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

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.

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https://hal.telecom-paristech.fr/hal-02287735
Contributor : Telecomparis Hal <>
Submitted on : Friday, September 13, 2019 - 5:17:46 PM
Last modification on : Thursday, October 17, 2019 - 12:37:00 PM

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  • HAL Id : hal-02287735, version 1

Citation

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⟩

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