Precise Few-shot Fat-free Thigh Muscle Segmentation in T1-weighted MRI
This addresses the challenge of limited precise annotations for thigh muscle segmentation in medical imaging, offering a more efficient solution for monitoring muscle loss in patients with diseases.
The paper tackled the problem of misclassifying intra-muscular fat as muscle in thigh MRI segmentation, which affects muscle volume analysis, by proposing a few-shot framework that achieves comparable performance to fully supervised methods using only 1% of fine-annotated data.
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.