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The anterior pelvic plane (APP) defined by both anterior superior iliac spines (ASIS) and the pubic symphysis (PS), is used as reference for cup orientation during total hip arthroplasty (THA). However, acquiring the PS and the contralateral ASIS during the intervention with the patient in lateral decubitus position, can be challenging due to the medical devices and the patient abdominal apron. The goal of this study is to find more easily accessible anatomical landmarks, useful for the APP acquisition. Thus we propose to study the variability of the pelvis anatomy in order to identify which landmarks vary with the APP. We built a statistical shape model (SSM) of the pelvis and studied the variability of APP orientation when deforming the SSM along its variation modes. We computed the APP inclination for each deformation and modeled linear relations between the APP inclination and the deformation along the variation modes. We found that the variability in APP inclination is mainly due to 3 variation modes that deform the iliac crest (IC), the posterior superior and anterior inferior iliac spines (PSIS, AIIS). Acquiring those three anatomical landmarks (IC, PSIS and AIIS) with the ipsilateral ASIS, could be a solution to determine more easily the APP for THA in lateral decubitus.

International audience; The anterior pelvic plane (APP) defined by both iliac spines and the pubic symphysis, is essential in total hip arthroplasty (THA) for the orientation of the prosthetic cup. However, the APP is nowadays still difficult to determine in computer assisted orthopedic surgery (CAOS). We propose to use a statistical shape model (SSM) of the pelvis to estimate the APP from ipsilateral anatomical landmarks, more easily accessible during surgery in computer assisted THA with the patient in lateral decubitus position. A SSM of the pelvis has been built from 40 male pelvises. Various ipsilateral anatomical landmarks have been extracted from these data and used to deform the SSM. Fitting the SSM to several combinations of these landmarks, we were able to reconstruct the pelvis with an accuracy between 2.8mm and 4.4mm, and estimate the APP inclination with an angular error between 1.3° and 2.8°, depending on the landmarks fitted. Results are promising and show that the APP could be acquired during the intervention from ipsilateral landmarks only.

International audience; Prediction of complete and premorbid scapular anatomy is an important aspect of successful shoulder arthroplasty surgeries to treat glenohumeral arthritis and which remains elusive in the current literature. We proposed to build a statistical shape model (SSM) of the scapula and use it to build a framework to predict a complete scapular shape from virtually created scapular bone defects. The bone defects were synthetically created to imitate bone loss in the glenoid region and missing bony part in inferior and superior scapular regions. Sixty seven dry scapulae were used to build the SSM while ten external scapular shapes (not used in SSM building) were selected to map scapular shape variability using its anatomical classification. For each external scapula, four virtual bone defects were created in the superior, inferior and glenoid regions by manually removing a part of the original mesh. Using these defective shapes as prior knowledge, original shapes were reconstructed using scapula SSM and Gaussian process regression. Robustness of the scapula SSM was excellent (generality = 0.79 mm, specificity = 1.74 mm, first 15 principal modes of variations accounted for 95% variability). The validity and quality of the reconstruction of complete scapular bone were evaluated using two methods (1) mesh distances in terms of mean and RMS values and (2) four anatomical measures (three angles: glenoid version, glenoid inclination, and critical shoulder angle, and glenoid center location). The prediction error in the angle measures ranged from 1.0° to 2.2°. For mesh distances, highest mean and RMS error was 0.97 mm and 1.30 respectively. DICE similarity coefficient between the original and predicted shapes was excellent (≥ 0.81). This framework provided high reconstruction accuracy and can be effectively embedded in the pre-surgical planning of shoulder arthroplasty or in morphology-based shoulder biomechanics modeling pipelines.

International audience; Objective: To illustrate (a) whether a statistical shape model (SSM) augmented with anatomical landmark set(s) performs better fitting and provides improved clinical relevance over non-augmented SSM and (b) which anatomical landmark set provides the best augmentation strategy for predicting the glenoid region of the scapula.Methods: Scapula SSM was built using 27 dry bone CT scans and augmented with three anatomical landmark sets (16 landmarks each) resulting in three augmented SSMs (aSSMproposed, aSSMset1, aSSMset2). The non-augmented and three augmented SSMs were then used in a non-rigid registration (regression) algorithm to fit to six external scapular shapes. The prediction error by each type of SSM was evaluated in the glenoid region for the goodness of fit (mean error, root mean square error, Hausdorff distance and Dice similarity coefficient) and for four anatomical angles (critical shoulder angle, lateral acromion angle, glenoid inclination, glenopoar angle).Results: Inter- and intra-observer reliability for landmark selection was moderate to excellent (ICC>0.74). Prediction error was significantly lower for SSMnon-augmented for mean (0.9 mm) and root mean square (1.15 mm) distances. Dice coefficient was significantly higher (0.78) for aSSMproposed compared to all other SSM types. Prediction error for anatomical angles was lowest using the aSSMproposed for critical shoulder angle (3.4°), glenoid inclination (2.6°), and lateral acromion angle (3.2°).Conclusion and significance: The conventional SSM robustness criteria or better goodness of fit do not guarantee improved anatomical angle accuracy which may be crucial for certain clinical applications in pre-surgical planning. This study provides insights into how SSM augmented with region-specific anatomical landmarks can provide improved clinical relevance.

Subject-specific musculoskeletal models can predict accurate joint and muscle biomechanics thereby helping clinicians and surgeons. Current modeling strategies do not incorporate accurate subject-specific muscle parameters. This study reports a statistical shape model (SSM) based method to predict subject-specific muscle attachment regions on shoulder bones and illustrates the concurrent validity of the predictions. Augmented SSMs of scapula and humerus bones were built using bone meshes and five muscle attachment (origin/insertion) regions which play important role in the shoulder motion and function. Muscle attachments included Subscapularis, Supraspinatus, Infraspinatus, Teres Major and Teres Minor on both the bones. The regions were represented by subset of vertices on the bone meshes and were tracked using vertex identifiers. Subject-specific muscle attachment regions were predicted using external set of bones not used in building the SSMs. Validity of predictions was determined by visual inspection and also by using four similarity measures between predicted and manually segmented regions. Excellent concurrent validity was found indicating the higher accuracy of predictions. This method can be effectively employed in modeling pipelines or in automatic segmentation of medical images. Further validations are warranted on all the muscles of the shoulder complex.