ArticleiScience2024
Enhanced cell segmentation with limited training datasets using cycle generative adversarial networks.
Article in iScience, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
8 citing papers in PubMed.
- An AI-integrated organoid platform enables high-throughput functional evaluation of bioactive metal ions.Bioactive materials · 2026Article
- A Deep Learning Pipeline for Cell Segmentation and Viability Quantification in 3D Constructs From Fluorescence Microscopy Images.Microscopy research and technique · 2026Article
- A multi-modal graph-based framework for Alzheimer's disease detection.Scientific reports · 2025Article
- Enhanced cell tracking using a GAN-based super-resolution video-to-video time-lapse microscopy generative model.iScience · 2025Article
- Riemannian Manifolds for Biological Imaging Applications Based on Unsupervised Learning.Journal of imaging · 2025Article
- State-of-the-Art Deep Learning Methods for Microscopic Image Segmentation: Applications to Cells, Nuclei, and Tissues.Journal of imaging · 2024Review
- Single molecule dynamics in a virtual cell combining a 3-dimensional matrix model with random walks.Scientific reports · 2024Article
- Generative artificial intelligence in graduate medical education.Frontiers in medicine · 2024Review
Corrections and comments
- Update of
Authors and funding
4 authors.
Funding
Abstract
Deep learning is transforming bioimage analysis, but its application in single-cell segmentation is limited by the lack of large, diverse annotated datasets. We addressed this by introducing a CycleGAN-based architecture, cGAN-Seg, that enhances the training of cell segmentation models with limited annotated datasets. During training, cGAN-Seg generates annotated synthetic phase-contrast or fluorescent images with morphological details and nuances closely mimicking real images. This increases the variability seen by the segmentation model, enhancing the authenticity of synthetic samples and thereby improving predictive accuracy and generalization. Experimental results show that cGAN-Seg significantly improves the performance of widely used segmentation models over conventional training techniques. Our approach has the potential to accelerate the development of foundation models for microscopy image analysis, indicating its significance in advancing bioimage analysis with efficient training methodologies.
Indexed as
Identifiers
What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.