Comprehensive Summary
This paper examines the application of enhanced segmenting techniques for gliomas using U-Net architecture and pre-trained U-Net backbone networks with different MRI weights. Accurate and timely segmentation of gliomas is essential in clinical settings as the results often impact the course of treatment for the patient. Gliomas are known to have three specific sub-regions: edema, necrotic, and active tumor. This study applied pre-trained and tested U-Net backbones ResNet, Inception, and VGG on the Brain Tumor Segmentation Challenge (BraTS) 2021 dataset. Model performance was evaluated across multiple MRI weights, including T1, T2, T1 post-contrast (T1Gd), and T2-Fluid-Attenuated Inversion Recovery (FLAIR), using Accuracy (ACC) and Intersection over Union (IoU) as metrics. Results showed that ResNet-U-Net with T1Gd provided the best segmentation for necrotic and active tumor regions (ACC: 98.62%, IoU: 0.830), while ResNet-U-Net with T2-FLAIR was most effective for edema (ACC: 98.42%, IoU: 0.795). In comparison, the standard U-Net achieved its best results with T1Gd (ACC: 98.15%, IoU: 0.782). Pre-trained U-Net models improve glioma MRI segmentation, with potential to support clinical decision-making.
Outcomes and Implications
This research is significant for accelerated glioma management and treatment. Manual segmentation of gliomas is both time-consuming and not entirely accurate. With an enhanced segmentation model, like pre-trained U-Net architectures across various MRI modalities, glioma sub-regions can be clearly outlined for providers to establish a treatment plan. For clinicians, this enables rapid and accurate delineation of tumor subregions, supporting more targeted radiation, surgical interventions, and monitoring of treatment response. Additionally, automated and standardized segmentation eliminates the issue of provider variability when it comes to assessing patient MRIs. Enhanced segmentation technology facilitates personalized treatment strategies and allows imaging data to be incorporated into predictive models for patient outcomes.