A Comparative Study of Unet Variants for Low-Grade Glioma Segmentation in Magnetic Resonance Imaging

Abstract

Brain tumors originating from glial cells are pathological entities that significantly impact quality of life and are classified based on their malignancy into low-grade gliomas (LGGs) and high-grade gliomas (HGGs). While the more aggressive HGGs have been extensively studied, LGGs are of critical importance for early diagnosis due to their potential progression to HGGs if left untreated. This has driven researchers to develop methods for the rapid and consistent diagnosis of LGGs. In this study, three models—UNet, Transformer UNet, and Super Vision UNet—were comparatively evaluated for the automatic segmentation of LGGs using magnetic resonance imaging (MRI) data. Multimodal MRI scans from 110 patients, retrieved from The Cancer Imaging Archive (TCIA), were used to train the models. Performance was evaluated using Dice Coefficient, Tversky Index, and Intersection over Union (IoU) metrics. The Super Vision UNet achieves the highest Dice (0.9115) and Tversky (0.9154) scores, while the Transformer UNet attains the highest IoU (0.8789). Both advanced models demonstrate superior segmentation performance with lower loss values compared to the conventional UNet. Visual outputs indicate that the modern architectures delineate tumor contours with greater precision. These results highlight the effectiveness and reliability of contemporary UNet-based and Transformer-based architectures in segmenting complex tumor structures such as LGGs. Integrating these models into clinical decision support systems holds promise for enhancing the speed and accuracy of the diagnostic process. © 2025 Elsevier B.V., All rights reserved.