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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. ; Landman, Bennett A. (2019):
Semi-automatic segmentation of JIA-induced inflammation in MRI images of ankle joints.
p. 123, SPIE Medical Imaging 2019, San Diego, United States, 16.-21. February 2019, DOI: 10.1117/12.2512986,
[Conference or Workshop Item]

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.

Item Type: Conference or Workshop Item
Erschienen: 2019
Creators: Wang, Anqi ; Franke, Andreas ; Wesarg, Stefan ; Angelini, Elsa D. ; Landman, Bennett A.
Title: Semi-automatic segmentation of JIA-induced inflammation in MRI images of ankle joints
Language: English
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.

Uncontrolled Keywords: 3D Segmentation Image processing
Divisions: 20 Department of Computer Science
20 Department of Computer Science > Interactive Graphics Systems
Event Title: SPIE Medical Imaging 2019
Event Location: San Diego, United States
Event Dates: 16.-21. February 2019
Date Deposited: 09 Apr 2020 10:25
DOI: 10.1117/12.2512986
Official URL: https://doi.org/10.1117/12.2512986
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