Unified Benchmark Compares Deep Learning Models for 3D Brain Tumor Segmentation

A recent benchmark study offers a comprehensive experimental evaluation of deep learning models aimed at 3D brain tumor segmentation using magnetic resonance imaging (MRI). Published on arXiv (ID: 2607.28858), this research tackles the issue of objective model assessment by analyzing five leading segmentation models—3D U-Net, SegResNet, Swin UNETR, SegMamba, and SegMambaV2—under consistent conditions. These models encompass convolutional neural networks (CNNs), Transformer-based frameworks, and novel State Space Model (SSM) designs. The assessment utilizes two distinct brain tumor segmentation datasets: intracranial meningioma segmentation (BraTS 2023) and post-treatment glioblastoma cases. By standardizing datasets, preprocessing, training methods, and evaluation processes, this benchmark aims to enhance computer-assisted diagnosis, treatment planning, and disease monitoring in neuro-oncology.