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Proceedings Paper

Differentiation of fat, muscle, and edema in thigh MRIs using random forest classification
Author(s): William Kovacs; Chia-Ying Liu; Ronald M. Summers; Jianhua Yao
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Paper Abstract

There are many diseases that affect the distribution of muscles, including Duchenne and fascioscapulohumeral dystrophy among other myopathies. In these disease cases, it is important to quantify both the muscle and fat volumes to track the disease progression. There has also been evidence that abnormal signal intensity on the MR images, which often is an indication of edema or inflammation can be a good predictor for muscle deterioration. We present a fully-automated method that examines magnetic resonance (MR) images of the thigh and identifies the fat, muscle, and edema using a random forest classifier. First the thigh regions are automatically segmented using the T1 sequence. Then, inhomogeneity artifacts were corrected using the N3 technique. The T1 and STIR (short tau inverse recovery) images are then aligned using landmark based registration with the bone marrow. The normalized T1 and STIR intensity values are used to train the random forest. Once trained, the random forest can accurately classify the aforementioned classes. This method was evaluated on MR images of 9 patients. The precision values are 0.91±0.06, 0.98±0.01 and 0.50±0.29 for muscle, fat, and edema, respectively. The recall values are 0.95±0.02, 0.96±0.03 and 0.43±0.09 for muscle, fat, and edema, respectively. This demonstrates the feasibility of utilizing information from multiple MR sequences for the accurate quantification of fat, muscle and edema.

Paper Details

Date Published: 24 March 2016
PDF: 7 pages
Proc. SPIE 9785, Medical Imaging 2016: Computer-Aided Diagnosis, 978507 (24 March 2016); doi: 10.1117/12.2217606
Show Author Affiliations
William Kovacs, National Institutes of Health (United States)
Chia-Ying Liu, National Institutes of Health (United States)
Ronald M. Summers, National Institutes of Health (United States)
Jianhua Yao, National Institutes of Health (United States)


Published in SPIE Proceedings Vol. 9785:
Medical Imaging 2016: Computer-Aided Diagnosis
Georgia D. Tourassi; Samuel G. Armato, Editor(s)

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