Abstract

Background

Algorithmic cellular segmentation is an essential step for the quantitative analysis of highly multiplexed tissue images. Current segmentation pipelines often require manual dataset annotation and additional training, significant parameter tuning, or a sophisticated understanding of programming to adapt the software to the researcher’s need. Here, we present CellSeg, an open-source, pre-trained nucleus segmentation and signal quantification software based on the Mask region-convolutional neural network (R-CNN) architecture. CellSeg is accessible to users with a wide range of programming skills.

Results

CellSeg performs at the level of top segmentation algorithms in the 2018 Kaggle Data Challenge both qualitatively and quantitatively and generalizes well to a diverse set of multiplexed imaged cancer tissues compared to established state-of-the-art segmentation algorithms. Automated segmentation post-processing steps in the CellSeg pipeline improve the resolution of immune cell populations for downstream single-cell analysis. Finally, an application of CellSeg to a highly multiplexed colorectal cancer dataset acquired on the CO-Detection by indEXing (CODEX) platform demonstrates that CellSeg can be integrated into a multiplexed tissue imaging pipeline and lead to accurate identification of validated cell populations.

Conclusion

CellSeg is a robust cell segmentation software for analyzing highly multiplexed tissue images, accessible to biology researchers of any programming skill level.

Details

Title
CellSeg: a robust, pre-trained nucleus segmentation and pixel quantification software for highly multiplexed fluorescence images
Author
Lee, Michael Y; Bedia, Jacob S  VIAFID ORCID Logo  ; Bhate, Salil S  VIAFID ORCID Logo  ; Barlow, Graham L  VIAFID ORCID Logo  ; Phillips, Darci  VIAFID ORCID Logo  ; Fantl, Wendy J  VIAFID ORCID Logo  ; Nolan, Garry P  VIAFID ORCID Logo  ; Schürch, Christian M  VIAFID ORCID Logo 
Pages
1-17
Section
Software
Publication year
2022
Publication date
2022
Publisher
BioMed Central
e-ISSN
14712105
Source type
Scholarly Journal
Language of publication
English
ProQuest document ID
2620938910
Copyright
© 2022. This work is licensed under http://creativecommons.org/licenses/by/4.0/ (the “License”). Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License.