Automatic labeling and segmentation of vertebrae in CT images

Abtin Rasoulian; Robert N. Rohling; Purang Abolmaesumi

doi:10.1117/12.2043256

12 March 2014 Automatic labeling and segmentation of vertebrae in CT images

Abtin Rasoulian, Robert N. Rohling, Purang Abolmaesumi

Author Affiliations +

Proceedings Volume 9036, Medical Imaging 2014: Image-Guided Procedures, Robotic Interventions, and Modeling; 903623 (2014) https://doi.org/10.1117/12.2043256
Event: SPIE Medical Imaging, 2014, San Diego, California, United States

Abstract

Labeling and segmentation of the spinal column from CT images is a pre-processing step for a range of image- guided interventions. State-of-the art techniques have focused either on image feature extraction or template matching for labeling of the vertebrae followed by segmentation of each vertebra. Recently, statistical multi- object models have been introduced to extract common statistical characteristics among several anatomies. In particular, we have created models for segmentation of the lumbar spine which are robust, accurate, and computationally tractable. In this paper, we reconstruct a statistical multi-vertebrae pose+shape model and utilize it in a novel framework for labeling and segmentation of the vertebra in a CT image. We validate our technique in terms of accuracy of the labeling and segmentation of CT images acquired from 56 subjects. The method correctly labels all vertebrae in 70% of patients and is only one level off for the remaining 30%. The mean distance error achieved for the segmentation is 2.1 +/- 0.7 mm.

Citation Download Citation

Abtin Rasoulian, Robert N. Rohling, and Purang Abolmaesumi "Automatic labeling and segmentation of vertebrae in CT images", Proc. SPIE 9036, Medical Imaging 2014: Image-Guided Procedures, Robotic Interventions, and Modeling, 903623 (12 March 2014); https://doi.org/10.1117/12.2043256

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