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

BUSU-Net: An Ensemble U-Net Framework for Medical Image Segmentation