  Recent advances of semantic image segmentation greatly benefit from deeper
and larger Convolutional Neural Network (CNN) models. Compared to image
segmentation in the wild, properties of both medical images themselves and of
existing medical datasets hinder training deeper and larger models because of
overfitting. To this end, we propose a novel two-stream UNET architecture for
automatic end-to-end medical image segmentation, in which intensity value and
gradient vector flow (GVF) are two inputs for each stream, respectively. We
demonstrate that two-stream CNNs with more low-level features greatly benefit
semantic segmentation for imperfect medical image datasets. Our proposed
two-stream networks are trained and evaluated on the popular medical image
segmentation benchmarks, and the results are competitive with the state of the
art. The code will be released soon.
