Predicting Tibia-Fibula Geometry and Density From Anatomical Landmarks Via Statistical Appearance Model: Influence of Errors on Finite Element-Calculated Bone Strain.

State-of-the-art participant-specific finite element models require advanced medical imaging to quantify bone geometry and density distribution; access to and cost of imaging is prohibitive to the use of this approach. Statistical appearance models may enable estimation of participants' geometry and density in the absence of medical imaging. The purpose of this study was to: (1) quantify errors associated with predicting tibia-fibula geometry and density distribution from skin-mounted landmarks using a statistical appearance model and (2) quantify how those errors propagate to finite element-calculated bone strain. Participant-informed models of the tibia and fibula were generated for thirty participants from height and sex and from twelve skin-mounted landmarks using a statistical appearance model. Participant-specific running loads, calculated using gait data and a musculoskeletal model, were applied to participant-informed and CT-based models to predict bone strain using the finite element method. Participant-informed meshes illustrated median geometry and density distribution errors of 4.39-5.17 mm and 0.116-0.142 g/cm3, respectively, resulting in large errors in strain distribution (median RMSE = 476-492 µÎµ), peak strain (limits of agreement =±27-34%), and strained volume (limits of agreement =±104-202%). These findings indicate that neither skin-mounted landmark nor height and sex-based predictions could adequately approximate CT-derived participant-specific geometry, density distribution, or finite element-predicted bone strain and therefore should not be used for analyses comparing between groups or individuals.

A scapular statistical shape model can reliably predict premorbid glenoid morphology in conditions of severe glenoid bone loss.

BACKGROUND: Knowledge of premorbid glenoid parameters at the time of shoulder arthroplasty, such as inclination, version, joint line position, height, and width, can assist with implant selection, implant positioning, metal augment sizing, and/or bone graft dimensions. The objective of this study was to validate a scapular statistical shape model (SSM) in predicting patient-specific glenoid morphology in scapulae with clinically relevant glenoid erosion patterns. METHODS: Computed tomography scans of 30 healthy scapulae were obtained and used as the control group. Each scapula was then virtually eroded to create 7 erosion patterns (Walch A1, A2, B2, B3, D, Favard E2, and E3). This resulted in 210 uniquely eroded glenoid models, forming the eroded glenoid group. A scapular SSM, created from a different database of 85 healthy scapulae, was then applied to each eroded scapula to predict the premorbid glenoid morphology. The premorbid glenoid inclination, version, height, width, radius of best-fit sphere, and glenoid joint line position were automatically calculated for each of the 210 eroded glenoids. The mean values for all outcome variables were compared across all erosion types between the healthy, eroded, and SSM-predicted groups using a 2-way repeated measures analysis of variance. RESULTS: The SSM was able to predict the mean premorbid glenoid parameters of the eroded glenoids with a mean absolute difference of 3° ± 2° for inclination, 3° ± 2° for version, 2 ± 1 mm for glenoid height, 2 ± 1 mm for glenoid width, 5 ± 4 mm for radius of best-fit sphere, and 1 ± 1 mm for glenoid joint line. The mean SSM-predicted values for inclination, version, height, width, and radius were not significantly different than the control group (P > .05). DISCUSSION: An SSM has been developed that can reliably predict premorbid glenoid morphology and glenoid indices in patients with common glenoid erosion patterns. This technology can serve as a useful template to visually represent the premorbid healthy glenoid in patients with severe glenoid bony erosions. Knowledge of the premorbid glenoid preoperatively can assist with implant selection, positioning, and sizing.

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 .

Detecting missing teeth on PMCT using statistical shape modeling.

The identification of teeth in 3D medical images can be a first step for victim identification from scant remains, for comparison of ante- and postmortem images or for other forensic investigations. We evaluate the performance of a tooth detection approach on mandibles with missing parts or pathologies based on statistical shape models. The proposed approach relies on a shape model that has been built from the full lower jaw, including the mandible and teeth. The model is fitted to the target, resulting in a reconstruction, in addition to a label map that indicates the presence or absence of teeth. We evaluate the accuracy of the proposed solution on a dataset consisting of 76 target mandibles, all extracted from CT images and exhibiting various cases of missing teeth or other cases, such as roots, implants, first dentition, and gap closure. We show an accuracy of approximately 90% on the front teeth (including incisors and canines in our study) that decreases for the molars due to high false-positive rates at the wisdom teeth level. Despite the drop in performance, the proposed approach can be used to obtain an estimate of the tooth count without wisdom teeth, tooth identification, reconstruction of the existing teeth to automate measurements taken as part of routine forensic procedures, or prediction of the missing teeth shape. In comparison to other approaches, our solution relies solely on shape information. This means it can be applied to cases obtained from either medical images or 3D scans because it does not depend on the imaging modality intensities. Another novelty is that the proposed solution avoids heuristics for the separation of teeth or for fitting individual tooth models. The solution is therefore not target-specific and can be directly applied to detect missing parts in other target organs using a shape model of the new target.

Developing a three-dimensional statistical shape model of normal dentition using an automated algorithm and normal samples.

OBJECTIVES: The statistical shape model (SSM) is a model of geometric properties of a set of shapes based on statistical shape analysis. The SSM develops an average model of several objects using an automated algorithm that excludes the operator's subjectivity. The aim of this study was to develop a three-dimensional (3D) SSM of normal dentition to provide virtual templates for efficient treatment. MATERIALS AND METHODS: Dental casts were obtained from participants with normal dentition. After acquiring the 3D models, the SSMs of the individual teeth and whole dental arch were generated by an iterative closest point (ICP)-based rigid registration and point correspondences, respectively. Then, the individual tooth SSM was aligned to the whole dental arch SSM using ICP-based registration to generate an average model of normal dentition. RESULTS: The generated 3D SSM showed specific morphological features of normal dentition similar to those previously reported. Moreover, on measuring the arch dimensions, all values in this study were similar to those previously reported using normal dentition. CONCLUSIONS: The 3D SSM of normal dentition may increase the diagnostic efficiency of orthodontic treatments by providing a visual objective. It can be also used as a 3D template in various fields of dentistry. CLINICAL RELEVANCE: Our SSM of normal dentition provides both quantitative and qualitative information on the 3D morphology of teeth and dental arches, which may provide valuable information on 3D virtual-setup, bracket fabrication, and aligner treatment.

Advanced quantitative 3D imaging improves the reliability of the classification of acetabular defects.

INTRODUCTION: Classifying complex acetabular defects in revision total hip arthroplasty (THA) by means of conventional radiographs comes with significant limitations. Statistical shape modelling allows the virtual reconstruction of the native pelvic morphology, hereby enabling an analytic acetabular defect assessment. Our objective was to evaluate the effect of advanced imaging augmented with analytic representations of the defect on (1) intra- and inter-rater reliability, and (2) up- or downscaling of classification scores when evaluating acetabular defects in patients undergoing revision THA. MATERIALS AND METHODS: The acetabular defects of 50 patients undergoing revision THA were evaluated by three independent, fellowship-trained orthopaedic surgeons. Defects were classified according to the acetabular defect classification (ADC) using four different imaging-based representations, namely, standard radiographs, CT imaging, a virtual three-dimensional (3D) model and a quantitative analytic representation of the defect based on a statistical shape model reconstruction. Intra- and inter-rater reliabilities were quantified using Fleiss' and Cohen's kappa scores, respectively. Up- and downscaling of classification scores were compared for each of the imaging-based representations and differences were tested. RESULTS: Overall inter-rater agreement across all imaging-based representations for the classification was fair (κ 0.29 95% CI 0.28-0.30). Inter-rater agreement was lowest for radiographs (κ 0.21 95% CI 0.19-0.22) and increased for other representations with agreement being highest when using analytic defect models (κ 0.46 95% CI 0.43-0.48). Overall intra-rater agreement was moderate (κ 0.51 95% CI 0.42-0.60). Intra-rater agreement was lowest for radiographs (κ 0.40 95% CI 0.23-0.57), and highest for ratings including analytic defect models (κ 0.64:95% CI 0.46-0.82). Virtual 3D models with quantitative analytic defect representations upscaled acetabular defect scores in comparison to standard radiographs. CONCLUSIONS: Using 3D CT imaging with statistical shape models doubles the intra- and inter-rater reliability and results in upscaling of acetabular defect classification when compared to standard radiographs. This method of evaluating defects will aid in planning surgical reconstruction and stimulate the development of new classification systems based on advanced imaging techniques.

[Three-dimensional reconstruction of femur based on Laplace operator and statistical shape model].

Reconstructing three-dimensional (3D) models from two-dimensional (2D) images is necessary for preoperative planning and the customization of joint prostheses. However, the traditional statistical modeling reconstruction shows a low accuracy due to limited 3D characteristics and information loss. In this study, we proposed a new method to reconstruct the 3D models of femoral images by combining a statistical shape model with Laplacian surface deformation, which greatly improved the accuracy of the reconstruction. In this method, a Laplace operator was introduced to represent the 3D model derived from the statistical shape model. By coordinate transformations in the Laplacian system, novel skeletal features were established and the model was accurately aligned with its 2D image. Finally, 50 femoral models were utilized to verify the effectiveness of this method. The results indicated that the precision of the method was improved by 16.8%-25.9% compared with the traditional statistical shape model reconstruction. Therefore, the method we proposed allows a more accurate 3D bone reconstruction, which facilitates the development of personalized prosthesis design, precise positioning, and quick biomechanical analysis.

Virtual reconstruction of orbital floor defects using a statistical shape model.

PURPOSE: The current standard in reconstructing defects of the orbital floor, by using the concept of mirroring, is time-consuming and ignores the natural asymmetry of the skull. By using a statistical shape model (SSM), the reconstruction can be automatized and improved in accuracy. The present study aims to show the possibilities of the virtual reconstruction of artificial defects of the orbital floor using an SSM and its potentials for clinical implementation. METHODS: Based on 131 unaffected CT scans of the midface, an SSM was created which contained the shape variability of the orbital floor. Nineteen midface CT scans, that were not included in the SSM, were manually segmented to establish ground truth (control group). Then artificial defects of larger and smaller sizes were created and reconstructed using SSM (Group I) and the gold standard of mirroring (Group II). Eventually, a comparison to the surface of the manual segmentation (control group) was performed. RESULTS: The proposed method of reconstruction using an SSM leads to more precise reconstruction results, compared with the conventional method of mirroring. Whereas mirroring led to the reconstruction errors of 0.7 mm for small defects and 0.73 mm for large defects, reconstruction using SSM led to deviations of 0.26 mm (small defect) and, respectively, 0.34 mm (large defect). CONCLUSIONS: The presented approach is an effective and accurate method for reconstructing the orbital floor. In connection with modern computer-aided design and manufacturing, individual patient-specific implants could be produced according to SSM-based reconstructions and could replace current methods using manual bending techniques. By acknowledging the natural asymmetry of the human skull, the SSM-based approach achieves higher accuracy in reconstructing injured orbits.

Camera Motion Agnostic Method for Estimating 3D Human Poses.

Although the performance of 3D human pose and shape estimation methods has improved considerably in recent years, existing approaches typically generate 3D poses defined in a camera or human-centered coordinate system. This makes it difficult to estimate a person's pure pose and motion in a world coordinate system for a video captured using a moving camera. To address this issue, this paper presents a camera motion agnostic approach for predicting 3D human pose and mesh defined in the world coordinate system. The core idea of the proposed approach is to estimate the difference between two adjacent global poses (i.e., global motion) that is invariant to selecting the coordinate system, instead of the global pose coupled to the camera motion. To this end, we propose a network based on bidirectional gated recurrent units (GRUs) that predicts the global motion sequence from the local pose sequence consisting of relative rotations of joints called global motion regressor (GMR). We use 3DPW and synthetic datasets, which are constructed in a moving-camera environment, for evaluation. We conduct extensive experiments and prove the effectiveness of the proposed method empirically.

Creating an anatomical wax-up in partially edentulous patients by means of a statistical shape model.

PURPOSE: Creating wax-ups of missing teeth for backward planning in implant surgery is a complex and time-consuming process. To facilitate implant-planning procedures, the automatic generation of a virtual wax-up would be useful. In the present study, the reconstruction of missing teeth in partially edentulous patients was performed automatically using newly developed software. The accuracy was investigated in order to test its clinical applicability. MATERIALS AND METHODS: This study presents a new method for creating an automatic virtual wax-up, which could serve as a basic tool in modern implant-planning procedures. First, a statistical shape model (SSM) based on 76 maxillary and mandibular arch scans from dentally healthy individuals was generated. Then, artificially generated tooth gaps were reconstructed. The accuracy of the workflow was evaluated on a separate testing sample of 10 individuals with artificially created tooth gaps given as a median deviation, in millimeters. Scans of three clinical cases with partial edentulism were equally reconstructed using the SSM and compared with the final prosthodontic work. RESULTS: The reconstruction of the artificial tooth gaps could be performed with the following median reconstruction accuracy: gap 21 with 0.15 mm; gap 27 with 0.20 mm; gap 34 with 0.22 mm: gap 36 with 0.22 mm; gaps 12 to 22 with 0.22 mm; gaps 34 to 36 with 0.22 mm. A scenario for an almost edentulous mandible with all teeth missing except teeth 33 and 43 could be reconstructed with a median reconstruction accuracy of 0.37 mm. The median tooth gap deviation of the SSM-based reconstruction in clinical cases differed from the final inserted prosthodontic teeth by 0.49 to 0.86 mm in median. CONCLUSION: A first feasibility of creating virtual wax-ups using an SSM could be shown. Artificially generated tooth gaps could be reconstructed close to the original with the proposed workflow. In the clinical cases, the SSM proposes an anatomical reconstruction, which does not yet consider prosthodontic aspects. To obtain clinical use, contact with antagonist teeth must be considered and more training data must be implemented. However, the presented method offers a fast and viable way for the approximate placement of missing crowns. This could be used in a digital planning workflow when implant position must be determined. (Int J Comput Dent 2022;25(4):349-0; doi: 10.3290/j.ijcd.b2599407).

[Rapid femur modeling method based on statistical shape model].

The geometric bone model of patients is an important basis for individualized biomechanical modeling and analysis, formulation of surgical planning, design of surgical guide plate, and customization of artificial joint. In this study, a rapid three-dimensional (3D) reconstruction method based on statistical shape model was proposed for femur. Combined with the patient plain X-ray film data, rapid 3D modeling of individualized patient femur geometry was realized. The average error of 3D reconstruction was 1.597-1.842 mm, and the root mean square error was 1.453-2.341 mm. The average errors of femoral head diameter, cervical shaft angle, offset distance and anteversion angle of the reconstructed model were 0.597 mm, 1.163°, 1.389 mm and 1.354°, respectively. Compared with traditional modeling methods, the new method could achieve rapid 3D reconstruction of femur more accurately in a shorter time. This paper provides a new technology for rapid 3D modeling of bone geometry, which is helpful to promote rapid biomechanical analysis for patients, and provides a new idea for the selection of orthopedic implants and the rapid research and development of customized implants.

Patient-specific statistical shape modeling for optimal spinal sagittal alignment in lumbar spinal fusion.

PURPOSE: The present study compared patients developing ASD after L4/5 spinal fusion with a control group using a patient-specific statistical shape model (SSM) to find alignment-differences between the groups. METHODS: This study included patients who had undergone spinal fusion at L4/5 and either remained asymptomatic (control group; n = 25, follow-up of > 4 years) or required revision surgery for epifusional ASD (n = 22). Landmarks on preoperative and postoperative lateral radiographs were annotated, and the optimal spinal sagittal alignment was calculated for each patient. The two-dimensional distance from the SSM-calculated optimum to the actual positions before and after fusion surgery was compared. RESULTS: Postoperatively, the additive mean distance from the SSM-calculated optimum was 86.8 mm in the ASD group and 67.7 mm in the control group (p = 0.119). Greater differences were observed between the groups with a larger distance to the ideal in patients with ASD at more cranial levels. Significant difference between the groups was seen postoperatively in the vertical distance of the operated segment L4. The patients with ASD (5.69 ± 3.0 mm) had a significant greater distance from the SSM as the control group (3.58 ± 3.5 mm, p = 0.034). CONCLUSION: Patients with ASD requiring revision after lumbar spinal fusion have greater differences from the optimal spinal sagittal alignment as an asymptomatic control group calculated by patient-specific statistical shape modeling. Further research might help to understand the value of SSM, in conjunction with already established indexes, for preoperative planning with the aim of reducing the risk of ASD. LEVEL OF EVIDENCE I: Diagnostic: individual cross-sectional studies with consistently applied reference standard and blinding.

Automated muscle elongation measurement during reverse shoulder arthroplasty planning.

BACKGROUND: Adequate deltoid and rotator cuff elongation in reverse shoulder arthroplasty is crucial to maximize postoperative functional outcomes and to avoid complications. Measurements of deltoid and rotator cuff elongation during preoperative planning can support surgeons in selecting a suitable implant design and position. Therefore, this study presented and evaluated a fully automated method for measuring deltoid and rotator cuff elongation. METHODS: Complete scapular and humeral models were extracted from computed tomography scans of 40 subjects. First, a statistical shape model of the complete humerus was created and evaluated to identify the muscle attachment points. Next, a muscle wrapping algorithm was developed to identify the muscle paths and to compute muscle lengths and elongations after reverse shoulder arthroplasty implantation. The accuracy of the muscle attachment points and the muscle elongation measurements was evaluated for the 40 subjects by use of both complete and artificially created partial humeral models. Additionally, the muscle elongation measurements were evaluated for a set of 50 arthritic shoulder joints. Finally, a sensitivity analysis was performed to evaluate the impact of implant positioning on deltoid and rotator cuff elongation. RESULTS: For the complete humeral models, all muscle attachment points were identified with a median error < 3.5 mm. For the partial humeral models, the errors on the deltoid attachment point largely increased. Furthermore, all muscle elongation measurements showed an error < 1 mm for 75% of the subjects for both the complete and partial humeral models. For the arthritic shoulder joints, the errors on the muscle elongation measurements were <2 mm for 75% of the subjects. Finally, the sensitivity analysis showed that muscle elongations were affected by implant positioning. DISCUSSION: This study presents an automated method for accurately measuring muscle elongations during preoperative planning of shoulder arthroplasty. The results show that the accuracy in measuring muscle elongations is higher than the accuracy in indicating the muscle attachment points. Hence, muscle elongation measurements are insensitive to the observed errors on the muscle attachment points. Related to this finding, muscle elongations can be accurately measured for both a complete humeral model and a partial humeral model. Because the presented method also showed accurate results for arthritic shoulder joints, it can be used during preoperative shoulder arthroplasty planning, in which typically only the proximal humerus is present in the scan and in which bone arthropathy can be present. As the muscle elongations are sensitive to implant positioning, surgeons can use the muscle elongation measurements to refine their surgical plan.

2D Statistical Lung Shape Analysis Using Chest Radiographs: Modelling and Segmentation.

Accurate information of the lung shape analysis and its anatomical variations is very noticeable in medical imaging. The normal variations of the lung shape can be interpreted as a normal lung. In contrast, abnormal variations of the lung shape can be a result of one of the pulmonary diseases. The goal of this study is twofold: (1) represent two lung shape models which are different at the reference points in registration process considering to show their impact on estimating the inter-patient 2D lung shape variations and (2) using the obtained models in lung field segmentation by utilizing active shape model (ASM) technique. The represented models which showed the inter-patient 2D lung shape variations in two different forms are fully compared and evaluated. The results show that the models along with standard principal component analysis (PCA) can be able to explain more than 95% of total variations in all cases using only first 7 principal component (PC) modes for both lungs. Both models are used in ASM-based segmentation technique for lung field segmentation. The segmentation results are evaluated using leave-one-out cross validation technique. According to the experimental results, the proposed method has average dice similarity coefficient of 97.1% and 96.1% for the right and the left lung, respectively. The results show that the proposed segmentation method is more stable and accurate than other model-based techniques to inter-patient lung field segmentation.

Morphological analysis of Gissane's angle utilising a statistical shape model of the calcaneus.

INTRODUCTION: Gissane's crucial angle (GA) facilitates to diagnose calcaneal fractures, and serves as an indicator of the quality of anatomical reduction after fixation. The study aimed to utilise statistical shape models (SSM) for analysing the complex 3D surface anatomy of the calcaneus represented by the simplified GA measurement on lateral radiographs. MATERIALS AND METHODS: SSMs were generated from CT scans of paired adult calcanei from 10 Japanese and 31 Thai specimens. GA measurements in 3D and 2D were obtained for the lateral, central and medial anatomy of the posterior facet and sinus tarsi. The correlation between calcaneal length and GA was analysed. Regression and principal component (PC) analyses were conducted for analysing morphological variability in calcaneal shape relating to GA. The bilateral symmetry of the obtained measurements was analysed. RESULTS: The mean GA (lateral) for the Japanese specimens was 105.1° ± 7.5 and 105.4° ± 8.5 for the Thai. The projected 2D angles of the central and medial measurements were larger (P < 0.00) than the 3D values. The medial projected 2D angles were larger (P ≤ 0.02) compared to the lateral. Despite the bilateral symmetry of GA and calcaneal length, their correlation displayed clear signs of asymmetry, which was confirmed by regression and PC analyses. CONCLUSIONS: Japanese and Thai specimens revealed lower GAs (both range and mean) compared to reported reference values of other ethnicities. As a reduced GA is generally indicative of a calcaneal fracture, our results are important to surgeons for their diagnostic assessment of Japanese and Thai patients. The results indicate that the GA measurement on a plain radiograph is a simplified representation of the lateral-to-central 3D calcaneal anatomy but significantly underestimates the angle measurement on the medial aspects of the respective surface areas.

Probabilistic Evaluation of 3D Surfaces Using Statistical Shape Models (SSM).

Inspecting a 3D object which shape has elastic manufacturing tolerances in order to find defects is a challenging and time-consuming task. This task usually involves humans, either in the specification stage followed by some automatic measurements, or in other points along the process. Even when a detailed inspection is performed, the measurements are limited to a few dimensions instead of a complete examination of the object. In this work, a probabilistic method to evaluate 3D surfaces is presented. This algorithm relies on a training stage to learn the shape of the object building a statistical shape model. Making use of this model, any inspected object can be evaluated obtaining a probability that the whole object or any of its dimensions are compatible with the model, thus allowing to easily find defective objects. Results in simulated and real environments are presented and compared to two different alternatives.

Posture normalisation of 3D body scans.

For product developers that design near-body products, virtual mannequins that represent realistic body shapes, are valuable tools. With statistical shape modelling, the variability of such body shapes can be described. Shape variation captured by statistical shape models (SSMs) is often polluted by posture variations, leading to less compact models. In this paper, we propose a framework that has low computational complexity to build a posture invariant SSM, by capturing and correcting the posture of an instance. The posture-normalised SSM is shown to be substantially more compact than the non-posture-normalised SSM. Practitioner summary: Statistical shape modelling is a technique to map out the variability of (body) shapes. This variability is often polluted by variations in posture. In this paper, we propose a framework to build a posture invariant statistical shape model. Abbreviations: SSM: statistical shape model; 1D: one-dimensional; 3D: three-dimensional; DHM: digital human model; LBS: linear blend skinning; PCA: princial component analysis; PC: principal component; TTR: thumb tip reach.

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.

Deformable torso phantoms of Chinese adults for personalized anatomy modelling.

In recent years, there has been increasing demand for personalized anatomy modelling for medical and industrial applications, such as ergonomics device development, clinical radiological exposure simulation, biomechanics analysis, and 3D animation character design. In this study, we constructed deformable torso phantoms that can be deformed to match the personal anatomy of Chinese male and female adults. The phantoms were created based on a training set of 79 trunk computed tomography (CT) images (41 males and 38 females) from normal Chinese subjects. Major torso organs were segmented from the CT images, and the statistical shape model (SSM) approach was used to learn the inter-subject anatomical variations. To match the personal anatomy, the phantoms were registered to individual body surface scans or medical images using the active shape model method. The constructed SSM demonstrated anatomical variations in body height, fat quantity, respiratory status, organ geometry, male muscle size, and female breast size. The masses of the deformed phantom organs were consistent with Chinese population organ mass ranges. To validate the performance of personal anatomy modelling, the phantoms were registered to the body surface scan and CT images. The registration accuracy measured from 22 test CT images showed a median Dice coefficient over 0.85, a median volume recovery coefficient (RCvlm ) between 0.85 and 1.1, and a median averaged surface distance (ASD) < 1.5 mm. We hope these phantoms can serve as computational tools for personalized anatomy modelling for the research community.

3X-Knee: A Novel Technology for 3D Preoperative Planning and Postoperative Evaluation of TKA Based on 2D X-Rays.

This chapter introduces a solution called "3X-knee" that can robustly derive 3D models of the lower extremity from 2D long leg standing X-ray radiographs for preoperative planning and postoperative treatment evaluation of total knee arthroplasty (TKA). There are three core components in 3X-knee technology: (1) a knee joint immobilization apparatus, (2) an X-ray image calibration phantom, and (3) a statistical shape model-based 2D-3D reconstruction algorithm. These three components are integrated in a systematic way in 3X-knee to derive 3D models of the complete lower extremity from 2D long leg standing X-ray radiographs acquired in weight-bearing position. More specifically, the knee joint immobilization apparatus will be used to rigidly fix the X-ray calibration phantom with respect to the underlying anatomy during the image acquisition. The calibration phantom then serves two purposes. For one side, the phantom will allow one to calibrate the projection parameters of any acquired X-ray image. For the other side, the phantom also allowsone to track positions of multiple X-ray images of the underlying anatomy without using any additional positional tracker, which is a prerequisite condition for the third component to compute patient-specific 3D models from 2D X-ray images and the associated statistical shape models. Validation studies conducted on both simulated X-ray images and on patients' X-ray data demonstrate the efficacy of the present solution.