Precise thigh muscle volumes are crucial to monitor the motor functionality of patients with diseases that may result in various degrees of thigh muscle loss. T1-weighted MRI is the default surrogate to obtain thigh muscle masks due to its contrast between muscle and fat signals. Deep learning approaches have recently been widely used to obtain these masks through segmentation. However, due to the insufficient amount of precise annotations, thigh muscle masks generated by deep learning approaches tend to misclassify intra-muscular fat (IMF) as muscle impacting the analysis of muscle volumetrics. As IMF is infiltrated inside the muscle, human annotations require expertise and time. Thus, precise muscle masks where IMF is excluded are limited in practice. To alleviate this, we propose a few-shot segmentation framework to generate thigh muscle masks excluding IMF. In our framework, we design a novel pseudo-label correction and evaluation scheme, together with a new noise robust loss for exploiting high certainty areas. The proposed framework only takes $1\%$ of the fine-annotated training dataset, and achieves comparable performance with fully supervised methods according to the experimental results.

翻译：精确的大腿肌肉体积对于监测可能导致不同程度大腿肌肉萎缩的患者的运动功能至关重要。T1加权MRI因其对肌肉与脂肪信号的对比度，已成为获取大腿肌肉掩模的默认替代方法。近年来，深度学习方法被广泛用于通过分割获取这些掩模。然而，由于精确标注数据量不足，深度学习方法生成的大腿肌肉掩模往往会将肌内脂肪误分类为肌肉，从而影响肌肉体积分析。由于肌内脂肪浸润在肌肉内部，人工标注需要专业知识和时间，因此实践中排除了肌内脂肪的精确肌肉掩模十分有限。为解决这一问题，我们提出了一种少样本分割框架，用于生成排除肌内脂肪的大腿肌肉掩模。在该框架中，我们设计了一种新型伪标签校正与评估方案，并引入了一种新的噪声鲁棒损失函数以利用高置信度区域。实验结果表明，所提框架仅需$1\%$的精细标注训练数据集，即可达到与全监督方法相当的性能。