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Proceedings Paper

Deep learning for automatic localization, identification, and segmentation of vertebral bodies in volumetric MR images
Author(s): Amin Suzani; Abtin Rasoulian; Alexander Seitel; Sidney Fels; Robert N. Rohling; Purang Abolmaesumi
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Paper Abstract

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.

Paper Details

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
Show Author Affiliations
Amin Suzani, The Univ. of British Columbia (Canada)
Abtin Rasoulian, The Univ. of British Columbia (Canada)
Alexander Seitel, The Univ. of British Columbia (Canada)
Sidney Fels, The Univ. of British Columbia (Canada)
Robert N. Rohling, The Univ. of British Columbia (Canada)
Purang Abolmaesumi, The Univ. of British Columbia (Canada)


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

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