ArticlePLoS computational biology2020
DeLTA: Automated cell segmentation, tracking, and lineage reconstruction using deep learning.
Article in PLoS computational biology, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 92 papers.
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Who cites it
92 citing papers in PubMed.
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- The small GTPase Rab5 inhibits actin polymerization mediated by the Legionella pneumophila effector VipA.Medical microbiology and immunology · 2026Article
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- An automated cell-tracking pipeline for the analysis of neutrophil dynamics.Frontiers in bioinformatics · 2026Article
- Artificial intelligence in experimental and clinical in vitro analysis: applications, limitations, and future directions.Biotechnology notes (Amsterdam, Netherlands) · 2026Review
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- Coupling Bacterial Cell Size Regulation with Clonal Proliferation Dynamics Reveals Cell Division Based on Surface Area.bioRxiv : the preprint server for biology · 2025Article
- Evaluating the predictive power of combined gene expression dynamics from single cells on antibiotic survival.mSystems · 2025Article
- A self-supervised learning approach for high throughput and high content cell segmentation.Communications biology · 2025Article
- Unique growth and morphology properties of Clade 5bioRxiv : the preprint server for biology · 2025Article
- Unique growth and morphology properties of Clade 5 Clostridioides difficile strains revealed by single-cell time-lapse microscopy.PLoS pathogens · 2025Article
- Cell-TRACTR: A transformer-based model for end-to-end segmentation and tracking of cells.PLoS computational biology · 2025Article
- Colony pattern multistability emerges from a bistable switch.Proceedings of the National Academy of Sciences of the United States of America · 2025Article
- Resolving spatiotemporal dynamics in bacterial multicellular populations: approaches and challenges.Microbiology and molecular biology reviews : MMBR · 2025Review
- Deep learning image analysis for continuous single-cell imaging of dynamic processes in Plasmodium falciparum-infected erythrocytes.Communications biology · 2025Article
- A deep learning pipeline for three-dimensional brain-wide mapping of local neuronal ensembles in teravoxel light-sheet microscopy.Nature methods · 2025Article
32 more citing papers are in PubMed but not listed here.
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Authors and funding
3 authors.
Funding
Abstract
Microscopy image analysis is a major bottleneck in quantification of single-cell microscopy data, typically requiring human oversight and curation, which limit both accuracy and throughput. To address this, we developed a deep learning-based image analysis pipeline that performs segmentation, tracking, and lineage reconstruction. Our analysis focuses on time-lapse movies of Escherichia coli cells trapped in a "mother machine" microfluidic device, a scalable platform for long-term single-cell analysis that is widely used in the field. While deep learning has been applied to cell segmentation problems before, our approach is fundamentally innovative in that it also uses machine learning to perform cell tracking and lineage reconstruction. With this framework we are able to get high fidelity results (1% error rate), without human intervention. Further, the algorithm is fast, with complete analysis of a typical frame containing ~150 cells taking <700msec. The framework is not constrained to a particular experimental set up and has the potential to generalize to time-lapse images of other organisms or different experimental configurations. These advances open the door to a myriad of applications including real-time tracking of gene expression and high throughput analysis of strain libraries at single-cell resolution.
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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.