Lumbar Spine Segmentation Using a Statistical Multi-Vertebrae Anatomical Shape+Pose Model

Abstract

Segmentation of the spinal column from computed tomography (CT) images is a preprocessing step for a range of image-guided interventions. One intervention that would benefit from accurate segmentation is spinal needle injection. Previous spinal segmentation techniques have primarily focused on identification and separate segmentation of each vertebra. Recently, statistical multi-object shape models have been introduced to extract common statistical characteristics between several anatomies. These models can be used for segmentation purposes because they are robust, accurate, and computationally tractable. In this paper, we develop a statistical multi-vertebrae shape+pose model and propose a novel registration-based technique to segment the CT images of spine. The multi-vertebrae statistical model captures the variations in shape and pose simultaneously, which reduces the number of registration parameters. We validate our technique in terms of accuracy and robustness of multi-vertebrae segmentation of CT images acquired from lumbar vertebrae of 32 subjects. The mean error of the proposed technique is below 2 mm, which is sufficient for many spinal needle injection procedures, such as facet joint injections.