Brain tumor segmentation with Deep Neural Networks.
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.
Université de Sherbrooke, Sherbrooke, Qc, Canada. Electronic address: seyed.mohammad.havaei@usherbrooke.ca.
Mohammad
M
Havaei
École Normale supérieure, Paris, France.
Axel
A
Davy
Université de Montréal, Montréal, Canada.
David
D
Warde-Farley
Université de Montréal, Montréal, Canada; École polytechnique, Palaiseau, France.
Antoine
A
Biard
Université de Montréal, Montréal, Canada.
Aaron
A
Courville
Université de Montréal, Montréal, Canada.
Yoshua
Y
Bengio
Université de Montréal, Montréal, Canada; École Polytechnique de Montréal, Canada.
Chris
C
Pal
Université de Sherbrooke, Sherbrooke, Qc, Canada.
Pierre-Marc
PM
Jodoin
Université de Sherbrooke, Sherbrooke, Qc, Canada.
Hugo
H
Larochelle
D001932:Brain Neoplasms / Q000000981:diagnostic imaging*; D005909:Glioblastoma / Q000000981:diagnostic imaging*; D006801:Humans; D007091:Image Processing, Computer-Assisted / Q000379:methods; D000069550:Machine Learning; D008279:Magnetic Resonance Imaging / Q000379:methods*; D016571:Neural Networks, Computer
Brain tumor segmentation; Cascaded convolutional neural networks; Convolutional neural networks; Deep neural networks