4.7 Article

Correction of out-of-focus microscopic images by deep learning

Journal

COMPUTATIONAL AND STRUCTURAL BIOTECHNOLOGY JOURNAL
Volume 20, Issue -, Pages 1957-1966

Publisher

ELSEVIER
DOI: 10.1016/j.csbj.2022.04.003

Keywords

Microscopic image; Out-of-focus correction; Confocal fluorescence microscope; Bright-field microscope; Mammalian cell; Leishmania parasite; Deep learning; CycleGAN

Funding

  1. Shenzhen Science and Technology Program [20200821222112001]
  2. Guangdong Basic and Applied Basic Research Foundation [2021A1515220115]

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In this study, a model based on Cycle Generative Adversarial Network (CycleGAN) and a multi-component weighted loss function was developed to address the issue of out-of-focus microscopic images. The proposed model achieved state-of-the-art performance in deblurring and demonstrated excellent generalization capabilities.
Motivation: Microscopic images are widely used in basic biomedical research, disease diagnosis and medical discovery. Obtaining high-quality in-focus microscopy images has been a cornerstone of the microscopy. However, images obtained by microscopes are often out-of-focus, resulting in poor performance in research and diagnosis. Results: To solve the out-of-focus issue in microscopy, we developed a Cycle Generative Adversarial Network (CycleGAN) based model and a multi-component weighted loss function. We train and test our network in two self-collected datasets, namely Leishmania parasite dataset captured by a brightfield microscope, and bovine pulmonary artery endothelial cells (BPAEC) captured by a confocal fluorescence microscope. In comparison to other GAN-based deblurring methods, the proposed model reached state-of-the-art performance in correction. Another publicly available dataset, human cells dataset from the Broad Bioimage Benchmark Collection is used for evaluating the generalization abilities of the model. Our model showed excellent generalization capability, which could transfer to different types of microscopic image datasets.Availability and Implementation: Code and dataset are publicly available at: https://github.com/jiangdat/ COMI. (c) 2022 The Author(s). Published by Elsevier B.V. on behalf of Research Network of Computational and Structural Biotechnology. This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/).

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