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| Cell Segmentation in Low Signal-to-Noise Ratio Microscopy Images Based on Machine Learning |
| YAN Yu-Xuan*, LU Jin-Wang*, SONG Yi-Hong, KUANG Xiao-Yu, TIAN Yuan, FU Jing-Yan** |
| State Key Laboratory of Agrobiotechnology, College of Biological Sciences, China Agricultural University, Beijing 100193, China |
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Abstract Fluorescence microscopy image plays an important role in the research of life sciences. A large number of image data can be processed effectively using computer and machine learning, so that we could obtain the conclusion with statistical significance. In this study, a cell segmentation model was proposed based on U-Net for low signal-to-noise ratio (SNR) fluorescence images, and a cell image dataset for training and validation was constructed. In this method, convolution kernels were adopted to extract features, residual modules to deepen network, and weighted loss function to make machine learning process pay more attention to cell edges. Compared to several other methods, this algorithm shows better performance in cell segmentation for low signal-to-noise ratio fluorescence images with 87.6% pixel accuracy and intersection-over-union (IOU) 72.0%. This study provides technical support for cell morphology research and image-based high-throughput cell screening.
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Received: 22 July 2020
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Corresponding Authors:
**jingyanfu@cau.edu.cn
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