The objective of this study was to compare simulated range of motion (ROM) for reverse total shoulder arthroplasty (rTSA) with and without adjustment for scapulothoracic orientation in a global reference system. We hypothesized that values for simulated ROM in preoperative planning software with and without adjustment for scapulothoracic orientation would be significantly different. A statistical shape model of the entire humerus and scapula was fitted into ten shoulder CT scans randomly selected from 162 patients who underwent rTSA. Six shoulder surgeons independently planned a rTSA in each model using prototype development software with the ability to adjust for scapulothoracic orientation, the starting position of the humerus, as well as kinematic planes in a global reference system simulating previously described posture types A, B, and C. ROM with and without posture adjustment was calculated and compared in all movement planes.Aims
Methods
Restoring the pre-morbid anatomy of the proximal humerus is a
goal of anatomical shoulder arthroplasty, but reliance is placed
on the surgeon’s experience and on anatomical estimations. The purpose
of this study was to present a novel method, ‘Statistical Shape
Modelling’, which accurately predicts the pre-morbid proximal humeral anatomy
and calculates the 3D geometric parameters needed to restore normal
anatomy in patients with severe degenerative osteoarthritis or a
fracture of the proximal humerus. From a database of 57 humeral CT scans 3D humeral reconstructions
were manually created. The reconstructions were used to construct
a statistical shape model (SSM), which was then tested on a second
set of 52 scans. For each humerus in the second set, 3D reconstructions
of four diaphyseal segments of varying lengths were created. These
reconstructions were chosen to mimic severe osteoarthritis, a fracture
of the surgical neck of the humerus and a proximal humeral fracture
with diaphyseal extension. The SSM was then applied to the diaphyseal
segments to see how well it predicted proximal morphology, using
the actual proximal humeral morphology for comparison.Aims
Materials and Methods
Automated MRI bone segmentation is one of the most challenging problems in medical imaging. To increase the segmentation robustness, a prior model of the structure could guide the segmentation. Statistical Shape Models (SSMs) are efficient examples for such application. We present an automated SSM construction approach of the The basic idea is to relate only corresponding parts of the shape under investigation. A sample from the samples set is chosen as a common reference (atlas), and the other samples are landmarked and registered to it so that the corresponding points can be identified. The registration has three levels: alignment, rigid and elastic transformations. To align two Afterwards, the samples are locally deformed toward the atlas using directly their landmarks (traditional approach). Unfortunately, landmarks-correspondences could be mismatched at some anatomically complex, “critical,” zones of the scapula. To overcome such a problem, we suggest to 3D-segment these “critical” zones using a 3D Watershed-based method. Watershed is based on a physical concept of immersion, where it is achieved in a similar way to water filling geographic basins. We believe that this is a natural way to segment the surface of the scapula since it has two large “basins”: the Once we have the zones, surface-to-surface correspondence is defined and the landmarks' point-to-point correspondences are obtained within each zone pair separately. The elastic registration is then applied on the whole surface via a multi-resolution B-Spline algorithm. The atlas is built through an iterative procedure to eliminate the bias to the initial choice and the correspondences are identified by a reverse registration. Finally, the statistical model can be constructed by performing Principle Component Analysis (PCA).INTRODUCTION
METHODS
