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Abstract #4064

Variational Model Augmented Deep Learning for Small Training Data MRI Thigh Muscle Segmentation

Paramjyoti Mohapatra1, Weihong Guo1, Mingrui Yang2, Richard Lartey2, and Xiaojuan Li2
1Mathematics, Applied Mathematics and Statistics, Case Western Reserve University, Cleveland, OH, United States, 2Lerner Research Institute, Cleveland Clinic, Cleveland, OH, United States

Synopsis

Keywords: Muscle, Machine Learning/Artificial Intelligence, muscle segmentation, fusion, variational modelAccurate automatic MRI thigh muscle group segmentation is essential for muscle morphology and composition which are related to osteoarthritis and sarcopenia. Due to challenges caused by lack of contrast between muscle groups, Deep Learning (DL) becomes a more natural choice than traditional model based approaches. It is however expensive to obtain a large amount of training data and DL using small training data often results in overfitting. We use a variational model based segmentation method in conjunction with a Bayesian neural network to optimize the train framework, producing about 1.6% increase in dice coefficients while working with minimal annotated data.

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Keywords