  Non-invasive techniques such as magnetic resonance imaging (MRI) are widely
employed in brain tumor diagnostics. However, manual segmentation of brain
tumors from 3D MRI volumes is a time-consuming task that requires trained
expert radiologists. Due to the subjectivity of manual segmentation, there is
low inter-rater reliability which can result in diagnostic discrepancies. As
the success of many brain tumor treatments depends on early intervention, early
detection is paramount. In this context, a fully automated segmentation method
for brain tumor segmentation is necessary as an efficient and reliable method
for brain tumor detection and quantification. In this study, we propose an
end-to-end approach for brain tumor segmentation, capitalizing on a modified
version of QuickNAT, a brain tissue type segmentation deep convolutional neural
network (CNN). Our method was evaluated on a data set of 233 patient's T1
weighted images containing three tumor type classes annotated (meningioma,
glioma, and pituitary). Our model, QuickTumorNet, demonstrated fast, reliable,
and accurate brain tumor segmentation that can be utilized to assist clinicians
in diagnosis and treatment.
