Abstract: “Glioma has been one of the most common life-threatening brain tumor diseases all over the world with different levels of aggressiveness: Low Grade Glioma (LGG) and High Grade Glioma (HGG), and consequently automated glioma grade prediction methods based on multi-modal MRI images are of great interest. However, the development of effective automated methods, and in particular convolutional neural networks (CNN) for fast and accurate medical image analysis has relied on the availability of large annotated training datasets. The purpose of this study was to develop a 2D CNN model, Triplanar-CNN, to a fully automated and accurate glioma grade prediction, using a small training dataset of less than 300 glioma patients who underwent pre-operative volumetric MRI exams, which included FLAIR, T1Gd, T1, and T2 modalities. Our approach operates on all of the MRI modalities and plane slices (axial, coronal, and sagittal) based on reconstructing the volumetric MRI as a set of 2D stacked slices in t”

Keywords: Deep Learning, Convolutional Neural Networks, Glioma grading, Multi-Modal MRI