Search research articles

ABOUT JoVE

Overview Leadership Blog JoVE Help Center

AUTHORS

Publishing Process Editorial Board Scope & Policies Peer Review FAQ Submit

LIBRARIANS

Testimonials Subscriptions Access Resources Library Advisory Board FAQ

RESEARCH

JoVE Journal Methods Collections JoVE Encyclopedia of Experiments Archive

EDUCATION

JoVE Core JoVE Business JoVE Science Education JoVE Lab Manual Faculty Resource Center Faculty Site

Terms & Conditions of Use

Related Concept Videos

Protein Networks

Protein Networks

An organism can have thousands of different proteins, and these proteins must cooperate to ensure the health of an organism. Proteins bind to other proteins and form complexes to carry out their functions. Many proteins interact with multiple other proteins creating a complex network of protein interactions.
These interactions can be represented through maps depicting protein-protein interaction networks, represented as nodes and edges. Nodes are circles that are representative of a protein,...

Protein Networks

Protein Networks

Synthesis and Decomposition Reactions

Synthesis and Decomposition Reactions

Synthesis and decomposition are two types of redox reactions. Synthesis means to make something, whereas decomposition means to break something. The reactions are accompanied by chemical and energy changes.

Network Covalent Solids

Network Covalent Solids

Network covalent solids contain a three-dimensional network of covalently bonded atoms as found in the crystal structures of nonmetals like diamond, graphite, silicon, and some covalent compounds, such as silicon dioxide (sand) and silicon carbide (carborundum, the abrasive on sandpaper). Many minerals have networks of covalent bonds.
To break or to melt a covalent network solid, covalent bonds must be broken. Because covalent bonds are relatively strong, covalent network solids are typically...

Lagging Strand Synthesis

Lagging Strand Synthesis

During replication, the complementary strands in double-stranded DNA are synthesized at different rates. Replication first begins on the leading strand. Replication starts later, occurs more slowly, and proceeds discontinuously on the lagging strand.
There are several major differences between synthesis of the leading strand and synthesis of the lagging strand. 1) Leading strand synthesis happens in the direction of replication fork opening, whereas lagging strand synthesis happens in the...

Transfer RNA Synthesis

Transfer RNA Synthesis

One of the unique features of tRNA is the presence of modified bases. In some tRNAs, modified bases account for nearly 20% of the total bases in the molecule. Altogether, these unusual bases protect the tRNA from enzymatic degradation by RNases.
Each of these chemical modifications is carried by a specific enzyme, post-transcription. All of these enzymes have unique base and site-specificity. Methylation, the most common chemical modification, is carried by at least nine different enzymes, with...

You might also read

Related Articles

Articles linked to this work by shared authors, journal, and citation graph.

Sort by

Same author

A Functional Limosilactobacillus reuteri ZY18 Strain: Probiotic Properties and Associated Anti-inflammatory Responses Against ETEC K88 Infection.

Probiotics and antimicrobial proteins·2026

Same author

Erythrocytapheresis Improves Health-Related Quality of Life in High-Altitude Migrants with Chronic Mountain Sickness: A Single-Arm Before-After Trial at 4,000-4,500 m.

High altitude medicine & biology·2026

Same author

Investigating the Potential Pharmacological Mechanisms of Huangqi in Allergic Rhinitis Using Network Pharmacology and Molecular Docking Strategies.

Combinatorial chemistry & high throughput screening·2026

Same author

Publisher Correction: Efficacy and safety of praziquantel combined with artemisinin derivatives versus praziquantel monotherapy for schistosomiasis: a meta-analysis of randomized controlled trials.

Infection·2026

Same author

Vitamin D for multiple sclerosis as an adjuvant therapy: A network meta-analysis of randomized controlled trials.

Clinical nutrition (Edinburgh, Scotland)·2026

Same author

A Feasibility study of a two-step ADC-PSAD-GWR rule for stratifying prostate lesions prior to biopsy.

BMC medical imaging·2026

Search research articles

Related Experiment Video

Updated: Jan 24, 2026

Author Spotlight: Learning Systematic Bronchoscopy in a Simulation-Base Setting

Author Spotlight: Learning Systematic Bronchoscopy in a Simulation-Base Setting

Published on: June 23, 2023

Retinal image synthesis from multiple-landmarks input with generative adversarial networks.

Zekuan Yu¹, Qing Xiang², Jiahao Meng²

¹Department of Biomedical Engineering, College of Engineering, Peking University, Beijing, 100871, China.

Biomedical Engineering Online

|May 23, 2019

Summary

This summary is machine-generated.

Synthesizing realistic medical images using generative adversarial networks (GANs) is crucial for imbalanced datasets. The novel multiple-channels-multiple-landmarks (MCML) preprocessing pipeline significantly enhances synthetic fundus image quality for improved medical image analysis.

Keywords:

Generative adversarial networks Multiple landmarks Retinal image synthesis

More Related Videos

Generation of Shear Adhesion Map Using SynVivo Synthetic Microvascular Networks

Generation of Shear Adhesion Map Using SynVivo Synthetic Microvascular Networks

Published on: May 25, 2014

Using Retinal Imaging to Study Dementia

Using Retinal Imaging to Study Dementia

Published on: November 6, 2017

Related Experiment Videos

Last Updated: Jan 24, 2026

Author Spotlight: Learning Systematic Bronchoscopy in a Simulation-Base Setting

Author Spotlight: Learning Systematic Bronchoscopy in a Simulation-Base Setting

Published on: June 23, 2023

Generation of Shear Adhesion Map Using SynVivo Synthetic Microvascular Networks

Generation of Shear Adhesion Map Using SynVivo Synthetic Microvascular Networks

Published on: May 25, 2014

Using Retinal Imaging to Study Dementia

Using Retinal Imaging to Study Dementia

Published on: November 6, 2017

Area of Science:

Medical Imaging
Computer Vision
Artificial Intelligence

Background:

Medical image datasets are often imbalanced, hindering effective training for AI models.
Generative Adversarial Networks (GANs) are used to synthesize medical images, augmenting datasets for improved analysis.
Existing methods for synthesizing fundus images, like image-to-image translation, have limitations in detail and quality.

Purpose of the Study:

To improve the quality and detail of synthetic fundus images.
To introduce a novel preprocessing pipeline for synthesizing color fundus images.
To evaluate the effectiveness of the proposed pipeline and GAN architectures on public datasets.

Main Methods:

Proposed a new preprocessing pipeline: multiple-channels-multiple-landmarks (MCML).
Synthesized color fundus images using vessel tree, optic disc, and optic cup inputs.
Compared MCML with single vessel mask inputs across various Pix2pix and Cycle-GAN architectures.
Designed and evaluated a Pix2pix model with a ResU-net generator.

Main Results:

The MCML method outperformed single vessel-based methods across all tested GAN architectures.
The Pix2pix model with a ResU-net generator demonstrated superior Peak Signal-to-Noise Ratio (PSNR) and Structural Similarity Index Measure (SSIM) compared to other GANs.
Utilizing high-resolution paired images further improved the performance of all GANs.

Conclusions:

The MCML method is effective in generating high-quality, realistic fundus images.
The combination of MCML, a Pix2pix network with a ResU-net generator, and high-resolution images yields optimal results.
The proposed approach shows significant potential for applications in computer-aided diagnosis, particularly for glaucoma detection using fundus images.