Local structure prediction with convolutional neural networks for multimodal brain tumor segmentation

Pavel Dvořák, Bjoern Menze

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

43 Scopus citations

Abstract

Most medical images feature a high similarity in the intensities of nearby pixels and a strong correlation of intensity profiles across different image modalities. One way of dealing with – and even exploiting – this correlation is the use of local image patches. In the same way, there is a high correlation between nearby labels in image annotation, a feature that has been used in the “local structure prediction” of local label patches. In the present study we test this local structure prediction approach for 3D segmentation tasks, systematically evaluating different parameters that are relevant for the dense annotation of anatomical structures. We choose convolutional neural network as learning algorithm, as it is known to be suited for dealing with correlation between features. We evaluate our approach on the public BRATS2014 data set with three multimodal segmentation tasks, being able to obtain state-of-the-art results for this brain tumor segmentation data set consisting of 254 multimodal volumes with computing time of only 13 s per volume.

Original languageEnglish
Title of host publicationMedical Computer Vision
Subtitle of host publicationAlgorithms for Big Data - International Workshop, MCV 2015 and Held in Conjunction with MICCAI 2015, Revised Selected Papers
EditorsMichael Kelm, Henning Müller, Bjoern Menze, Shaoting Zhang, Dimitris Metaxas, Georg Langs, Albert Montillo, Weidong Cai
PublisherSpringer Verlag
Pages59-71
Number of pages13
ISBN (Print)9783319420158
DOIs
StatePublished - 2016
EventInternational Workshop on Medical Image Computing for Computer Assisted Intervention, 2015 MICCAI - Germany, Germany
Duration: 9 Oct 20159 Oct 2015

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume9601 LNCS
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Conference

ConferenceInternational Workshop on Medical Image Computing for Computer Assisted Intervention, 2015 MICCAI
Country/TerritoryGermany
CityGermany
Period9/10/159/10/15

Keywords

  • Brain tumor
  • CNN
  • Clustering
  • Deep learning
  • Image segmentation
  • MRI
  • Patch
  • Structure
  • Structured prediction

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