@article{ZHANG2021107885,
title = {LCU-Net: A novel low-cost U-Net for environmental microorganism image segmentation},
journal = {Pattern Recognition},
volume = {115},
pages = {107885},
year = {2021},
issn = {0031-3203},
doi = {https://doi.org/10.1016/j.patcog.2021.107885},
url = {https://www.sciencedirect.com/science/article/pii/S0031320321000728},
author = {Jinghua Zhang and Chen Li and Sergey Kosov and Marcin Grzegorzek and Kimiaki Shirahama and Tao Jiang and Changhao Sun and Zihan Li and Hong Li},
keywords = {Environmental miroorganisms, Image segmentation, Deep convolutional neural networks, Low-cost},
abstract = {In this paper, we propose a novel Low-cost U-Net (LCU-Net) for the Environmental Microorganism (EM) image segmentation task to assist microbiologists in detecting and identifying EMs more effectively. The LCU-Net is an improved Convolutional Neural Network (CNN) based on U-Net, Inception, and concatenate operations. It addresses the limitation of single receptive field setting and the relatively high memory cost of U-Net. Experimental results show the effectiveness and potential of the proposed LCU-Net in the practical EM image segmentation field.}
}