This paper proposes an automatic method for vertebra localization, labeling, and segmentation in multi-slice Magnetic Resonance (MR) images. Prior work in this area on MR images mostly requires user interaction while our method is fully automatic. Cubic intensity-based features are extracted from image voxels. A deep learning approach is used for simultaneous localization and identification of vertebrae. The localized points are refined by local thresholding in the region of the detected vertebral column. Thereafter, a statistical multi-vertebrae model is initialized on the localized vertebrae. An iterative Expectation Maximization technique is used to register the vertebral body of the model to the image edges and obtain a segmentation of the lumbar vertebral bodies. The method is evaluated by applying to nine volumetric MR images of the spine. The results demonstrate 100% vertebra identification and a mean surface error of below 2.8 mm for 3D segmentation. Computation time is less than three minutes per high-resolution volumetric image.

Date Published: 18 March 2015
PDF: 7 pages
Proc. SPIE 9415, Medical Imaging 2015: Image-Guided Procedures, Robotic Interventions, and Modeling, 941514 (18 March 2015); doi: 10.1117/12.2081542

Published in SPIE Proceedings Vol. 9415:
Medical Imaging 2015: Image-Guided Procedures, Robotic Interventions, and Modeling
Robert J. Webster III; Ziv R. Yaniv, Editor(s)