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Segmentation of joint and musculoskeletal tissue in the study of arthritis

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Abstract

As the most frequent cause of physical disability, musculoskeletal diseases such as arthritis and osteoporosis have a great social and economical impact. Quantitative magnetic resonance imaging (MRI) biomarkers are important tools that allow clinicians to better characterize, monitor, and even predict musculoskeletal disease progression. Post-processing pipelines often include image segmentation. Manually identifying the border of the region of interest (ROI) is a difficult and time-consuming task. Manual segmentation is also affected by inter- and intrauser variability, thus limiting standardization. Fully automatic or semi-automatic methods that minimize the user interaction are highly desirable. Unfortunately, an ultimate, highly reliable and extensively evaluated solution for joint and musculoskeletal tissue segmentation has not yet been proposed, and many clinical studies still adopt fully manual procedures. Moreover, the clinical translation of several promising quantitative MRI techniques is highly affected by the lack of an established, fast, and accurate segmentation method. The goal of this review is to present some of the techniques proposed in recent literature that have been adopted in clinical studies for joint and musculoskeletal tissue analyses in arthritis patients. The most widely used MRI sequences and image processing algorithms employed to accomplish segmentation challenges will be discussed in this paper.

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The authors would like to thank Colin Russell for proofreading the manuscript.

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  1. Musculoskeletal Quantitative Imaging Research Group, Department of Radiology and Biomedical Imaging, UC San Francisco, 1700 Fourth Street, Suite 201, QB3 Building, San Francisco, CA, 94107, USA

    Valentina Pedoia, Sharmila Majumdar & Thomas M. Link

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  1. Valentina Pedoia
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Pedoia, V., Majumdar, S. & Link, T.M. Segmentation of joint and musculoskeletal tissue in the study of arthritis. Magn Reson Mater Phy 29, 207–221 (2016). https://doi.org/10.1007/s10334-016-0532-9

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