Differentiation of fat, muscle, and edema in thigh MRIs using random forest classification

William Kovacs; Chia-Ying Liu; Ronald M. Summers; Jianhua Yao

doi:10.1117/12.2217606

24 March 2016 Differentiation of fat, muscle, and edema in thigh MRIs using random forest classification

William Kovacs, Chia-Ying Liu, Ronald M. Summers, Jianhua Yao

Proceedings Volume 9785, Medical Imaging 2016: Computer-Aided Diagnosis; 978507 (2016) https://doi.org/10.1117/12.2217606
Event: SPIE Medical Imaging, 2016, San Diego, California, United States

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.

Citation Download Citation

William Kovacs, Chia-Ying Liu, Ronald M. Summers, and Jianhua Yao "Differentiation of fat, muscle, and edema in thigh MRIs using random forest classification", Proc. SPIE 9785, Medical Imaging 2016: Computer-Aided Diagnosis, 978507 (24 March 2016); https://doi.org/10.1117/12.2217606

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