Intervertebral Disc Shape Analysis with Geodesic Metric in Shape Space

Abstract

Shapes of anatomical structures extracted from medical imaging usually contain diagnostic and therapeutic cues in clinical applications. In this paper, we propose a framework on analyzing disc shapes based on a geodesic metric in an anatomical shape space. All disc shapes, containing both normal and abnormal ones, are formulated as elements in this space. The geodesic connecting these elements and other statistics are then numerically approximated. With these tools quantifying the intrinsic difference between disc shapes, the normal shapes from a dataset are unsupervisedly clustered and a statistical inference based on the learned Gaussian model is made. Experimental results show a reasonable accuracy of classifying normal and abnormal intervertebral discs.