Automatic Segmentation of Muscle Tissue and Inter-muscular Fat in Thigh and Calf MRI Images

TL;DR

This paper presents a deep-learning method for accurately segmenting muscle and fat tissues in thigh and calf MRI images, improving quantification of fat infiltration in muscular dystrophies across varying severity levels.

Contribution

The study introduces a novel deep-learning approach that achieves high accuracy in segmenting muscle and fat tissues, outperforming existing methods especially in severe infiltration cases.

Findings

High Dice Similarity Coefficient for muscle-region (0.964)

High DSC for healthy muscle (0.917)

High DSC for inter-muscular adipose tissue (0.933)

Abstract

Magnetic resonance imaging (MRI) of thigh and calf muscles is one of the most effective techniques for estimating fat infiltration into muscular dystrophies. The infiltration of adipose tissue into the diseased muscle region varies in its severity across, and within, patients. In order to efficiently quantify the infiltration of fat, accurate segmentation of muscle and fat is needed. An estimation of the amount of infiltrated fat is typically done visually by experts. Several algorithmic solutions have been proposed for automatic segmentation. While these methods may work well in mild cases, they struggle in moderate and severe cases due to the high variability in the intensity of infiltration, and the tissue's heterogeneous nature. To address these challenges, we propose a deep-learning approach, producing robust results with high Dice Similarity Coefficient (DSC) of 0.964, 0.917 and 0.933 for muscle-region, healthy muscle and inter-muscular adipose tissue (IMAT) segmentation, respectively.