ArticleScientific reports2025
Enhanced glioma semantic segmentation using U-net and pre-trained backbone U-net architectures.
Article in Scientific reports, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.
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4 citing papers in PubMed.
- Impact of MRI modality selection on glioma sub-region segmentation using 3D U-Net and Attention U-Net: A comparative evaluation across ten MRI sequences.European journal of radiology open · 2026Article
- Article
- Modeling Ischemic Stroke Pathological Dynamics via Continuous Fields and Vector Flow.NPJ digital medicine · 2026Article
- Automated Brain Tumor MRI Segmentation Using ARU-Net with Residual-Attention Modules.Diagnostics (Basel, Switzerland) · 2025Article
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Abstract
Gliomas are known to have different sub-regions within the tumor, including the edema, necrotic, and active tumor regions. Segmenting of these regions is very important for glioma treatment decisions and management. This paper aims to demonstrate the application of U-Net and pre-trained U-Net backbone networks in glioma semantic segmentation, utilizing different magnetic resonance imaging (MRI) image weights. The data used in this study for network training, validation, and testing is the Multimodal Brain Tumor Segmentation (BraTS) 2021 challenge. In this study, we applied the U-Net and different pre-trained Backbone U-Net for the semantic segmentation of glioma regions. The ResNet, Inception, and VGG networks, which are pre-trained using the ImageNet dataset, have been used as the Backbone in the U-Net architecture. The Accuracy (ACC) and Intersection over Union (IoU) were employed to assess the performance of the networks. The most prominent finding to emerge from this study is that trained ResNet-U-Net with T
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