From Neonatal to Adult Brain MR Image Segmentation in a Few Seconds Using 3D-Like Fully Convolutional Network and Transfer Learning

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Revision as of 14:43, 25 January 2017 by Yongchao Xu (talk | contribs)

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}
		  
}