TY - JOUR TI - Graphical-model framework for automated annotation of cell identities in dense cellular images AU - Chaudhary, Shivesh AU - Lee, Sol Ah AU - Li, Yueyi AU - Patel, Dhaval S AU - Lu, Hang A2 - Calabrese, Ronald L VL - 10 PY - 2021 DA - 2021/02/24 SP - e60321 C1 - eLife 2021;10:e60321 DO - 10.7554/eLife.60321 UR - https://doi.org/10.7554/eLife.60321 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 Caenorhabditis 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. KW - cell annotation KW - automation KW - probabilistic graphical model KW - whole-brain JF - eLife SN - 2050-084X PB - eLife Sciences Publications, Ltd ER -