Spine segmentation from C-arm CT data sets: application to region-of-interest volumes for spinal interventions

C. Buerger; C. Lorenz; D. Babic; J. Hoppenbrouwers; R. Homan; R. Nachabe; J. M. Racadio; M. Grass

doi:10.1117/12.2249031

3 March 2017 Spine segmentation from C-arm CT data sets: application to region-of-interest volumes for spinal interventions

C. Buerger, C. Lorenz, D. Babic, J. Hoppenbrouwers, R. Homan, R. Nachabe, J. M. Racadio, M. Grass

Author Affiliations +

Proceedings Volume 10135, Medical Imaging 2017: Image-Guided Procedures, Robotic Interventions, and Modeling; 101351N (2017) https://doi.org/10.1117/12.2249031
Event: SPIE Medical Imaging, 2017, Orlando, Florida, United States

Abstract

Spinal fusion is a common procedure to stabilize the spinal column by fixating parts of the spine. In such procedures, metal screws are inserted through the patients back into a vertebra, and the screws of adjacent vertebrae are connected by metal rods to generate a fixed bridge. In these procedures, 3D image guidance for intervention planning and outcome control is required. Here, for anatomical guidance, an automated approach for vertebra segmentation from C-arm CT images of the spine is introduced and evaluated. As a prerequisite, 3D C-arm CT images are acquired covering the vertebrae of interest. An automatic model-based segmentation approach is applied to delineate the outline of the vertebrae of interest. The segmentation approach is based on 24 partial models of the cervical, thoracic and lumbar vertebrae which aggregate information about (i) the basic shape itself, (ii) trained features for image based adaptation, and (iii) potential shape variations. Since the volume data sets generated by the C-arm system are limited to a certain region of the spine the target vertebra and hence initial model position is assigned interactively. The approach was trained and tested on 21 human cadaver scans. A 3-fold cross validation to ground truth annotations yields overall mean segmentation errors of 0.5 mm for T1 to 1.1 mm for C6. The results are promising and show potential to support the clinician in pedicle screw path and rod planning to allow accurate and reproducible insertions.

Citation Download Citation

C. Buerger, C. Lorenz, D. Babic, J. Hoppenbrouwers, R. Homan, R. Nachabe, J. M. Racadio, and M. Grass "Spine segmentation from C-arm CT data sets: application to region-of-interest volumes for spinal interventions", Proc. SPIE 10135, Medical Imaging 2017: Image-Guided Procedures, Robotic Interventions, and Modeling, 101351N (3 March 2017); https://doi.org/10.1117/12.2249031

ACCESS THE FULL ARTICLE

INSTITUTIONAL
Select your institution to access the SPIE Digital Library.

SELECT YOUR INSTITUTION

PERSONAL
Sign in with your SPIE account to access your personal subscriptions or to use specific features such as save to my library, sign up for alerts, save searches, etc.

PERSONAL SIGN IN

No SPIE Account? Create one

PURCHASE THIS CONTENT

SUBSCRIBE TO DIGITAL LIBRARY

50 downloads per 1-year subscription

Members: $195

Non-members: $335 ADD TO CART

25 downloads per 1 - year subscription

Members: $145

Non-members: $250 ADD TO CART

PURCHASE SINGLE ARTICLE

Includes PDF, HTML & Video, when available