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

arXiv: https://arxiv.org/abs/2304.14053 · HTML: https://mlsanity.com/paper/2304.14053/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 13 of its 23 references that are in the map.
- First posted on arXiv 27 Apr 2023.

## Builds on (most cited first, 13 of 13)

- [U-Net: Convolutional Networks for Biomedical Image Segmentation](https://mlsanity.com/paper/1505.04597/) (2015)
- [A Simple Framework for Contrastive Learning of Visual Representations](https://mlsanity.com/paper/2002.05709/) (2020)
- [Attention U-Net: Learning Where to Look for the Pancreas](https://mlsanity.com/paper/1804.03999/) (2018)
- [Unpaired Image-to-Image Translation using Cycle-Consistent Adversarial Networks](https://mlsanity.com/paper/1703.10593/) (2017)
- [PANet: Few-Shot Image Semantic Segmentation with Prototype Alignment](https://mlsanity.com/paper/1908.06391/) (2019)
- [Uncertainty-aware Self-ensembling Model for Semi-supervised 3D Left Atrium Segmentation](https://mlsanity.com/paper/1907.07034/) (2019)
- [Rectifying Pseudo Label Learning via Uncertainty Estimation for Domain Adaptive Semantic Segmentation](https://mlsanity.com/paper/2003.03773/) (2020)
- [Mutual Consistency Learning for Semi-supervised Medical Image Segmentation](https://mlsanity.com/paper/2109.09960/) (2021)
- [Semi-supervised Left Atrium Segmentation with Mutual Consistency Training](https://mlsanity.com/paper/2103.02911/) (2021)
- [Source-Free Domain Adaptive Fundus Image Segmentation with Denoised Pseudo-Labeling](https://mlsanity.com/paper/2109.09735/) (2021)
- [Unsupervised Instance Segmentation in Microscopy Images via Panoptic Domain Adaptation and Task Re-weighting](https://mlsanity.com/paper/2005.02066/) (2020)
- [Co-Seg: An Image Segmentation Framework Against Label Corruption](https://mlsanity.com/paper/2102.00523/) (2021)
- [Automatic Segmentation of Muscle Tissue and Inter-muscular Fat in Thigh and Calf MRI Images](https://mlsanity.com/paper/1910.04866/) (2019)

## Abstract (arXiv)

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.