Accurate 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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Mohapatra et al. (2024) studied this question.
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