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Semi-automatic segmentation of JIA-induced inflammation in MRI images of ankle joints
Author(s): Anqi Wang; Andreas Franke; Stefan Wesarg
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Paper Abstract

The autoimmune disease Juvenile Idiopathic Arthritis (JIA) affects children of under 16 years and leads to the symptom of inflamed synovial membranes in affected joints. In clinical practice, characteristics of these inflamed membranes are used to stage the disease progression and to predict erosive bone damage. Manual outlining of inflammatory regions in each slide of a MRI dataset is still the gold standard for detection and quantification, however, this process is very tiresome and time-consuming. In addition, the inter- and intra-observer variability is a known problem of human annotators. We have developed the first method to detect inflamed regions in and around major joints in the human ankle. First, we use an adapted coupled shape model framework to segment the ankle bones in a MRI dataset. Based on these segmentations, joints are defined as locations where two bones are particularly close to each other. A number of potential inflammation candidates are generated using multi-level thresholding. Since it is known that inflamed synovial membranes occur in the proximity of joints, we filter out structures with similar intensities such as vessels and tendons sheaths using not only a vesselness filter, but also their distance to the joints and their size. The method has been evaluated on a set of 10 manually annotated clinical MRI datasets and achieved the following results: Precision 0.6785 ± 0.1584, Recall 0.5388 ± 0.1213, DICE 0.5696 ± 0.0976.

Paper Details

Date Published: 15 March 2019
PDF: 7 pages
Proc. SPIE 10949, Medical Imaging 2019: Image Processing, 109493E (15 March 2019); doi: 10.1117/12.2512986
Show Author Affiliations
Anqi Wang, Fraunhofer-Institut für Graphische Datenverarbeitung (Germany)
Technische Univ. Darmstadt (Germany)
Andreas Franke, Technische Univ. Darmstadt (Germany)
Stefan Wesarg, Fraunhofer-Institut für Graphische Datenverarbeitung (Germany)
Technische Univ. Darmstadt (Germany)

Published in SPIE Proceedings Vol. 10949:
Medical Imaging 2019: Image Processing
Elsa D. Angelini; Bennett A. Landman, Editor(s)

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