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.

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), T 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, T Conclusions: Within the present experimental framework, Attention U-Net trained with T