Abstract
In this paper, we propose a fast automatic method that segments white matter hyperintensities (WMH) in 3D brain MR images, using a fully convolutional network (FCN) and transfer learning. This FCN is the Visual Geometry Group neural network (VGG for short) pre-trained on ImageNet for natural image classification, and fine tuned with the training dataset of the MICCAI WMH Challenge. We consider three images for each slice of the volume to segment: the T1 slice, the FLAIR slice, and the result 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 corresponding 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 s including pre-processing. Our technique was ranked 6-th over 20 participants at the MICCAI WMH Challenge.
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Acknowledgments
The authors want to thank the organizers of the White Matter Hyperintensities Segmentation Challenge at MICCAI 2017, and the reviewers for their valuable comments.
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Xu, Y., Géraud, T., Puybareau, É., Bloch, I., Chazalon, J. (2018). White Matter Hyperintensities Segmentation in a Few Seconds Using Fully Convolutional Network and Transfer Learning. In: Crimi, A., Bakas, S., Kuijf, H., Menze, B., Reyes, M. (eds) Brainlesion: Glioma, Multiple Sclerosis, Stroke and Traumatic Brain Injuries. BrainLes 2017. Lecture Notes in Computer Science(), vol 10670. Springer, Cham. https://doi.org/10.1007/978-3-319-75238-9_42
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