Share Email Print

Proceedings Paper

Automatic segmentation of lumbar vertebrae in CT images
Author(s): Amruta Kulkarni; Akshita Raina; Mona Sharifi Sarabi; Christine S. Ahn; Diana Babayan; Bilwaj Gaonkar; Luke Macyszyn; Cauligi Raghavendra
Format Member Price Non-Member Price
PDF $17.00 $21.00

Paper Abstract

Lower back pain is one of the most prevalent disorders in the developed/developing world. However, its etiology is poorly understood and treatment is often determined subjectively. In order to quantitatively study the emergence and evolution of back pain, it is necessary to develop consistently measurable markers for pathology. Imaging based measures offer one solution to this problem. The development of imaging based on quantitative biomarkers for the lower back necessitates automated techniques to acquire this data. While the problem of segmenting lumbar vertebrae has been addressed repeatedly in literature, the associated problem of computing relevant biomarkers on the basis of the segmentation has not been addressed thoroughly. In this paper, we propose a Random-Forest based approach that learns to segment vertebral bodies in CT images followed by a biomarker evaluation framework that extracts vertebral heights and widths from the segmentations obtained. Our dataset consists of 15 CT sagittal scans obtained from General Electric Healthcare. Our main approach is divided into three parts: the first stage is image pre-processing which is used to correct for variations in illumination across all the images followed by preparing the foreground and background objects from images; the next stage is Machine Learning using Random-Forests, which distinguishes the interest-point vectors between foreground or background; and the last step is image post-processing, which is crucial to refine the results of classifier. The Dice coefficient was used as a statistical validation metric to evaluate the performance of our segmentations with an average value of 0.725 for our dataset.

Paper Details

Date Published: 3 March 2017
PDF: 8 pages
Proc. SPIE 10134, Medical Imaging 2017: Computer-Aided Diagnosis, 1013448 (3 March 2017); doi: 10.1117/12.2254697
Show Author Affiliations
Amruta Kulkarni, The Univ. of Southern California (United States)
Akshita Raina, The Univ. of Southern California (United States)
Mona Sharifi Sarabi, The Univ. of Southern California (United States)
Christine S. Ahn, Univ. of California, Los Angeles (United States)
Diana Babayan, Univ. of California, Los Angeles (United States)
Bilwaj Gaonkar, Univ. of California, Los Angeles (United States)
Luke Macyszyn, Univ. of California, Los Angeles (United States)
Cauligi Raghavendra, The Univ. of Southern California (United States)

Published in SPIE Proceedings Vol. 10134:
Medical Imaging 2017: Computer-Aided Diagnosis
Samuel G. Armato III; Nicholas A. Petrick, Editor(s)

© SPIE. Terms of Use
Back to Top
Sign in to read the full article
Create a free SPIE account to get access to
premium articles and original research
Forgot your username?