Impact of You Only Look Once (YOLO)-Derived Regions of Interest on U-Net Segmentation of Post-Treatment Glioma in Magnetic Resonance Imaging: A Methodological Study on the BraTS (Brain Tumor Segmentation) 2024 Dataset

BackgroundHybrid deep learning pipelines combining You Only Look Once (YOLO) for lesion detection and U-Net for segmentation are increasingly applied to brain tumor MRI, yet the specific contribution of region of interest (ROI) cropping to segmentation performance remains poorly quantified.This study evaluates four ROI-based U-Net configurations for posttreatment glioma segmentation on the Brain Tumor Segmentation (BraTS) 2024 -GLI dataset, systematically varying the YOLO detector (YOLOv8m vs.YOLO11m) and the U-Net encoder backbone (ResNet-34 vs. ResNet-50). MethodsNormalized T1 volumes and expert tumor masks were converted into axial 2D slices.Two YOLO detectors were trained for single-class tumor localization; their bounding-box predictions were used to crop 256 × 256 ROIs with adaptive padding.U-Net models with ResNet-34 and ResNet-50 encoders were then trained on each ROI set using combined Dice and binary cross-entropy loss.Performance was measured by Dice score and intersection over union (IoU) on held-out validation subjects. ResultsThe best-performing configuration, YOLO11m-U-Net with a ResNet-34 encoder, achieved a validation Dice of 0.8824 and an IoU near 0.80.YOLOv8m-based ROIs with ResNet-34 produced comparable results (Dice 0.8797).ResNet-50 encoders yielded lower scores across both detectors (Dice 0.8656 and 0.8601). ConclusionsFailure cases were predominantly linked to missed or poorly localized detections.These findings indicate that moderatedepth encoders paired with high-quality ROI generation offer the most favorable accuracy-efficiency tradeoff for posttreatment glioma segmentation in hybrid computer-aided diagnosis systems.