An Improved Shape-Constrained Deformable Model for Segmentation of Vertebrae from CT Lumbar Spine Images

Abstract

Accurate and robust segmentation of spinal and vertebral structures from medical images is a challenging task due to a relatively high degree of anatomical complexity and articulation of spinal structures, as well as due to image spatial resolution, inhomogeneity and low signal-to-noise ratio. In this paper, we describe an improved framework for vertebra segmentation that is based on an existing shape-constrained deformable model, which was modified with the aim to improve segmentation accuracy, and combined with a robust initialization that results from vertebra detection by interpolation-based optimization. The performance of the proposed segmentation framework was evaluated on \(10\) computed tomography (CT) images of the lumbar spine. The overall segmentation performance of \(0.43\,{\pm }\,0.14\) mm in terms of mean symmetric absolute surface distance and \(93.76\,{\pm }\,1.61\,\%\) in terms of Dice coefficient, computed against corresponding reference vertebra segmentations, indicates that the proposed framework can accurately segment vertebrae from CT images of the lumbar spine.