Automatic lumbar vertebra segmentation from clinical CT for wedge compression fracture diagnosis

Subarna Ghosh; Raja' S. Alomari; Vipin Chaudhary; Gurmeet Dhillon

doi:10.1117/12.878055

4 March 2011 Automatic lumbar vertebra segmentation from clinical CT for wedge compression fracture diagnosis

Subarna Ghosh, Raja' S. Alomari, Vipin Chaudhary, Gurmeet Dhillon

Author Affiliations +

Proceedings Volume 7963, Medical Imaging 2011: Computer-Aided Diagnosis; 796303 (2011) https://doi.org/10.1117/12.878055
Event: SPIE Medical Imaging, 2011, Lake Buena Vista (Orlando), Florida, United States

Abstract

Lumbar vertebral fractures vary greatly in types and causes and usually result from severe trauma or pathological conditions such as osteoporosis. Lumbar wedge compression fractures are amongst the most common ones where the vertebra is severely compressed forming a wedge shape and causing pain and pressure on the nerve roots and the spine. Since vertebral segmentation is the first step in any automated diagnosis task, we present a fully automated method for robustly localizing and segmenting the vertebrae for preparation of vertebral fracture diagnosis. Our segmentation method consists of five main steps towards the CAD(Computer-Aided Diagnosis) system: 1) Localization of the intervertebral discs. 2) Localization of the vertebral skeleton. 3) Segmentation of the individual vertebra. 4) Detection of the vertebrae center line and 5) Detection of the vertebrae major boundary points. Our segmentation results are promising with an average error of 1.5mm (modified Hausdorff distance metric) on 50 clinical CT cases i.e. a total of 250 lumbar vertebrae. We also present promising preliminary results for automatic wedge compression fracture diagnosis on 15 cases, 7 of which have one or more vertebral compression fracture, and obtain an accuracy of 97.33%.

Citation Download Citation

Subarna Ghosh, Raja' S. Alomari, Vipin Chaudhary, and Gurmeet Dhillon "Automatic lumbar vertebra segmentation from clinical CT for wedge compression fracture diagnosis", Proc. SPIE 7963, Medical Imaging 2011: Computer-Aided Diagnosis, 796303 (4 March 2011); https://doi.org/10.1117/12.878055

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