3 March 2017 Automatic segmentation of lumbar vertebrae in CT images
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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.
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Amruta Kulkarni, Akshita Raina, Mona Sharifi Sarabi, Christine S. Ahn, Diana Babayan, Bilwaj Gaonkar, Luke Macyszyn, Cauligi Raghavendra, "Automatic segmentation of lumbar vertebrae in CT images", Proc. SPIE 10134, Medical Imaging 2017: Computer-Aided Diagnosis, 1013448 (3 March 2017); doi: 10.1117/12.2254697; https://doi.org/10.1117/12.2254697
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