Extreme leg motion analysis of professional ballet dancers via MRI segmentation of multiple leg postures

Professional ballet dancers are subject to constant extreme motion which is known to be at the origin of many articular disorders. To analyze their extreme motion, we exploit a unique magnetic resonance imaging (MRI) protocol, denoted as 'dual-posture' MRI, which scans the subject in both the normal (supine) and extreme (split) postures. However, due to inhomogeneous tissue intensities and image artifacts in these scans, coupled with unique acquisition protocol (split posture), segmentation of these scans is difficult. We present a novel algorithm that exploits the correlation between scans (bone shape invariance, appearance similarity) in automatically segmenting the dancer MRI images.

While validated segmentation algorithms are available for standard supine MRI, these algorithms cannot be applied to the split scan which exhibits a unique posture and strong inter-subject variations. In this study, the supine MRI is segmented with a deformable models method. The appearance and shape of the segmented supine models are then re-used to segment the split MRI of the same subject. Models are first registered to the split image using a novel constrained global optimization, before being refined with the deformable models technique.

Experiments with 10 dual-posture MRI datasets in the segmentation of left and right femur bones reported accurate and robust results (mean distance error: 1.39 ± 0.31 mm).

The use of segmented models from the supine posture to assist the split posture segmentation was found to be equally accurate and consistent to supine results. Our results suggest that dual-posture MRI can be efficiently and robustly segmented.