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Glioblastoma is one of the most aggressive and deadliest types of braincancer, with low survival rates compared to other types of cancer. Analysis ofMagnetic Resonance Imaging (MRI) scans is one of the most effective methods forthe diagnosis and treatment of brain cancers such as glioblastoma. Accuratetumor segmentation in MRI images is often required for treatment planning andrisk assessment of treatment methods. Here, we propose a novel pipeline, BrainRadiology Aided by Intelligent Neural NETworks (BRAINNET), which leveragesMaskFormer, a vision transformer model, and generates robust tumor segmentationmaks. We use an ensemble of nine predictions from three models separatelytrained on each of the three orthogonal 2D slice directions (axial, sagittal,and coronal) of a 3D brain MRI volume. We train and test our models on thepublicly available UPenn-GBM dataset, consisting of 3D multi-parametric MRI(mpMRI) scans from 611 subjects. Using Dice coefficient (DC) and 95% Hausdorffdistance (HD) for evaluation, our models achieved state-of-the-art results insegmenting all three different tumor regions -- tumor core (DC = 0.894, HD =2.308), whole tumor (DC = 0.891, HD = 3.552), and enhancing tumor (DC = 0.812,HD = 1.608).

Glioblastoma Tumor Segmentation using an Ensemble of Vision Transformers