Detail publikace

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

DVOŘÁK, P. MENZE, B.

Originální název

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

Typ

článek v časopise ve Web of Science, Jimp

Jazyk

angličtina

Originální abstrakt

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 seconds per volume.

Klíčová slova

Brain Tumor, Clustering, CNN, Deep Learning, Image Segmentation, MRI, Patch, Structure, Structured Prediction.

Autoři

DVOŘÁK, P.; MENZE, B.

Rok RIV

2015

Vydáno

9. 10. 2015

ISSN

0302-9743

Periodikum

Lecture Notes in Computer Science

Ročník

8965

Číslo

1

Stát

Spolková republika Německo

Strany od

1

Strany do

12

Strany počet

12

BibTex

@article{BUT115707,
  author="Pavel {Dvořák} and Bjoern {Menze}",
  title="Local Structure Prediction with Convolutional Neural Networks for Multimodal Brain Tumor Segmentation",
  journal="Lecture Notes in Computer Science",
  year="2015",
  volume="8965",
  number="1",
  pages="1--12",
  doi="10.1007/978-3-319-42016-5\{_}6",
  issn="0302-9743"
}