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Semi-automatic Segmentation of JIA-induced Inflammation in MRI Images of Ankle Joints

 
: Wang, Anqi; Franke, Andreas; Wesarg, Stefan

:

Angelini, Elsa D. (Ed.) ; Society of Photo-Optical Instrumentation Engineers -SPIE-, Bellingham/Wash.:
Medical Imaging 2019. Image Processing : 19-21 February 2019, San Diego, California, United States
Bellingham, WA: SPIE, 2019 (Proceedings of SPIE 10949)
ISBN: 978-1-5106-2546-4
ISBN: 978-1-5106-2545-7
Paper 109493E, 8 S.
Conference "Medical Imaging - Image Processing" <2019, San Diego/Calif.>
Englisch
Konferenzbeitrag
Fraunhofer IGD ()
Lead Topic: Individual Health; Research Area: Computer vision (CV); 3D Segmentation; image processing

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.

: http://publica.fraunhofer.de/dokumente/N-559230.html