Publications/puybareau.18.brainles

Authors

Élodie Puybareau, Guillaume Tochon, Joseph Chazalon, Jonathan Fabrizio

Where

Proceedings of the Workshop on Brain Lesions (BrainLes)in conjunction with MICCAI

Type

inproceedings

Publisher

Springer

Projects

Olena

Keywords

Image

Date

2018-11-05

Abstract

In this paper, we propose a fast automatic method that seg- ments glioma without any manual assistance, using a fully convolutional network (FCN) and transfer learning. From this segmentation, we predict the patient overall survival using only the results of the segmentation and a home made atlas. The FCN is the base network of VGG-16pretrained on ImageNet for natural image classificationand fine tuned with the training dataset of the MICCAI 2018 BraTS Challenge. It relies on the "pseudo-3D" method published at ICIP 2017, which allows for segmenting objects from 2D color images which contain 3D information of MRI volumes. For each n th slice of the volume to segment, we consider three images, corresponding to the (n-1)th, nthand (n-1)th slices of the original volume. These three gray-level 2D images are assembled to form a 2D RGB color image (one image per channel). This image is the input of the FCN to obtain a 2D segmentation of the n th slice. We process all slices, then stack the results to form the 3D output segmentation. With such a technique, the segmentation of a 3D volume takes only a few seconds. The prediction is based on Random Forests, and has the advantage of not being dependant of the acquisition modality, making it robust to inter-base data.

Documents

• Paper

Bibtex (lrde.bib)

@InProceedings{	  puybareau.18.brainles,
  author	= {\'Elodie Puybareau and Guillaume Tochon and Joseph
		  Chazalon and Jonathan Fabrizio},
  title		= {Segmentation of Gliomas and Prediction of Patient Overall
		  Survival: {A} Simple and Fast Procedure},
  booktitle	= {Proceedings of the Workshop on Brain Lesions (BrainLes),
		  in conjunction with MICCAI},
  year		= 2018,
  series	= {Lecture Notes in Computer Science},
  volume	= {11384},
  pages		= {199--209},
  publisher	= {Springer},
  abstract	= {In this paper, we propose a fast automatic method that
		  seg- ments glioma without any manual assistance, using a
		  fully convolutional network (FCN) and transfer learning.
		  From this segmentation, we predict the patient overall
		  survival using only the results of the segmentation and a
		  home made atlas. The FCN is the base network of VGG-16,
		  pretrained on ImageNet for natural image classification,
		  and fine tuned with the training dataset of the MICCAI 2018
		  BraTS Challenge. It relies on the "pseudo-3D" method
		  published at ICIP 2017, which allows for segmenting objects
		  from 2D color images which contain 3D information of MRI
		  volumes. For each n th slice of the volume to segment, we
		  consider three images, corresponding to the (n-1)th, nth,
		  and (n-1)th slices of the original volume. These three
		  gray-level 2D images are assembled to form a 2D RGB color
		  image (one image per channel). This image is the input of
		  the FCN to obtain a 2D segmentation of the n th slice. We
		  process all slices, then stack the results to form the 3D
		  output segmentation. With such a technique, the
		  segmentation of a 3D volume takes only a few seconds. The
		  prediction is based on Random Forests, and has the
		  advantage of not being dependant of the acquisition
		  modality, making it robust to inter-base data.},
  doi		= {10.1007/978-3-030-11726-9_18}
}