Automated 3D Lumbar Intervertebral Disc Segmentation from MRI Data Sets

Dong, Xiao; Zheng, Guoyan (2016). Automated 3D Lumbar Intervertebral Disc Segmentation from MRI Data Sets. In: Zheng, Guoyan; Li, Shuo (eds.) Computational Radiology for Orthopaedic Interventions. Lecture Notes in Computational Vision and Biomechanics: Vol. 23 (pp. 25-40). Cham: Springer 10.1007/978-3-319-23482-3_2

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This paper proposed an automated three-dimensional (3D) lumbar intervertebral disc (IVD) segmentation strategy from Magnetic Resonance Imaging (MRI) data. Starting from two user supplied landmarks, the geometrical parameters of all lumbar vertebral bodies and intervertebral discs are automatically extracted from a mid-sagittal slice using a graphical model based template matching approach. Based on the estimated two-dimensional (2D) geometrical parameters, a 3D variable-radius soft tube model of the lumbar spine column is built by model fitting to the 3D data volume. Taking the geometrical information from the 3D lumbar spine column as constraints and segmentation initialization, the disc segmentation is achieved by a multi-kernel diffeomorphic registration between a 3D template of the disc and the observed MRI data. Experiments on 15 patient data sets showed the robustness and the accuracy of the proposed algorithm.

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