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.
@article{arxiv.2003.01581,
title = {BUSU-Net: An Ensemble U-Net Framework for Medical Image Segmentation},
author = {Wei Hao Khoong},
journal= {arXiv preprint arXiv:2003.01581},
year = {2020}
}
Comments
GitHub link to the model scripts and trained model weights can be found in the manuscript. Version 2: Added S-UNet's Mi-UNet results for comparison and reference