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

• Published in: Computational and structural biotechnology journal Vol. 20; pp. 1957 - 1966
• Main Authors: Zhang, Chi, Jiang, Hao, Liu, Weihuang, Li, Junyi, Tang, Shiming, Juhas, Mario, Zhang, Yang
• Format: Journal Article
• Language: English
• Published: Netherlands Elsevier B.V 01.01.2022; Research Network of Computational and Structural Biotechnology; Elsevier
• Subjects: Bright-field microscope; Confocal fluorescence microscope; CycleGAN; Deep learning; Leishmania parasite; Mammalian cell; Microscopic image; Out-of-focus correction; Microscopic image; Deep learning; Out-of-focus correction; Mammalian cell; Bright-field microscope; Leishmania parasite; Confocal fluorescence microscope; CycleGAN
• ISSN: 2001-0370; 2001-0370
• DOI: 10.1016/j.csbj.2022.04.003

[Display omitted] 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. 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 bright-field 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. Code and dataset are publicly available at: https://github.com/jiangdat/COMI.