  In this paper, we focus on three problems in deep learning based medical
image segmentation. Firstly, U-net, as a popular model for medical image
segmentation, is difficult to train when convolutional layers increase even
though a deeper network usually has a better generalization ability because of
more learnable parameters. Secondly, the exponential ReLU (ELU), as an
alternative of ReLU, is not much different from ReLU when the network of
interest gets deep. Thirdly, the Dice loss, as one of the pervasive loss
functions for medical image segmentation, is not effective when the prediction
is close to ground truth and will cause oscillation during training. To address
the aforementioned three problems, we propose and validate a deeper network
that can fit medical image datasets that are usually small in the sample size.
Meanwhile, we propose a new loss function to accelerate the learning process
and a combination of different activation functions to improve the network
performance. Our experimental results suggest that our network is comparable or
superior to state-of-the-art methods.
