, a three-dimensional (3D) reference or template is very useful. Hence, a 3D statistical shape model was developed of a male skull shape from information from a population of 40 male patients containing the mean shape and principal components of shape variation. By overlaying the template and the patient's 3D skull model, this method identified the regions of gender dimorphism in this case to be the orbital ridge, zygomatic regions, and frontal bossing area. Based on the 3D template overlay, polyetheretherketone augmentation implants were virtually designed in close consultation with a patient to augment the aforementioned regions. The virtual statistical shape modeling template offered an objective reference, and the possibility to fully involve the patient in the treatment planning.

Three-dimensional bone morphology is a risk factor for medial postmeniscectomy syndrome: A retrospective cohort study.

The study aims to identify differences in tibiofemoral joint morphology between responders (R group, no pain) to arthroscopic partial medial meniscectomy (APMM) versus medial postmeniscectomy syndrome patients (MPMS group, recurrent pain at 2 years postmeniscectomy) in a clinically neutrally aligned patient population. The second aim was to build a morphology-based predictive algorithm for response to treatment (RTT) in APMM. Two patient groups were identified from a large multicentre database of meniscectomy patients at 2 years of follow-up: the R group included 120 patients with a KOOS pain score > 75, and the MPMS group included 120 patients with a KOOS pain score ≤ 75. Statistical shape models (SSMs) of distal femur, proximal tibia and tibiofemoral joint were used to compare knee morphology. Finally, a predictive model was developed to predict RTT, with the SSM-derived morphologic variables as predictors. No differences were found between the R and MPMS groups for patient age, sex, height, weight or cartilage status. Knees in the MPMS group were significantly smaller, had a wider femoral notch and a smaller medial femoral condyle. A morphology-based predictive model was able to predict MPMS at 2 years follow-up with a sensitivity of 74.9% (95% confidence interval [CI]: 74.4%-75.4%) and a specificity of 81.0% (95% CI: 80.6%-81.5%). A smaller tibiofemoral joint, a wider intercondylar notch and smaller medial femoral condyle were observed shape variations related to medial postmeniscectomy syndrome. These promising results are a first step towards a knee morphology-based clinical decision support tool for meniscus treatment. Case-control study. Level IIIb.

Considering What Counts: The Fall and Rise of Maternal Mortality Rates

Twentieth-century medical advances reduced the rate of maternal mortality by more than 99 percent. Yet recently we hear that there has been a substantial increase in the maternal mortality rate. This increase has been caused by contemporary researchers adopting much broader definitions for what counts as cases of maternal mortality. This example illustrations the importance of definitions in shaping statistics.

Left Atrial Roof Enlargement Is a Distinct Feature of Heart Failure With Preserved Ejection Fraction.

It remains unknown to what extent intrinsic atrial cardiomyopathy or left ventricular diastolic dysfunction drive atrial remodeling and functional failure in heart failure with preserved ejection fraction (HFpEF). Computational 3-dimensional (3D) models fitted to cardiovascular magnetic resonance allow state-of-the-art anatomic and functional assessment, and we hypothesized to identify a phenotype linked to HFpEF. Patients with exertional dyspnea and diastolic dysfunction on echocardiography (E/e', >8) were prospectively recruited and classified as HFpEF or noncardiac dyspnea based on right heart catheterization. All patients underwent rest and exercise-stress right heart catheterization and cardiovascular magnetic resonance. Computational 3D anatomic left atrial (LA) models were generated based on short-axis cine sequences. A fully automated pipeline was developed to segment cardiovascular magnetic resonance images and build 3D statistical models of LA shape and find the 3D patterns discriminant between HFpEF and noncardiac dyspnea. In addition, atrial morphology and function were quantified by conventional volumetric analyses and deformation imaging. A clinical follow-up was conducted after 24 months for the evaluation of cardiovascular hospitalization. Beyond atrial size, the 3D LA models revealed roof dilation as the main feature found in masked HFpEF (diagnosed during exercise-stress only) preceding a pattern shift to overall atrial size in overt HFpEF (diagnosed at rest). Characteristics of the 3D model were integrated into the LA HFpEF shape score, a biomarker to characterize the gradual remodeling between noncardiac dyspnea and HFpEF. The LA HFpEF shape score was able to discriminate HFpEF (n=34) to noncardiac dyspnea (n=34; area under the curve, 0.81) and was associated with a risk for atrial fibrillation occurrence (hazard ratio, 1.02 [95% CI, 1.01-1.04]; P=0.003), as well as cardiovascular hospitalization (hazard ratio, 1.02 [95% CI, 1.00-1.04]; P=0.043). LA roof dilation is an early remodeling pattern in masked HFpEF advancing to overall LA enlargement in overt HFpEF. These distinct features predict the occurrence of atrial fibrillation and cardiovascular hospitalization. URL: https://www.clinicaltrials.gov; Unique identifier: NCT03260621.

GRASS. II. Simulations of Potential Granulation Noise Mitigation Methods

We present an updated version of the GRanulation And Spectrum Simulator (GRASS) which now uses an expanded library of 22 solar lines to empirically model time-resolved spectral variations arising from solar granulation. We show that our synthesis model accurately reproduces disk-integrated solar line profiles and bisectors, and we quantify the intrinsic granulation-driven radial-velocity (RV) variability for each of the 22 lines studied. We show that summary statistics of bisector shape (e.g., bisector inverse slope) are strongly correlated with the measured anomalous, variability-driven RV at high pixel signal-to-noise ratio SNR and spectral resolution. Further, the strength of the correlations varies both line by line and with the summary statistic used. These correlations disappear for individual lines at the typical spectral resolutions and SNRs achieved by current extremely precise radial velocity spectrographs; so we use simulations from GRASS to demonstrate that they can, in principle, be recovered by selectively binning lines that are similarly affected by granulation. In the best-case scenario (high SNR and large number of binned lines), we find that a ≲30% reduction in the granulation-induced root mean square RV can be achieved, but that the achievable reduction in variability is most strongly limited by the spectral resolution of the observing instrument. Based on our simulations, we predict that existing ultra-high-resolution spectrographs, namely, ESPRESSO and PEPSI, should be able to resolve convective variability in other, bright stars.

A big data scheme for heart disease classification in map reduce using jellyfish search flow regime optimization enabled Spinalnet.

The disease related to the heart is serious and can lead to death. Precise heart disease prediction is imperative for the effective treatment of cardiac patients. This can be attained by machine learning (ML) techniques using healthcare data. Several models on the basis of ML predict and identify disease in the heart, but this model cannot manage a huge database because of the deficiency of the smart model. This paper provides an optimized SpinalNet with a MapReduce model to categorize heart disease. The objective is to design a big data approach for heart disease classification using the proposed Jellyfish Search Flow Regime Optimization (JSFRO)-based SpinalNet. The binary image conversion is applied on Electrocardiogram (ECG) images for converting the image to binary image. MapReduce model is adapted, in which the mappers execute feature extraction and the reducer performs heart disease classification. In the mapper phase, the features like statistical features, shape features and temporal features are extracted and in reducer, the SpinalNet with JSFRO is considered. Here, the training of SpinalNet is done with JSFRO, which is produced by the unification of Jellyfish Search Optimization (JSO) and Flow Regime Optimization (FRO). The JSFRO-based SpinalNet offered effectual performance with the finest accuracy of 90.8%, sensitivity of 95.2% and specificity of 93.6%.

Poster 200: Statistical Shape Modeling of an Experimental Induced FAI Cam Deformity in a Rabbit Model: A Platform to Study Mechanism of Hip Disease

Poster 200: Statistical Shape Modeling of an Experimental Induced FAI Cam Deformity in a Rabbit Model: A Platform to Study Mechanism of Hip Disease

Can near-to-mid Infrared Spectral Energy Distribution Quantitatively Trace Protoplanetary Disk Evolution?

Infrared (IR) spectral energy distribution (SED) is the major tracer of protoplanetary disks. It was recently proposed to use the near-to-mid IR (or K-24) SED slope α defined between 2 and 24μm as a potential quantitative tracer of disk age. We critically examine the viability of this idea and confront it with additional statistics of IR luminosities and SED shapes. We point out that, because the statistical properties of most of the complicated physical factors involved in disk evolution are still poorly understood in a quantitative sense, the only viable way is to assume them to be random so that an idealized “average disk” can be defined, which allows the α histogram to trace its age. We confirm that the statistics of the zeroth order (luminosity), first order (slope α), and second order characteristics (concavity) of the observed K-24 SEDs indeed carry useful information upon the evolutionary processes of the “average disk”. We also stress that intrinsic diversities in K-24 SED shapes and luminosities are always large at the level of individual stars so that the application of the evolutionary path of the “average disk” to individual stars must be done with care. The data of most curves in plots are provided on GitHub (Disk-age package https://github.com/starage/disk-age/).

A Label-Efficient Framework for Automated Sinonasal CT Segmentation in Image-Guided Surgery.

Segmentation, the partitioning of patient imaging into multiple, labeled segments, has several potential clinical benefits but when performed manually is tedious and resource intensive. Automated deep learning (DL)-based segmentation methods can streamline the process. The objective of this study was to evaluate a label-efficient DL pipeline that requires only a small number of annotated scans for semantic segmentation of sinonasal structures in CT scans. Retrospective cohort study. Academic institution. Forty CT scans were used in this study including 16 scans in which the nasal septum (NS), inferior turbinate (IT), maxillary sinus (MS), and optic nerve (ON) were manually annotated using an open-source software. A label-efficient DL framework was used to train jointly on a few manually labeled scans and the remaining unlabeled scans. Quantitative analysis was then performed to obtain the number of annotated scans needed to achieve submillimeter average surface distances (ASDs). Our findings reveal that merely four labeled scans are necessary to achieve median submillimeter ASDs for large sinonasal structures-NS (0.96 mm), IT (0.74 mm), and MS (0.43 mm), whereas eight scans are required for smaller structures-ON (0.80 mm). We have evaluated a label-efficient pipeline for segmentation of sinonasal structures. Empirical results demonstrate that automated DL methods can achieve submillimeter accuracy using a small number of labeled CT scans. Our pipeline has the potential to improve pre-operative planning workflows, robotic- and image-guidance navigation systems, computer-assisted diagnosis, and the construction of statistical shape models to quantify population variations. N/A.

The Corpus Callosum in Schizoaffective Disorder: A Shape Analysis Study

Objective: The corpus callosum is the largest white matter structure in the human brain that connects the cortical regions of both hemispheres. Diseases could lead to degenerative alterations in brain structures such as the corpus callosum (CC). Studies have associated CC abnormalities with Schizoaffective Disorder (SAD) symptoms. We predicted that there may be differences in the CC, an important structure connecting the two halves of the brain, in patients with SAD. The present study aims to analyze the CC of patients with statistical shape analysis (SSA) and compare the findings with healthy controls. Methods: Thirty-nine SAD patients and 39 healthy individuals (11 females and 28 males) of similar age that included subjects participated in the study. CC landmarks were marked on the mid-sagittal images of each participant. The mean ‘Procrustes’ point was determined, and shape deformations were analyzed with thin plate spline analysis. Results: Significant differences were observed between the shapes of CC in the two groups, and maximum CC deformation was observed in the posterior regions of SAD patients. There was no significant difference between the CC area of the SAD patients and the controls. Conclusion: In the present study, the maximum deformation was observed in the posterior region (isthmus and splenium) and the rostrum of the CC. The first CC region, the rostrum (+genu), connects prefrontal and premotor regions, which are associated with cognitive information (landmarks = 1, 7, 8, 9, 13, 15, and 12). The second area, the splenium, connects temporal and occipital cortical areas. These predominantly have auditory, peripheral, and central visual stimulation functions (landmarks = 5, 3, and 4). The current study could assist future studies on the etiology, diagnosis, and treatment of SAD.

Procrustes analysis of a shape of pediatric supracondylar humerus fractures

Shape of supracondylar fracture of the humeral of pediatric patients is analysed with Procrustes method. XR-images of fractures are considered both in anterio-posterior (AP) view and in a lateral (L) view. Applying Procrustes method for both views mean images are constructed and compared. Variability of shapes is quantified with a shape principal component analysis. Possibility of predictions of typical shape of humeral fracture and its variability using statistical shape analysis offers additional information on injury characteristics important in preoperative planning. Non-parametric tests (permutational and bootstrap) do not indicate statistical difference between Procrustes mean shapes in anterio-posterior and lateral projections. It is shown, however, that AP and L shapes of humeral fractures differ in their variability quantified by shape principal components.

Computational modeling of revision total hip arthroplasty involving acetabular defects: A systematic review.

Revision total hip arthroplasty (rTHA) involving acetabular defects is a complex procedure associated with lower rates of success than primary THA. Computational modeling has played a key role in surgical planning and prediction of postoperative outcomes following primary THA, but modeling applications in rTHA for acetabular defects remain poorly understood. This study aimed to systematically review the use of computational modeling in acetabular defect classification, implant selection and placement, implant design, and postoperative joint functional performance evaluation following rTHA involving acetabular defects. The databases of Web of Science, Scopus, Medline, Embase, Global Health and Central were searched. Fifty-three relevant articles met the inclusion criteria, and their quality were evaluated using a modified Downs and Black evaluation criteria framework. Manual image segmentation from computed tomography scans, which is time consuming, remains the primary method used to generate 3D models of hip bone; however, statistical shape models, once developed, can be used to estimate pre-defect anatomy rapidly. Finite element modeling, which has been used to estimate bone stresses and strains, and implant micromotion postoperatively, has played a key role in custom and off-the-shelf implant design, mitigation of stress shielding, and prediction of bone remodeling and implant stability. However, model validation is challenging and requires rigorous evaluation and comparison with respect to mid- to long-term clinical outcomes. Development of fast, accurate methods to model acetabular defects, including statistical shape models and artificial neural networks, may ultimately improve uptake of and expand applications in modeling and simulation of rTHA for the research setting and clinic.

Computer-aided design and fabrication of nasal prostheses: a semi-automated algorithm using statistical shape modeling

Computer-aided design and fabrication of nasal prostheses: a semi-automated algorithm using statistical shape modeling

An AI and Statistical Shape Analysis combined framework to extract features in complex cardiovascular structures

An AI and Statistical Shape Analysis combined framework to extract features in complex cardiovascular structures

Dataset of Finite Element Models of Normal and Deformed Thoracolumbar Spine

Adult spine deformity (ASD) is prevalent and leads to a sagittal misalignment in the vertebral column. Computational methods, including Finite Element (FE) Models, have emerged as valuable tools for investigating the causes and treatment of ASD through biomechanical simulations. However, the process of generating personalised FE models is often complex and time-consuming. To address this challenge, we present a dataset of FE models with diverse spine morphologies that statistically represent real geometries from a cohort of patients. These models are generated using EOS images, which are utilized to reconstruct 3D surface spine models. Subsequently, a Statistical Shape Model (SSM) is constructed, enabling the adaptation of a FE hexahedral mesh template for both the bone and soft tissues of the spine through mesh morphing. The SSM deformation fields facilitate the personalization of the mean hexahedral FE model based on sagittal balance measurements. Ultimately, this new hexahedral SSM tool offers a means to generate a virtual cohort of 16807 thoracolumbar FE spine models, which are openly shared in a public repository.

QUANTIFYING HIPPOCAMPAL SHAPE ASYMMETRY IN ALZHEIMER'S DISEASE USING OPTIMAL SHAPE CORRESPONDENCES.

Hippocampal atrophy in Alzheimer's disease (AD) is asymmetric and spatially inhomogeneous. While extensive work has been done on volume and shape analysis of atrophy of the hippocampus in AD, less attention has been given to hippocampal asymmetry specifically. Previous studies of hippocampal asymmetry are limited to global volume or shape measures, which don't localize shape asymmetry at the point level. In this paper, we propose to quantify localized shape asymmetry by optimizing point correspondences between left and right hippocampi within a subject, while simultaneously favoring a compact statistical shape model of the entire sample. To account for related variables that have an impact on AD and healthy subject differences, we build linear models with other confounding factors. Our results on the OASIS3 dataset demonstrate that compared to volumetric information, shape asymmetry reveals fine-grained, localized differences that inform us about the hippocampal regions of most significant shape asymmetry in AD patients.

Back to the Roots: Reconstructing Large and Complex Cranial Defects using an Image-based Statistical Shape Model

Designing implants for large and complex cranial defects is a challenging task, even for professional designers. Current efforts on automating the design process focused mainly on convolutional neural networks (CNN), which have produced state-of-the-art results on reconstructing synthetic defects. However, existing CNN-based methods have been difficult to translate to clinical practice in cranioplasty, as their performance on large and complex cranial defects remains unsatisfactory. In this paper, we present a statistical shape model (SSM) built directly on the segmentation masks of the skulls represented as binary voxel occupancy grids and evaluate it on several cranial implant design datasets. Results show that, while CNN-based approaches outperform the SSM on synthetic defects, they are inferior to SSM when it comes to large, complex and real-world defects. Experienced neurosurgeons evaluate the implants generated by the SSM to be feasible for clinical use after minor manual corrections. Datasets and the SSM model are publicly available at https://github.com/Jianningli/ssm.

HYMALAIA: a hybrid lagrangian model for intrinsic alignments

ABSTRACT The intrinsic alignment of galaxies is an important ingredient for modelling weak-lensing measurements, and a potentially valuable cosmological and astrophysical signal. In this paper, we present HYbrid Model Advected from LAgrangian space for IA (HYMALAIA): a new model to predict the intrinsic alignments of biased tracers. HYMALAIA is based on a perturbative expansion of the statistics of the Lagrangian shapes of objects, which is then advected to Eulerian space using the fully non-linear displacement field obtained from N-body simulations. We demonstrate that HYMALAIA is capable of consistently describing monopole and quadrupole of halo shape–shape and matter–shape correlators, and that, without increasing the number of free parameters, it does so more accurately than other perturbatively inspired models such as the non-linear alignment model and the tidal-alignment-tidal-torquing model.