Please use this identifier to cite or link to this item: http://hdl.handle.net/1822/52002

TitleDeep convolutional neural networks for the segmentation of gliomas in multi-sequence MRI
Author(s)Pereira, Sérgio
Pinto, Adriano
Alves, Victor
Silva, Carlos A.
KeywordsBrain tumor
Deep convolutional neural network
Deep learning
Glioma
Magnetic resonance imaging
Segmentation
Issue date2016
PublisherSpringer-Verlag
JournalLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Abstract(s)In their most aggressive form, the mortality rate of gliomas is high. Accurate segmentation is important for surgery and treatment planning, as well as for follow-up evaluation. In this paper, we propose to segment brain tumors using a Deep Convolutional Neural Network. Neural Networks are known to suffer from overfitting. To address it, we use Dropout, Leaky Rectifier Linear Units and small convolutional kernels. To segment the High Grade Gliomas and Low Grade Gliomas we trained two different architectures, one for each grade. Using the proposed method it was possible to obtain promising results in the 2015 Multimodal Brain Tumor Segmentation (BraTS) data set, as well as the second position in the on-site challenge.
TypeConference paper
URIhttp://hdl.handle.net/1822/52002
ISBN9783319308579
DOI10.1007/978-3-319-30858-6_12
ISSN0302-9743
Peer-Reviewedyes
AccessRestricted access (Author)
Appears in Collections:DEI - Artigos em atas de congressos internacionais

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