Validation of U-Net models for direct EPI Segmentation of brain MRI: towards faster and accurate diffusion Analysis

Motivation: Brain sub-region segmentation from MRI scans aids in detailed structural analysis. We attempt to directly segment EPI to simplify diffusion metric analysis, potentially allowing for swift regional analysis of diffusion metrics. Goal(s): Our primary goal is to develop deep learning U-Net models for EPI segmentation, aiming to circumvent the necessity for T1 images and to simplify the segmentation workflow. Approach: We collected 3182 datasets from public MRI databases, enhancing ground-truth labels through distortion correction methods. Results: The ASEG model achieves the highest Dice coefficient (0.709), reducing execution time significantly. Subsequent analyses show ASEG model's diffusion results correlate highly with conventional template registration. Impact: The results enhanced speed and precision in EPI segmentation, promising substantial advancements in clinical and research domains through rapid acquisition of brain structural information. The anticipated open-source availability of this methodology stands to greatly facilitate clinical research involving regional brain analysis.