  Convolutional Neural Networks (CNN) have emerged as powerful tools for
learning discriminative image features. In this paper, we propose a framework
of 3-D fully CNN models for Glioblastoma segmentation from multi-modality MRI
data. By generalizing CNN models to true 3-D convolutions in learning 3-D tumor
MRI data, the proposed approach utilizes a unique network architecture to
decouple image pixels. Specifically, we design a convolutional layer with
pre-defined Difference- of-Gaussian (DoG) filters to perform true 3-D
convolution incorporating local neighborhood information at each pixel. We then
use three trained convolutional layers that act to decouple voxels from the
initial 3-D convolution. The proposed framework allows identification of
high-level tumor structures on MRI. We evaluate segmentation performance on the
BRATS segmentation dataset with 274 tumor samples. Extensive experimental
results demonstrate encouraging performance of the proposed approach comparing
to the state-of-the-art methods. Our data-driven approach achieves a median
Dice score accuracy of 89% in whole tumor glioblastoma segmentation, revealing
a generalized low-bias possibility to learn from medium-size MRI datasets.
