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Evaluation of the bony anatomy of the glenohumeral joint is frequently required for surgical planning and subject-specific computational modeling and simulation. The three-dimensional geometry of bones is traditionally obtained by segmenting medical image datasets, but this can be time-consuming and may not be practical in the clinical setting. The aims of this study were twofold. Firstly, to develop and validate a statistical shape modeling approach to rapidly reconstruct the complete scapular and humeral geometries using discrete morphometric measurements that can be quickly and easily measured directly from CT, and secondly, to assess the effectiveness of statistical shape modeling in reconstruction of the entire humerus using just the landmarks in the immediate vicinity of the glenohumeral joint. The most representative shape prediction models presented in this study achieved complete scapular and humeral geometry prediction from seven or fewer morphometric measurements and yielded a mean surface root mean square (RMS) error under 2 mm. Reconstruction of the entire humerus was achieved using information of only proximal humerus bony landmarks and yielding mean surface RMS errors under 3 mm. The proposed statistical shape modeling facilitates rapid generation of 3D anatomical models of the shoulder, which may be useful in rapid development of personalized musculoskeletal models.

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

Adequate deltoid and rotator cuff lengthening in total shoulder arthroplasty (TSA) is crucial to maximize the postoperative functional outcome and to avoid complications. Hence surgeons and patients could benefit from including muscle length information in preoperative planning software.Although different methods have been introduced to automatically indicate patient-specific muscle attachment and wrapping points, the definition of a fast and accurate workflow is still a challenge, due to the large variability in bone shapes. Therefore, the goal of this study is to develop and evaluate the accuracy of a novel method to automatically estimate muscle elongation, based on a statistical shape modelling (SSM) approach.Firstly, humerus and scapula SSMs were used to automatically indicate the attachment points of the main shoulder muscles: subscapularis, supraspinatus, infraspinatus, teres minor and deltoid. Secondly, a wrapping algorithm was applied to identify the points where muscles wrap around bones or potential implants. Finally, the accuracy of the automatically indicated landmarks and its effect on the muscle elongation were evaluated by comparing the manually indicated landmarks with the landmarks identified through the SSM for a set of 40 healthy shoulder CT-scans.The low errors on elongation values suggest that the presented automated workflow is a promising tool for allowing surgeons to evaluate patient-specific muscle elongations during preoperative planning. Although the evaluation was limited to healthy joints, this method allows to easily process large datasets and to potentially find a correlation between muscle elongations and postoperative outcome.

This article outlines the methods and applications of threshold-based techniques to assess in vivo muscle and bone tissue distribution in normal and pathological conditions using computed tomography imaging. The approaches described here use medical imaging processing techniques and computational methods to study bone mechanical proprieties, analyze and quantify muscle morphology, visualize changes with 3-D models, develop subject-specific numerical profiles, and assess muscle and bone changes during clinical treatments.