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Segmentation and quantification of white matter hyperintensities (WMHs) areof great importance in studying and understanding various neurological andgeriatric disorders. Although automatic methods have been proposed for WMHsegmentation on magnetic resonance imaging (MRI), manual corrections are oftennecessary to achieve clinically practical results. Major challenges for WMHsegmentation stem from their inhomogeneous MRI intensities, random location andsize distributions, and MRI noise. The presence of other brain anatomies ordiseases with enhanced intensities adds further difficulties. To cope withthese challenges, we present a specifically designed fully convolutional neuralnetwork (FCN) with residual connections to segment WMHs by using combined T1and fluid-attenuated inversion recovery (FLAIR) images. Our customized FCN isdesigned to be straightforward and generalizable, providing efficientend-to-end training due to its enhanced information propagation. We tested ourmethod on the open WMH Segmentation Challenge MICCAI2017 dataset, and, despiteour method's relative simplicity, results show that it performs amongst theleading techniques across five metrics. More importantly, our method achievesthe best score for hausdorff distance and average volume difference in testingdatasets from two MRI scanners that were not included in training,demonstrating better generalization ability of our proposed method over itscompetitors.

White matter hyperintensity segmentation from T1 and FLAIR images using fully convolutional neural networks enhanced with residual connections