ArticleeLife2021
Graphical-model framework for automated annotation of cell identities in dense cellular images.
Article in eLife, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 19 papers.
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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.
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Who cites it
19 citing papers in PubMed, 44 citations in OpenAlex.
- Deep neural networks to register and annotate cells in moving and deforming nervous systems.eLife · 2026Article
- Unifying community whole-brain imaging datasets enables robust neuron identification and reveals determinants of neuron position in C. elegans.Cell reports methods · 2025Article
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- Unifying community-wide whole-brain imaging datasets enables robust automated neuron identification and reveals determinants of neuron positioning inbioRxiv : the preprint server for biology · 2024Article
- Automated segmentation and recognition of C. elegans whole-body cells.Bioinformatics (Oxford, England) · 2024Article
- Versatile multiple object tracking in sparse 2D/3D videos via deformable image registration.PLoS computational biology · 2024Article
- See Elegans: Simple-to-use, accurate, and automatic 3D detection of neural activity from densely packed neurons.PloS one · 2024Article
- Automated neuron tracking inside moving and deforming C. elegans using deep learning and targeted augmentation.Nature methods · 2024Article
- The fusion of microfluidics and artificial intelligence: a novel alliance for medical advancements.Bioanalysis · 2024Article
- Benzimidazoles cause lethality by inhibiting the function of Caenorhabditis elegans neuronal beta-tubulin.International journal for parasitology. Drugs and drug resistance · 2022Article
- Rapid detection and recognition of whole brain activity in a freely behaving Caenorhabditis elegans.PLoS computational biology · 2022Article
- Fast, efficient, and accurate neuro-imaging denoising via supervised deep learning.Nature communications · 2022Article
- Correcting motion induced fluorescence artifacts in two-channel neural imaging.PLoS computational biology · 2022Article
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- Toward a more accurate 3D atlas of C. elegans neurons.BMC bioinformatics · 2022Article
- Imaging whole-brain activity to understand behavior.Nature reviews. Physics · 2022Article
- Tracking calcium dynamics from individual neurons in behaving animals.PLoS computational biology · 2021Article
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Corrections and comments
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Authors and funding
5 authors at 1 institution in 1 country.
Funding
Abstract
Although identifying cell names in dense image stacks is critical in analyzing functional whole-brain data enabling comparison across experiments, unbiased identification is very difficult, and relies heavily on researchers' experiences. Here, we present a probabilistic-graphical-model framework, CRF_ID, based on Conditional Random Fields, for unbiased and automated cell identification. CRF_ID focuses on maximizing intrinsic similarity between shapes. Compared to existing methods, CRF_ID achieves higher accuracy on simulated and ground-truth experimental datasets, and better robustness against challenging noise conditions common in experimental data. CRF_ID can further boost accuracy by building atlases from annotated data in highly computationally efficient manner, and by easily adding new features (e.g. from new strains). We demonstrate cell annotation in
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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.