  In recent years, convolutional neural networks (CNNs) have revolutionized
medical image analysis. One of the most well-known CNN architectures in
semantic segmentation is the U-net, which has achieved much success in several
medical image segmentation applications. Also more recently, with the rise of
autoML ad advancements in neural architecture search (NAS), methods like
NAS-Unet have been proposed for NAS in medical image segmentation. In this
paper, with inspiration from LadderNet, U-Net, autoML and NAS, we propose an
ensemble deep neural network with an underlying U-Net framework consisting of
bi-directional convolutional LSTMs and dense connections, where the first (from
left) U-Net-like network is deeper than the second (from left). We show that
this ensemble network outperforms recent state-of-the-art networks in several
evaluation metrics, and also evaluate a lightweight version of this ensemble
network, which also outperforms recent state-of-the-art networks in some
evaluation metrics.
