ArticleComputational and structural biotechnology journal2022
Correction of out-of-focus microscopic images by deep learning.
Article in Computational and structural biotechnology journal, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 12 papers.
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Who cites it
12 citing papers in PubMed.
- AI-empowered super-resolution microscopy: a revolution in nanoscale cellular imaging.Nature methods · 2026Review
- Artificial intelligence-powered microscopy: Transforming the landscape of parasitology.Journal of microscopy · 2026Review
- Multi-marker Similarity Enables Reduced-Reference and Interpretable Image Quality Assessment in Optical Microscopy.Research (Washington, D.C.) · 2025Article
- Synthetic data generation methods in healthcare: A review on open-source tools and methods.Computational and structural biotechnology journal · 2024Review
- Exploring Bioinformatics Solutions for Improved Leishmaniasis Diagnostic Tools: A Review.Molecules (Basel, Switzerland) · 2024Review
- Deep learning permits imaging of multiple structures with the same fluorophores.Biophysical journal · 2024Article
- Differentially localized protein identification for breast cancer based on deep learning in immunohistochemical images.Communications biology · 2024Article
- Nanoscale insights into hematology: super-resolved imaging on blood cell structure, function, and pathology.Journal of nanobiotechnology · 2024Review
- Enhancing scanning electron microscopy imaging quality of weakly conductive samples through unsupervised learning.Scientific reports · 2024Article
- Scoping Review of Deep Learning Techniques for Diagnosis, Drug Discovery, and Vaccine Development in Leishmaniasis.Transboundary and emerging diseases · 2024Article
- Decomposed Dissimilarity Measure for Evaluation of Digital Image Denoising.Sensors (Basel, Switzerland) · 2023Article
- Prediction of anticancer drug resistance using a 3D microfluidic bladder cancer model combined with convolutional neural network-based image analysis.Frontiers in bioengineering and biotechnology · 2023Article
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Authors and funding
7 authors.
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Abstract
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 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. Availability and Implementation: Code and dataset are publicly available at: https://github.com/jiangdat/COMI.
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