Graphical-Model Framework for Automated Annotation of Cell Identities in Dense Cellular Images

Author:

Chaudhary ShiveshORCID,Lee Sol Ah,Li Yueyi,Patel Dhaval S.ORCID,Lu HangORCID

Abstract

AbstractAlthough 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.

Publisher

Cold Spring Harbor Laboratory

Cited by 1 articles. 订阅此论文施引文献 订阅此论文施引文献,注册后可以免费订阅5篇论文的施引文献,订阅后可以查看论文全部施引文献

1. Probabilistic Joint Segmentation and Labeling of C. elegans Neurons;Medical Image Computing and Computer Assisted Intervention – MICCAI 2020;2020

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