  Nowadays U-net-like FCNs predominate various biomedical image segmentation
applications and attain promising performance, largely due to their elegant
architectures, e.g., symmetric contracting and expansive paths as well as
lateral skip-connections. It remains a research direction to devise novel
architectures to further benefit the segmentation. In this paper, we develop an
ACE-net that aims to enhance the feature representation and utilization by
augmenting the contracting and expansive paths. In particular, we augment the
paths by the recently proposed advanced techniques including ASPP, dense
connection and deep supervision mechanisms, and novel connections such as
directly connecting the raw image to the expansive side. With these
augmentations, ACE-net can utilize features from multiple sources, scales and
reception fields to segment while still maintains a relative simple
architecture. Experiments on two typical biomedical segmentation tasks validate
its effectiveness, where highly competitive results are obtained in both tasks
while ACE-net still runs fast at inference.
