Date of Award
Doctor of Philosophy (PhD)
Rheumatoid arthritis (RA) is a systemic disease that can affect the nervous system, lungs, heart, skin, reticuloendothelium and joints. Currently, the gold-standard measurement for tracking the progression of the disease involves a semi-quantitative assessment of bone erosion, bone marrow edema and synovitis, as seen in magnetic resonance (MR) images, by a musculoskeletal radiologist. The work presented in this thesis identifies how computer automation can be used to quantify bone erosion volumes in MR images without a radiologists' expert and time consuming intervention. A new semi-automated hybrid segmentation algorithm that combines two established techniques: region growing and level-set segmentation, is described and evaluated for use in a clinical setting. A total of 40 participants with RA were scanned using a 1-Tesla peripheral MR scanner. Eight of the participant scans were used to train the algorithm with the remaining used to determine the accuracy, precision, and speed of the technique. The reproducibility of the hybrid algorithm and that of manual segmentation were defined in terms of intra-class correlation coefficients (ICCs). Both techniques were equally precise with ICC values greater than 0.9. According to a least squares fit between erosion volumes obtained by the hybrid algorithm with those obtained from manual tracings drawn by a radiologist, the former was found to be highly accurate ( m=1.030, b=1.385: r-squared=0.923). The hybrid algorithm was significantly faster than manual segmentation, which took two to four times longer to complete. In conclusion, computer automation shows promise as a means to quantitatively assess bone erosion volumes. The new hybrid segmentation algorithm described in this thesis could be used in a clinical setting to track the progression of RA and to evaluate the effectiveness of treatment.
Emond, Patrick D., "Bone Erosion Measurement in Subjects with Rheumatoid Arthritis Using Magnetic Resonance Imaging" (2012). Open Access Dissertations and Theses. Paper 6730.
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