White matter hyperintensities segmentation in a few seconds using fully convolutional network and transfer learning - Archive ouverte HAL Access content directly
Conference Papers Year : 2017

White matter hyperintensities segmentation in a few seconds using fully convolutional network and transfer learning

, , , (1, 2) ,
1
2
Yongchao Xu
  • Function : Author
  • PersonId : 766521
  • IdRef : 177844531
Thierry Géraud
Elodie Puybareau
  • Function : Author
Joseph Chazalon

Abstract

In this paper, we propose a fast automatic method that seg- ments white matter hyperintensities (WMH) in 3D brain MR images, using a fully convolutional network (FCN) and transfer learning. This FCN is VGG, pre-trained on ImageNet for natural image classification, and fine tuned with the training dataset of the MICCAI WMH Chal- lenge. We consider three images for each slice of volume to segment: the i-th T1 slice, the i-th FLAIR slice, and the residue of a morphological operator that emphasizes small bright structures. These three 2D images are assembled to form a 2D color image, that inputs the FCN to obtain the 2D segmentation of the i-th slice. We process all slices, and stack the results to form the 3D output segmentation. With such a technique, the segmentation of WMH on a 3D brain volume takes about 10 seconds. Our technique was ranked 6-th over 20 participants at the MICCAI WMH Challenge.
Not file

Dates and versions

hal-02287735 , version 1 (13-09-2019)

Identifiers

  • HAL Id : hal-02287735 , version 1

Cite

Yongchao Xu, Thierry Géraud, Elodie Puybareau, Isabelle Bloch, Joseph Chazalon. White matter hyperintensities segmentation in a few seconds using fully convolutional network and transfer learning. BrainLes MICCAI Workshop and WMH Challenge, 2017, Québec, Canada. ⟨hal-02287735⟩
62 View
0 Download

Share

Gmail Facebook Twitter LinkedIn More