RT Journal Article SR Electronic T1 Graphical-Model Framework for Automated Annotation of Cell Identities in Dense Cellular Images JF bioRxiv FD Cold Spring Harbor Laboratory SP 2020.03.10.986356 DO 10.1101/2020.03.10.986356 A1 Shivesh Chaudhary A1 Sol Ah Lee A1 Yueyi Li A1 Dhaval S. Patel A1 Hang Lu YR 2021 UL http://biorxiv.org/content/early/2021/02/19/2020.03.10.986356.abstract AB 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.Competing Interest StatementThe authors have declared no competing interest.