LCU-Net: A novel low-cost U-Net for environmental microorganism image segmentation

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 concat...

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Bibliographic Details
Published inPattern recognition Vol. 115; p. 107885
Main Authors Zhang, Jinghua, Li, Chen, Kosov, Sergey, Grzegorzek, Marcin, Shirahama, Kimiaki, Jiang, Tao, Sun, Changhao, Li, Zihan, Li, Hong
Format Journal Article
LanguageEnglish
Published Elsevier Ltd 01.07.2021
Subjects
Deep convolutional neural networks
Environmental miroorganisms
Image segmentation
Low-cost
Deep convolutional neural networks
Low-cost
Image segmentation
Environmental miroorganisms
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Summary: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.
ISSN:0031-3203
1873-5142
DOI:10.1016/j.patcog.2021.107885