ArticleMagma (New York, N.Y.)2025
Importance of neural network complexity for the automatic segmentation of individual thigh muscles in MRI images from patients with neuromuscular diseases.
Article in Magma (New York, N.Y.), 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.
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Who cites it
2 citing papers in PubMed.
- Automated thigh muscle segmentation using foundation models: validation and impact of segmentation quality.European radiology experimental · 2026Article
- Optimized reconstruction of undersampled Dixon sequences using new memory-efficient unrolled deep neural networks: HalfVarNet and HalfDIRCN.Magnetic resonance in medicine · 2026Article
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Authors and funding
10 authors.
Funding
Abstract
objectiveSegmentation of individual thigh muscles in MRI images is essential for monitoring neuromuscular diseases and quantifying relevant biomarkers such as fat fraction (FF). Deep learning approaches such as U-Net have demonstrated effectiveness in this field. However, the impact of reducing neural network complexity remains unexplored in the FF quantification in individual muscles. MATERIAL AND
methodsU-Net architectures with different complexities have been compared for the quantification of the fat fraction in each muscle group selected in the central part of the thigh region. The corresponding performance has been assessed in terms of Dice score (DSC) and FF quantification error. The database contained 1450 thigh images from 59 patients and 14 healthy subjects (age: 47 ± 17 years, sex: 36F, 37M). Ten individual muscles were segmented in each image. The performance of each model was compared to nnU-Net, a complex architecture with 4.35
resultsAs expected, nnU-Net achieved the highest DSC (94.77 ± 0.13%). A simpler U-Net (5.81 DISCUSSION: The poor correlation between observed DSC and FF indicates that less complex architectures, reducing GPU memory utilization and training time, can still accurately quantify FF.
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