European journal of radiology open · 2026
Impact of MRI modality selection on glioma sub-region segmentation using 3D U-Net and Attention U-Net: A comparative evaluation across ten MRI sequences.
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Khorasani Amir
Аннотация
BACKGROUND: Accurate segmentation of glioma sub-regions is essential for treatment planning and clinical decision-making. The selection of MRI input modality plays a critical role in the performance of deep learning-based segmentation models, as each weighting captures distinct tissue contrasts and physiological characteristics. This study performed a comparative evaluation of the impact of MRI modality selection on semantic segmentation of glioma sub-regions.
METHODS: A total of 468 subjects from the UCSF-PDGM dataset were included. Ten MRI weightings were assessed independently: apparent diffusion coefficient (ADC) maps, arterial spin labeling (ASL), diffusion-weighted imaging (DWI), fractional anisotropy (FA), mean diffusivity (MD), susceptibility-weighted imaging (SWI), T1-weighted, T1-weighted post-contrast (T1Gd), FLAIR, and T2-weighted imaging. For each modality, dedicated 3D U-Net and 3D Attention U-Net models were trained. TOPSIS multi-criteria ranking was applied to identify optimal model-modality combinations per sub-region and overall.
RESULTS: The 3D Attention U-Net numerically outperformed the standard U-Net across all modalities and sub-regions, with significant differences in several sequence-region comparisons. Sub-region-specific TOPSIS rankings identified ASL as optimal for active tumor segmentation, T1Gd for necrotic core delineation, and FLAIR for peritumoral edema. Overall TOPSIS ranking, aggregated across all sub-regions, identified the Attention U-Net trained on T1Gd as the most consistently high-performing combination. In settings where gadolinium administration is contraindicated, ASL and MD emerged as the strongest non-contrast alternatives.
CONCLUSIONS: Within the present experimental framework, Attention U-Net trained with T1Gd yielded the most consistently high-performing single-modality results for comprehensive segmentation, whereas ASL emerged as a promising contrast-free candidate warranting further evaluation.
METHODS: A total of 468 subjects from the UCSF-PDGM dataset were included. Ten MRI weightings were assessed independently: apparent diffusion coefficient (ADC) maps, arterial spin labeling (ASL), diffusion-weighted imaging (DWI), fractional anisotropy (FA), mean diffusivity (MD), susceptibility-weighted imaging (SWI), T1-weighted, T1-weighted post-contrast (T1Gd), FLAIR, and T2-weighted imaging. For each modality, dedicated 3D U-Net and 3D Attention U-Net models were trained. TOPSIS multi-criteria ranking was applied to identify optimal model-modality combinations per sub-region and overall.
RESULTS: The 3D Attention U-Net numerically outperformed the standard U-Net across all modalities and sub-regions, with significant differences in several sequence-region comparisons. Sub-region-specific TOPSIS rankings identified ASL as optimal for active tumor segmentation, T1Gd for necrotic core delineation, and FLAIR for peritumoral edema. Overall TOPSIS ranking, aggregated across all sub-regions, identified the Attention U-Net trained on T1Gd as the most consistently high-performing combination. In settings where gadolinium administration is contraindicated, ASL and MD emerged as the strongest non-contrast alternatives.
CONCLUSIONS: Within the present experimental framework, Attention U-Net trained with T1Gd yielded the most consistently high-performing single-modality results for comprehensive segmentation, whereas ASL emerged as a promising contrast-free candidate warranting further evaluation.