
High accuracy automated cell annotation in multi-cell imaging can expedite cell identification and reduce subjectivity in biological images.


coremsg

Cell identification is an important yet difficult process in data analysis of biological images. Previously, we developed an automated cell identification method called CRF_ID and demonstrated its high performance in C. elegans whole-brain images ([Chaudhary et al, 2021][1]). However, because the method was optimized for whole-brain imaging, comparable performance could not be guaranteed for application in commonly used C. elegans multi-cell images that display a subpopulation of cells. Here, we present an advance CRF\_ID 2.0 that expands the generalizability of the method to multi-cell imaging beyond whole-brain imaging. To illustrate the application of the advance, we show the characterization of CRF\_ID 2.0 in multi-cell imaging and cell-specific gene expression analysis in C. elegans . This work demonstrates that high accuracy automated cell annotation in multi-cell imaging can expedite cell identification and reduce its subjectivity in C. elegans and potentially other biological images of various origins.

### Competing Interest Statement

The authors have declared no competing interest.

 [1]: #ref-5

Automated cell annotation in multi-cell images using an improved CRF_ID algorithm

automated-cell-annotation-in-multi-cell-images-using-crf-id-algorithm


Automated Cell Annotation in Multi-Cell Images Using CRF_ID Algorithm
