Brain tumor segmentation with Deep Neural Networks

• Published in: Medical image analysis Vol. 35; pp. 18 - 31
• Main Authors: Havaei, Mohammad, Davy, Axel, Warde-Farley, David, Biard, Antoine, Courville, Aaron, Bengio, Yoshua, Pal, Chris, Jodoin, Pierre-Marc, Larochelle, Hugo
• Format: Journal Article
• Language: English
• Published: Netherlands Elsevier B.V 01-01-2017; Elsevier BV
• Subjects: Artificial neural networks; Brain; Brain cancer; Brain diseases; Brain Neoplasms - diagnostic imaging; Brain tumor segmentation; Brain tumors; Cascaded convolutional neural networks; Computer vision; Convolutional neural networks; Deep neural networks; Glioblastoma - diagnostic imaging; Humans; Image processing; Image Processing, Computer-Assisted - methods; Image segmentation; Learning algorithms; Machine Learning; Magnetic resonance imaging; Magnetic Resonance Imaging - methods; Neural networks; Neural Networks (Computer); Tumors; Brain tumor segmentation; Deep neural networks; Convolutional neural networks; Cascaded convolutional neural networks
• ISSN: 1361-8415; 1361-8423
• DOI: 10.1016/j.media.2016.05.004

•A fast and accurate fully automatic method for brain tumor segmentation which is competitive both in terms of accuracy and speed compared to the state of the art.•The method is based on deep neural networks (DNN) and learns features that are specific to brain tumor segmentation.•We present a new DNN architecture which exploits both local features as well as more global contextual features simultaneously.•Using a GPU implementation and a convolutional output layer, the model is an order of magnitude faster than other state of the art methods.•Introduces a novel cascaded architecture that allows the system to more accurately model local label dependencies.
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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.