ArticleScientific reports2021
Training a deep learning model for single-cell segmentation without manual annotation.
Article in Scientific reports, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 11 papers.
What it found
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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.
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Who cites it
11 citing papers in PubMed.
- Cellular Morphometric Analysis (CellMorph)-a comprehensive imaging-based tool for quantifying cellular phenotype heterogeneity and dynamics across biological processes.The FEBS journal · 2026Article
- Transforming microfluidics for single-cell analysis with robotics and artificial intelligence.Lab on a chip · 2025Review
- Automated Segmentation and Morphometric Analysis of Thioflavin-S-Stained Amyloid Deposits in Alzheimer's Disease Brains and Age-Matched Controls Using Weakly Supervised Deep Learning.International journal of molecular sciences · 2025Article
- A self-supervised learning approach for high throughput and high content cell segmentation.Communications biology · 2025Article
- Phenomics Demonstrates Cytokines Additive Induction of Epithelial to Mesenchymal Transition.Journal of cellular physiology · 2025Article
- Deep learning unlocks label-free viability assessment of cancer spheroids in microfluidics.Lab on a chip · 2024Article
- Particle uptake in cancer cells can predict malignancy and drug resistance using machine learning.Science advances · 2024Article
- Three-dimensional topology-based analysis segments volumetric and spatiotemporal fluorescence microscopy.Biological imaging · 2024Article
- A deep learning-based pipeline for analyzing the influences of interfacial mechanochemical microenvironments on spheroid invasion using differential interference contrast microscopic images.Materials today. Bio · 2023Article
- Small hand-designed convolutional neural networks outperform transfer learning in automated cell shape detection in confluent tissues.PloS one · 2023Article
- Different approaches to Imaging Mass Cytometry data analysis.Bioinformatics advances · 2023Review
Corrections and comments
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Authors and funding
2 authors.
Funding
Abstract
Advances in the artificial neural network have made machine learning techniques increasingly more important in image analysis tasks. Recently, convolutional neural networks (CNN) have been applied to the problem of cell segmentation from microscopy images. However, previous methods used a supervised training paradigm in order to create an accurate segmentation model. This strategy requires a large amount of manually labeled cellular images, in which accurate segmentations at pixel level were produced by human operators. Generating training data is expensive and a major hindrance in the wider adoption of machine learning based methods for cell segmentation. Here we present an alternative strategy that trains CNNs without any human-labeled data. We show that our method is able to produce accurate segmentation models, and is applicable to both fluorescence and bright-field images, and requires little to no prior knowledge of the signal characteristics.
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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.