This study reports a novel, computerised method that accurately predicts the pre-morbid proximal humeral anatomy even in challenging situations. This information can be used in the surgical planning and operative reconstruction of patients with severe degenerative osteoarthritis or with a fracture of the proximal humerus. Cite this article: 2017;99-B:927-33.

No abstract

International audienceAutomated bone segmentation is one of the most challenging problems in medical imaging. The increasingly demanded MR imaging suffers from low contrast and signal-to-noise ratio when it comes to bones. To increase the segmentation robustness, a prior model of the structure could guide the segmentation when explicit information is missing or weakly presented. Statistical Shape Models (SSMs) are efficient examples for such application where a set of dense correspondences between the training samples is to be established. The complexity of the anatomy of the scapula's bone is a real challenge at this level. We present an automated SSM construction approach with an adapted initialization to address the correspondences problem. Our approach is atlas-based where landmarks are matched on each sample using rigid and elastic registration. Our innovation stems from the derivation of a robust SSM based on Watershed segmentation which steers the elastic registration at some critical zones