In this paper, we present a fully automatic brain tumor segmentation method based on Deep Neural Networks (DNNs). The proposed networks are tailored to glioblastomas (both low and high grade) pictured in MR images. By their very nature, these tumors can appear anywhere in the brain and have almost any kind of shape, size, and contrast. These reasons motivate our exploration of a machine learning solution that exploits a flexible, high capacity DNN while being extremely efficient. Here, we give a description of different model choices that we've found to be necessary for obtaining competitive performance. We explore in particular different architectures based on Convolutional Neural Networks (CNN), i.e. DNNs specifically adapted to image data. We present a novel CNN architecture which differs from those traditionally used in computer vision. Our CNN exploits both local features as well as more global contextual features simultaneously. Also, different from most traditional uses of CNNs, our networks use a final layer that is a convolutional implementation of a fully connected layer which allows a 40 fold speed up. We also describe a 2-phase training procedure that allows us to tackle difficulties related to the imbalance of tumor labels. Finally, we explore a cascade architecture in which the output of a basic CNN is treated as an additional source of information for a subsequent CNN. Results reported on the 2013 BRATS test data-set reveal that our architecture improves over the currently published state-of-the-art while being over 30 times faster. (M. Havaei). 1 http://www.ucancer.org impact for improved diagnostics, growth rate prediction and treatment planning. While some tumors such as meningiomas can be easily segmented, others like gliomas and glioblastomas are much more difficult to localize. These tumors (together with their surrounding edema) are often diffused, poorly contrasted, and extend tentaclelike structures that make them difficult to segment. Another fundamental difficulty with segmenting brain tumors is that they can appear anywhere in the brain, in almost any shape and size. Furthermore, unlike images derived from X-ray computed tomography (CT) scans, the scale of voxel values in MR images is not standardized. Depending on the type of MR machine used (1.5, 3 or 7 tesla) and the acquisition protocol (field of view value, voxel resolution, gradient strength, b0 value, etc.), the same tumorous cells may end up having drastically different gray-scale values when pictured in different hospitals. Healthy brains are typically made of 3 types of tissues: the white matter, the gray matter, and the cerebrospinal fluid. The goal of brain tumor segmentation is to detect the location and
Havaei et al. REF used a tailored convolutional neural network to segment glioblastomas.
Brain tumor segmentation with Deep Neural Networks
Medical Image Analysis
Computer Science Medicine