Publication detail

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

DVOŘÁK, P. MENZE, B.

Original Title

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

English Title

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

Type

journal article in Web of Science

Language

en

Original 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 seconds per volume.

English 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 seconds per volume.

Keywords

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

RIV year

2015

Released

09.10.2015

Pages from

1

Pages to

12

Pages count

12

BibTex


@article{BUT115707,
  author="Pavel {Dvořák} and Bjoern {Menze}",
  title="Local Structure Prediction with Convolutional Neural Networks for Multimodal Brain Tumor Segmentation",
  annote="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.",
  booktitle="Proceedings MICCAI-MCV 2015",
  chapter="115707",
  doi="10.1007/978-3-319-42016-5_6",
  howpublished="online",
  number="1",
  volume="8965",
  year="2015",
  month="october",
  pages="1--12",
  type="journal article in Web of Science"
}