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Precise localization and volumetric segmentation of glioblastoma before and after surgery are crucial for various clinical purposes, including post-surgery treatment planning, monitoring tumour recurrence, and creating radiotherapy maps. Manual delineation is time-consuming and prone to errors, hence the adoption of automated 3D quantification methods using deep learning algorithms from MRI scans in recent times. However, automated segmentation often leads to over-segmentation or under-segmentation of tumour regions. Introducing an interactive deep-learning tool would empower radiologists to rectify these inaccuracies by adjusting the over-segmented and under-segmented voxels as needed. This paper proposes a network named Atten-SEVNETR, that has a combined architecture of vision transformers and convolutional neural networks (CNN). This hybrid architecture helps to learn the input volume representation in sequences and focuses on the global multi-scale information. An interactive graphical user interface is also developed where the initial 3D segmentation of glioblastoma can be interactively corrected to remove falsely detected spurious tumour regions. Atten-SEVNETR is trained on BraTS training dataset and tested on BraTS validation dataset and on Uppsala University post-operative glioblastoma dataset. The methodology outperformed state-of-the-art networks like nnFormer, SwinUNet, and SwinUNETR. The mean dice score achieved is 0.7302, and the mean Hausdorff distance-95 got is 7.78 mm for the Uppsala University dataset.