Inter-subject variability is inherently present in patient anatomy and is apparent in differences in shape, size and relative alignment of the bony structures. Understanding the variability in patient anatomy is useful for distinguishing between pathologies and to assist in surgical planning. With the aim of supporting the development of stratified orthopaedic interventions, this work introduces an Articulated Statistical Shape Model (ASSM) of the lower limb. The model captures inter-subject variability and allows reconstructing ‘virtual’ knee joints of the lower limb shape while considering pose. A training dataset consisting of 173 lower limbs from CT scans of 110 subjects (77 male, 33 female) was used to construct the ASSM of the lower limb. Each bone of the lower limb was segmented using ScanIP (Simpleware Ltd., UK), reconstructed into 3D surface meshes, and a SSM of each bone was created. A series of sizing and positioning procedures were carried out to ensure all the lower limbs were in full extension, had the same femoral length and that the femora were aligned with a coincident centre. All articulated lower limbs were represented as: (femur scale factor) × (full extension articulated lower limb + relative transformation of tibia, fibula and patella to femur). Articulated lower limbs were in full extension were used to construct a statistical shape model, representing the variance of lower limb morphology. Relative transformations of the tibia, fibula and patella versus the femur were used to form a statistical pose model. Principal component analysis (PCA) was used to extract the modes of changes in the model. The first 30 modes of the shape model covered 90% of the variance in shape and the first 10 modes of the pose model covered 90% of the pose variance. The first mode captures changes of the femoral CCD angle and the varus/valgus alignment of the knee. The second mode represents the changes in the ratio of femur to tibia length. The third mode reflects change of femoral shaft diameter and patella size. The first mode characterising pose captures the medial/lateral translation between femur and tibia. The second mode represents variation in knee flexion. The third mode reflects variation in tibio-femoral joint space. An articulated statistical modelling approach was developed to characterize inter-subject variability in lower limb morphology for a set of training specimens. This model can generate large sets of lower limbs to systematically study the effect of anatomical variability on joint replacement performance. Moreover, if a series of images of the lower limb during a dynamic activity are used as training data, this method can be applied to analyse variance of lower limb motion across a population

Adult Spine Deformity (ASD) is a degenerative condition of the adult spine leading to altered spine curvatures and mechanical balance. Computational approaches, like Finite Element (FE) Models have been proposed to explore the etiology or the treatment of ASD, through biomechanical simulations. However, while the personalization of the models is a cornerstone, personalized FE models are cumbersome to generate. To cover this need, we share a virtual cohort of 16807 thoracolumbar spine FE models with different spine morphologies, presented in an online user-interface platform (SpineView). To generate these models, EOS images are used, and 3D surface spine models are reconstructed. Then, a Statistical Shape Model (SSM), is built, to further adapt a FE structured mesh template for both the bone and the soft tissues of the spine, through mesh morphing. Eventually, the SSM deformation fields allow the personalization of the mean structured FE model, leading to generate FE meshes of thoracolumbar spines with different morphologies. Models can be selectively viewed and downloaded through SpineView, according to personalized user requests of specific morphologies characterized by the geometrical parameters: Pelvic Incidence; Pelvic Tilt; Sacral Slope; Lumbar Lordosis; Global Tilt; Cobb Angle; and GAP score. Data quality is assessed using visual aids, correlation analyses, heatmaps, network graphs, Anova and t-tests, and kernel density plots to compare spinopelvic parameter distributions and identify similarities and differences. Mesh quality and ranges of motion have been assessed to evaluate the quality of the FE models. This functional repository is unique to generate virtual patient cohorts in ASD. Acknowledgements: European Commission (MSCA-TN-ETN-2020-Disc4All-955735, ERC-2021-CoG-O-Health-101044828)

Summary. This work proposes a novel, automatic method to obtain an anatomical reconstruction for 3D segmented bones with large acetabular defects. The method works through the fitting of a Statistical Shape Model to the non-defect parts of the bone. Introduction. Patient-specific implants can be used to treat patients with large acetabular bone defects (IIa-c, IIIb, Paprosky 1994). These implants require a full 3D preoperative planning that includes segmentation of volumetric images (CT or MRI), extraction of the 3D shape, reconstruction of the bone defect into its anatomic (non-defect) state, design of an implant with a perfect fit and optimal placement of the screws. The anatomic reconstruction of the bone defect will play a key role in diagnosing the amount of bone loss and in the design of the implant. Previous reconstruction methods rely on a healthy contralateral (Gelaude 2007); however this is not always available (e.g. partial scan or implant present). Statistical shape models (SSM) of healthy bones can help to increase the accuracy and usability of the reconstruction and will decrease the manual labor and user dependency. Skadlubowicz (2009) illustrated the use of an SSM to reconstruct pelvic bones with tumor defects; however tumors generally affect a smaller region of the bone so that the reconstruction will be easier than in large acetabular bone defects. Also, the tumor reconstruction method uses 80 manually indicated landmarks, while the proposed method only uses 14. Patients & Methods. CT-scans from subjects with a healthy hemi-pelvis (15 male, 33 female, mean age: 69±20) were used to generate an SSM. The CT-scans were segmented using Mimics (Materialise NV, Belgium) to create a triangulated mesh. Preprocessing of the meshes ensured that the triangulation was smooth and uniform to help solve the corresponding point problem. An algorithm based on Redert (1999) was used to morph the template hemi-pelvis onto each dataset entity, creating a dataset with corresponding points. From this dataset the SSM was calculated using principal component analysis, so that the principal components serve as parameters for the mathematical model of the hemi-pelvis. To fit the SSM to a new defect hemi-pelvis, a matching algorithm was used. The algorithm varies the Principal Components independently optimizing the distance of the non-defect parts of the defect hemi-pelvis to the SSM sample. To validate the reconstruction method, 6 healthy bone meshes were used to generate a synthetic defect in the acetabular region. The original mesh was used as ‘golden standard’ to measure the reconstruction error. To illustrate the clinical use of the reconstruction method, one hemi-pelvis with a substantial defect was reconstructed. Results. The correspondence error for the morphing algorithm was 4.68±0.78 mm. The leave-one-out error for the SSM was 1.30±0.96 mm. The reconstruction error for the non-defect part was 1.44±1.13mm and for the reconstructed part 2.15±1.53mm. Discussion/Conclusion. The proposed method performs comparable to the contralateral method and the tumor reconstruction method, without the need of a healthy contralateral geometry. Consequently, the validation and the clinical illustration show that the proposed method is promising for automatic reconstruction of large acetabular defects

Pre-operative 3D glenoid planning improves component placement in terms of version, inclination, offset and orientation. Version and inclination measurements require the position of the inferior angle. As a consequence, current planning tools require a 3D model of the full scapula to accurately determine the glenoid parameters. Statistical shape models (SSMs) can be used to reconstruct the missing anatomy of bones. Therefore, the objective of this study is to develop and validate an SSM for the reconstruction of the inferior scapula, hereby reducing the irradiation exposure for patients. The training dataset for the statistical shape consisted of 110 CT images from patients without observable scapulae pathologies as judged by an experienced shoulder surgeon. 3D scapulae models were constructed from the segmented images. An open-source non-rigid B-spline-based registration algorithm was used to obtain point-to-point correspondences between the models. A statistical shape model was then constructed from the dataset using principal component analysis. Leave-one-out cross-validation was performed to evaluate the accuracy of the predicted glenoid parameters from virtual partial scans. Five types of virtual partial scans were created on each of the training set models, where an increasing amount of scapular body was removed to mimic a partial CT scan. The statistical shape model was reconstructed using the leave-one-out method, so the corresponding training set model is no longer incorporated in the shape model. Reconstruction was performed using a Monte Carlo Markov chain algorithm, random walk proposals included both shape and pose parameters, the closest fitting proposal was selected for the virtual reconstruction. Automatic 3D measurements were performed on both the training and reconstructed 3D models, including glenoid version, inclination, glenoid centre point position and glenoid offset. In terms of inclination and version we found a mean absolute difference between the complete model and the different virtual partial scan models of 0.5° (SD 0.4°). The maximum difference between models was 3° for inclination and 2° for version. For offset and centre point position the mean absolute difference was 0 mm with an absolute maximum of 1 mm. The magnitude of the mean and maximum differences for all anatomic measurements between the partial scan and complete models is smaller than the current surgical accuracy. Considering these findings, we believe a SSM based reconstruction technique can be used to accurately reconstruct the glenoid parameters from partial CT scans