  In this paper, we develop a two-stage neural network solution for the
challenging task of white-matter lesion segmentation. To cope with the vast
vari- ability in lesion sizes, we sample brain MR scans with patches at three
differ- ent dimensions and feed them into separate fully convolutional neural
networks (FCNs). In the second stage, we process large and small lesion
separately, and use ensemble-nets to combine the segmentation results generated
from the FCNs. A novel activation function is adopted in the ensemble-nets to
improve the segmen- tation accuracy measured by Dice Similarity Coefficient.
Experiments on MICCAI 2017 White Matter Hyperintensities (WMH) Segmentation
Challenge data demonstrate that our two-stage-multi-sized FCN approach, as well
as the new activation function, are effective in capturing white-matter lesions
in MR images.
