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 T1 post-contrast enhancement (T1Gd) has the highest ACC and IoU for the necrotic and active tumor regions semantic segmentation in glioma. It was also demonstrated that a trained ResNet-U-Net with T2 Fluid-Attenuated Inversion Recovery (T2-FLAIR) is a suitable combination for edema segmentation in glioma. Our study further validates that the proposed framework’s architecture and modules are scientifically grounded and practical, enabling the extraction and aggregation of valuable semantic information to enhance glioma semantic segmentation capability. It demonstrates how useful the ResNet-U-Net will be for physicians to extract glioma regions automatically. Keywords Glioma, Artificial intelligence, Segmentation, Magnetic resonance imaging