Data-driven spine detection for multi-sequence MRI

Abstract

Epidemiology studies on vertebra’s shape and appearance require big databases of medical images and image processing methods, that are robust against deformation and noise. This work presents a solution of the first step: the vertebrae detection. We propose a method that automatically detects the central spinal curve with 3D data-driven methods in multi-sequence magnetic resonance images (MRI). Additionally, we use simple edge operations for vertebra border detection that can be used for a statistical evaluation with help of some fast user interaction. Our automatic vertebrae detection algorithm fits a polynomial curve through the spinal canal, that afterwards is shifted towards the vertebra centers. An edge operator gives a first approximation of the vertebra borders, that can be evaluated and corrected by some user interaction within 12 seconds. We show, that our algorithm automatically detects more than 90% of all spines correctly, and present a preliminary analysis of vertebrae sizes.