Precise Few-shot Fat-free Thigh Muscle Segmentation in T1-weighted MRI

TL;DR

This paper introduces a few-shot segmentation framework for thigh muscle MRI that accurately excludes intra-muscular fat using minimal annotations, leveraging pseudo-label correction and noise-robust loss.

Contribution

The novel framework achieves high accuracy with only 1% of annotated data, improving thigh muscle segmentation in MRI.

Findings

Comparable performance to fully supervised methods

Effective exclusion of intra-muscular fat in segmentation

Reduces annotation effort significantly

Abstract

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.