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.