Publications/xu.17.icip

Authors

Yongchao Xu, Thierry Géraud, Isabelle Bloch

Journal

Submitted

Type

inproceedings

Keywords

Image

Date

2017-01-25

Abstract

Brain magnetic resonance imaging (MRI) is widely used to assess

 brain developments in neonates and to diagnose a wide range of
 neurological diseases in adults. Such studies are usually based on
 quantitative analysis of different brain tissues, so it is essential
 to be able to classify accurately these tissues.  In this paper, we
 propose a fast automatic method that segments 3D brain MR images
 into different tissues using fully convolutional network (FCN) and
 transfer learning.  As compared to existing deep learning-based
 approaches that rely either on 2D patches or on fully 3D FCN, our
 method is way much faster: it only takes a few seconds, and only a
 single modality (T1 or T2) is required.  In order to take the 3D
 information into account, the set of all 3 successive 2D slices are
 stacked to form a set of 2D color images, which serve as input for
 the FCN pre-trained on ImageNet for natural image classification.
 To the best of our knowledge, this is the first method that attempts
 to apply transfer learning for segmenting both neonatal and adult
 brain 3D MR images, and we show that the proposed method achieves
 state-of-the-art results on two public datasets.

Bibtex (lrde.bib)

@Article{	  xu.17.icip,
  author	= {Yongchao Xu and Thierry G\'eraud and Isabelle Bloch},
  title		= {From Neonatal to Adult Brain MR Image Segmentation in a
  Few Seconds Using 3D-Like Fully Convolutional Network and Transfer
  Learning},
  journal	= {Submitted for publication},
  year		= 2017,
  project	= {Image},
  abstract	= {Brain magnetic resonance imaging (MRI) is widely used to assess
  brain developments in neonates and to diagnose a wide range of
  neurological diseases in adults. Such studies are usually based on
  quantitative analysis of different brain tissues, so it is essential
  to be able to classify accurately these tissues.  In this paper, we
  propose a fast automatic method that segments 3D brain MR images
  into different tissues using fully convolutional network (FCN) and
  transfer learning.  As compared to existing deep learning-based
  approaches that rely either on 2D patches or on fully 3D FCN, our
  method is way much faster: it only takes a few seconds, and only a
  single modality (T1 or T2) is required.  In order to take the 3D
  information into account, the set of all 3 successive 2D slices are
  stacked to form a set of 2D color images, which serve as input for
  the FCN pre-trained on ImageNet for natural image classification.
  To the best of our knowledge, this is the first method that attempts
  to apply transfer learning for segmenting both neonatal and adult
  brain 3D MR images, and we show that the proposed method achieves
  state-of-the-art results on two public datasets.},
  note		= {Submitted},
  optlrdepaper	= {https://www.lrde.epita.fr/dload/papers/xu.2017.icip.pdf}
		  
}