ArticleCommunications biology2022
Self-supervised machine learning for live cell imagery segmentation.
Article in Communications biology, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 9 papers.
What it found
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Who cites it
9 citing papers in PubMed.
- Integrating Environmental Exposure Profiles with Temporal Transcriptomics: An Explainable Machine Learning Model for Cardiovascular Disease Risk Prediction and Biological Validation.Cardiovascular drugs and therapy · 2026Article
- Meeting the challenges of electronic health record (EHR) optimization.NPJ digital medicine · 2025Review
- Directing Cell Phenotype: Quantitative Single-Cell Migration Assay Leveraging Tunable Extracellular Surfaces.Langmuir : the ACS journal of surfaces and colloids · 2025Article
- A self-supervised learning approach for high throughput and high content cell segmentation.Communications biology · 2025Article
- Optical sorting: past, present and future.Light, science & applications · 2025Review
- Low-frequency ERK and Akt activity dynamics are predictive of stochastic cell division events.NPJ systems biology and applications · 2024Article
- BIDCell: Biologically-informed self-supervised learning for segmentation of subcellular spatial transcriptomics data.Nature communications · 2024Article
- Bridging live-cell imaging and next-generation cancer treatment.Nature reviews. Cancer · 2023Review
- Automated cell segmentation for reproducibility in bioimage analysis.Synthetic biology (Oxford, England) · 2023Article
Corrections and comments
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Authors and funding
4 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Segmenting single cells is a necessary process for extracting quantitative data from biological microscopy imagery. The past decade has seen the advent of machine learning (ML) methods to aid in this process, the overwhelming majority of which fall under supervised learning (SL) which requires vast libraries of pre-processed, human-annotated labels to train the ML algorithms. Such SL pre-processing is labor intensive, can introduce bias, varies between end-users, and has yet to be shown capable of robust models to be effectively utilized throughout the greater cell biology community. Here, to address this pre-processing problem, we offer a self-supervised learning (SSL) approach that utilizes cellular motion between consecutive images to self-train a ML classifier, enabling cell and background segmentation without the need for adjustable parameters or curated imagery. By leveraging motion, we achieve accurate segmentation that trains itself directly on end-user data, is independent of optical modality, outperforms contemporary SL methods, and does so in a completely automated fashion-thus eliminating end-user variability and bias. To the best of our knowledge, this SSL algorithm represents a first of its kind effort and has appealing features that make it an ideal segmentation tool candidate for the broader cell biology research community.
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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.