  In this paper, we propose a new pre-training scheme for U-net based image
segmentation. We first train the encoding arm as a localization network to
predict the center of the target, before extending it into a U-net architecture
for segmentation. We apply our proposed method to the problem of segmenting the
optic disc from fundus photographs. Our work shows that the features learned by
encoding arm can be transferred to the segmentation network to reduce the
annotation burden. We propose that an approach could have broad utility for
medical image segmentation, and alleviate the burden of delineating complex
structures by pre-training on annotations that are much easier to acquire.
