Interpolation-Based Detection of Lumbar Vertebrae in CT Spine Images

Detection of an object of interest can be represented as an optimization problem that can be solved by brute force or heuristic algorithms. However, the globally optimal solution may not represent the optimal detection result, which can be especially observed in the case of vertebra detection, where neighboring vertebrae are of similar appearance and shape. An adequate optimizer has to therefore consider not only the global optimum but also local optima that represent candidate locations for each vertebra. In this paper, we describe a novel framework for automated spine and vertebra detection in three-dimensional (3D) images of the lumbar spine, where we apply a novel optimization technique based on interpolation theory to detect the location of the whole spine in the 3D image and to detect the location of individual vertebrae within the spinal column. The performance of the proposed framework was evaluated on \(10\) computed tomography (CT) images of the lumbar spine. The resulting mean symmetric absolute surface distance of \(1.25\,{\pm }\,0.41\) mm and Dice coefficient of \(83.67\,{\pm }\,4.44\)%, computed from the final vertebra detection results against corresponding reference vertebra segmentations, indicate that the proposed framework can successfully detected vertebrae in CT images of the lumbar spine.