A CADe System for Gliomas in Brain MRI using Convolutional Neural Networks

Inspired by the success of Convolutional Neural Networks (CNN), we develop a novel Computer Aided Detection (CADe) system using CNN for Glioblastoma Multiforme (GBM) detection and segmentation from multi channel MRI data. A two-stage approach first identifies the presence of GBM. This is followed by a GBM localization in each "abnormal" MR slice. As part of the CADe system, two CNN architectures viz. Classification CNN (C-CNN) and Detection CNN (D-CNN) are employed. The CADe system considers MRI data consisting of four sequences ($T_1$, $T_{1c},$ $T_2$, and $T_{2FLAIR}$) as input, and automatically generates the bounding boxes encompassing the tumor regions in each slice which is deemed abnormal. Experimental results demonstrate that the proposed CADe system, when used as a preliminary step before segmentation, can allow improved delineation of tumor region while reducing false positives arising in normal areas of the brain. The GrowCut method, employed for tumor segmentation, typically requires a foreground and background seed region for initialization. Here the algorithm is initialized with seeds automatically generated from the output of the proposed CADe system, thereby resulting in improved performance as compared to that using random seeds.