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Sensors and Materials
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Sensors and Materials, Volume 31, Number 6(3) (2019)
Copyright(C) MYU K.K.
pp. 2013-2028
S&M1912 Research Paper of Special Issue
https://doi.org/10.18494/SAM.2019.2406
Published: June 28, 2019

Segmentation of Activated Sludge Phase Contrast Microscopy Images Using U-Net Deep Learning Model [PDF]

Li-Jie Zhao, Shi-Da Zou, Yu-Hong Zhang, Ming-Zhong Huang, Yue Zuo, Jia Wang, Xing-Kui Lu, Zhi-Hao Wu, and Xiang-Yu Liu

(Received April 147, 2019; Accepted May 29, 2019)

Keywords: wastewater treatment, activated sludge, phase contrast microscopy, image segmentation, U-Net model

For the activated sludge wastewater treatment process, the image segmentation of flocs and filaments has become a crucial component in the successful implementation of a sludge volume index (SVI) sensor and the early fault detection of filamentous bulking. The segmentation of a phase contrast microscopy (PCM) image is a challenging problem because of the weak greyscale distinction between flocs and filaments, as well as the artifacts of halos and shadows. In this work, we proposed an automatic floc and filament segmentation method for PCM images using a U-Net deep learning structure with data augmentation. A loss function combining the binary cross entropy (BCE) function and Dice coefficient is proposed to improve the segmentation accuracy and sensitivity with unbalanced foreground and background samples. The performance of the segmentation algorithm is evaluated by the accuracy, precision, recall, F-measure, and intersection-over-union (IoU) metrics. Lab-scale experiments on the activated sludge process have been carried out to verify the proposed image segmentation method. Our proposed U-Net models with the combined loss function give better results than the U-Net models with BCE, fully convolutional network-VGG16 (FCN-VGG16), and a traditional segmentation method.

Corresponding author: Yu-Hong Zhang


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This work is licensed under a Creative Commons Attribution 4.0 International License.

Cite this article
Li-Jie Zhao, Shi-Da Zou, Yu-Hong Zhang, Ming-Zhong Huang, Yue Zuo, Jia Wang, Xing-Kui Lu, Zhi-Hao Wu, and Xiang-Yu Liu, Segmentation of Activated Sludge Phase Contrast Microscopy Images Using U-Net Deep Learning Model, Sens. Mater., Vol. 31, No. 6, 2019, p. 2013-2028.



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