Graphical-model framework for automated annotation of cell identities in dense cellular images
- Georgia Institute of Technology, United States
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 C. elegans images across strains, animal orientations, and tasks including gene-expression localization, multi-cellular and whole-brain functional imaging experiments. Together, these successes demonstrate that unbiased cell annotation can facilitate biological discovery, and this approach may be valuable to annotation tasks for other systems.
Data availability
All data generated or analysed during this study are included in the manuscript and supporting files. Source data files are provided at https://github.com/shiveshc/CRF_Cell_ID.git.
Article and author information
Author details
Funding
National Institutes of Health (R21DC015652)
- Hang Lu
National Institutes of Health (R01NS096581)
- Hang Lu
National Institutes of Health (R01GM088333)
- Hang Lu
National Science Foundation (1764406)
- Hang Lu
National Science Foundation (1707401)
- Hang Lu
The funders had no role in study design, data collection and interpretation, or the decision to submit the work for publication.
Copyright
© 2021, Chaudhary et al.
This article is distributed under the terms of the Creative Commons Attribution License permitting unrestricted use and redistribution provided that the original author and source are credited.
Metrics
-
- 2,567
- views
-
- 312
- downloads
-
- 35
- citations
Views, downloads and citations are aggregated across all versions of this paper published by eLife.
Download links
A two-part list of links to download the article, or parts of the article, in various formats.
Downloads (link to download the article as PDF)
Open citations (links to open the citations from this article in various online reference manager services)
Cite this article (links to download the citations from this article in formats compatible with various reference manager tools)
Graphical-model framework for automated annotation of cell identities in dense cellular images
eLife 10:e60321.
https://doi.org/10.7554/eLife.60321
