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.

中文翻译：

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LCU-Net：用于环境微生物图像分割的新型低成本U-Net

在本文中，我们为环境微生物（EM）图像分割任务提出了一种新颖的低成本U-Net（LCU-Net），以帮助微生物学家更有效地检测和识别EM。LCU-Net是基于U-Net，Inception和串联操作的改进的卷积神经网络（CNN）。它解决了单接收域设置的局限性以及U-Net相对较高的存储成本。实验结果表明，所提出的LCU-Net在实际的EM图像分割领域中具有有效性和潜力。