Glioblastoma Tumor Segmentation using an Ensemble of Vision Transformers
Huafeng Liu Benjamin Dowdell Todd Engelder Zarah Pulmano Nicolas Osa Arko Barman
Glioblastoma is one of the most aggressive and deadliest types of brain
cancer, with low survival rates compared to other types of cancer. Analysis of
Magnetic Resonance Imaging (MRI) scans is one of the most effective methods for
the diagnosis and treatment of brain cancers such as glioblastoma. Accurate
tumor segmentation in MRI images is often required for treatment planning and
risk assessment of treatment methods. Here, we propose a novel pipeline, Brain
Radiology Aided by Intelligent Neural NETworks (BRAINNET), which leverages
MaskFormer, a vision transformer model, and generates robust tumor segmentation
maks. We use an ensemble of nine predictions from three models separately
trained 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 the
publicly available UPenn-GBM dataset, consisting of 3D multi-parametric MRI
(mpMRI) scans from 611 subjects. Using Dice coefficient (DC) and 95% Hausdorff
distance (HD) for evaluation, our models achieved state-of-the-art results in
segmenting 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).