Deformable Model-Based Segmentation of Intervertebral Discs from MR Spine Images by Using the SSC Descriptor

Gradual degeneration of intervertebral discs of the lumbar spine is one of the most common causes of low back pain. A fully automatic, accurate and robust segmentation of intervertebral discs in magnetic resonance (MR) images is therefore a prerequisite for the computer-aided diagnosis and quantification of intervertebral disc degeneration. In this paper, we propose an automated framework for intervertebral disc segmentation from MR spine images, in which intervertebral disc detection is performed by a landmark-based approach and segmentation by a deformable model-based approach using the self-similarity context (SSC) descriptor. The performance was evaluated on three publicly available databases of MR spine images that represent the training, on-line and on-site testing data for the intervertebral disc localization and segmentation challenge in conjunction with the 3rd MICCAIWorkshop& Challenge on Computational Methods and Clinical Applications for Spine Imaging MICCAI-CSI2015, yielding an overall mean Euclidean distance of 2.4, 1.7 and 2.2mm for intervertebral disc localization, and an overall mean Dice coefficient of 92.5, 91.5 and 92.0% for intervertebral disc segmentation for training, on-line and on-site testing data, respectively.