Automated segmentation of knee and ankle regions of rats from CT images to quantify bone mineral density for monitoring treatments of rheumatoid arthritis

Francisco Cruz; Raquel Sevilla; Joe Zhu; Amy Vanko; Jung Hoon Lee; Belma Dogdas; Weisheng Zhang

doi:10.1117/12.2044289

13 March 2014 Automated segmentation of knee and ankle regions of rats from CT images to quantify bone mineral density for monitoring treatments of rheumatoid arthritis

Francisco Cruz, Raquel Sevilla, Joe Zhu, Amy Vanko, Jung Hoon Lee, Belma Dogdas, Weisheng Zhang

Author Affiliations +

Proceedings Volume 9038, Medical Imaging 2014: Biomedical Applications in Molecular, Structural, and Functional Imaging; 903814 (2014) https://doi.org/10.1117/12.2044289
Event: SPIE Medical Imaging, 2014, San Diego, California, United States

Abstract

Bone mineral density (BMD) obtained from a CT image is an imaging biomarker used pre-clinically for characterizing the Rheumatoid arthritis (RA) phenotype. We use this biomarker in animal studies for evaluating disease progression and for testing various compounds. In the current setting, BMD measurements are obtained manually by selecting the regions of interest from three-dimensional (3-D) CT images of rat legs, which results in a laborious and low-throughput process. Combining image processing techniques, such as intensity thresholding and skeletonization, with mathematical techniques in curve fitting and curvature calculations, we developed an algorithm for quick, consistent, and automatic detection of joints in large CT data sets. The implemented algorithm has reduced analysis time for a study with 200 CT images from 10 days to 3 days and has improved the robust detection of the obtained regions of interest compared with manual segmentation. This algorithm has been used successfully in over 40 studies.

Citation Download Citation

Francisco Cruz, Raquel Sevilla, Joe Zhu, Amy Vanko, Jung Hoon Lee, Belma Dogdas, and Weisheng Zhang "Automated segmentation of knee and ankle regions of rats from CT images to quantify bone mineral density for monitoring treatments of rheumatoid arthritis", Proc. SPIE 9038, Medical Imaging 2014: Biomedical Applications in Molecular, Structural, and Functional Imaging, 903814 (13 March 2014); https://doi.org/10.1117/12.2044289

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KEYWORDS

Image segmentation

Bone

Computed tomography

Image processing

Image processing algorithms and systems

Minerals

Algorithm development

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