Please use this identifier to cite or link to this item:

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
Magnetic resonance imaging
Issue date2016
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
AccessRestricted access (Author)
Appears in Collections:DEI - Artigos em atas de congressos internacionais

Files in This Item:
File Description SizeFormat 
  Restricted access
2,65 MBAdobe PDFView/Open

Partilhe no FacebookPartilhe no TwitterPartilhe no DeliciousPartilhe no LinkedInPartilhe no DiggAdicionar ao Google BookmarksPartilhe no MySpacePartilhe no Orkut
Exporte no formato BibTex mendeley Exporte no formato Endnote Adicione ao seu ORCID