Local Structure Prediction with Convolutional Neural Networks for Multimodal Brain Tumor Segmentation

Result code in IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F68081731%3A_____%2F16%3A00507004" target="_blank" >RIV/68081731:_____/16:00507004 - isvavai.cz</a>

Result on the web

<a href="http://dx.doi.org/10.1007/978-3-319-42016-5_6" target="_blank" >http://dx.doi.org/10.1007/978-3-319-42016-5_6</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.1007/978-3-319-42016-5_6" target="_blank" >10.1007/978-3-319-42016-5_6</a>

Result language

angličtina

Original language name

Local Structure Prediction with Convolutional Neural Networks for Multimodal Brain Tumor Segmentation

Original language description

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 predictiol´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 13s per volume

Czech name

—

Czech description

—

Type

D - Article in proceedings

CEP classification

—

OECD FORD branch

30210 - Clinical neurology

Project

Result was created during the realization of more than one project. More information in the Projects tab.

Continuities

P - Projekt vyzkumu a vyvoje financovany z verejnych zdroju (s odkazem do CEP)

Publication year

2016

Confidentiality

S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Article name in the collection

Lecture Notes in Computer Science

ISBN

978-3-319-42015-8

ISSN

0302-9743

e-ISSN

—

Number of pages

13

Pages from-to

59-71

Publisher name

Springer

Place of publication

Cham

Event location

Munich

Event date

Oct 9, 2016

Type of event by nationality

WRD - Celosvětová akce

UT code for WoS article

000389404000006