CellSeg: a robust, pre-trained nucleus segmentation and pixel quantification software for highly multiplexed fluorescence images
Abstract
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.
Document Details
- Document Type
- Pub Defense Publication
- Publication Date
- Jan 18, 2022
- Source ID
- 10.1186/s12859-022-04570-9
Entities
People
- Christian Schürch
- Darci Phillips
- Garry P. Nolan
- Graham L. Barlow
- Jacob S. Bedia
- Michael Y. Lee
- Salil S. Bhate
- Wendy J. Fantl
Organizations
- Cancer Research Institute
- Cancer Research UK
- Food and Drug Administration
- Gates Foundation
- Kenneth Rainin Foundation
- National Institutes of Health
- Parker Institute for Cancer Immunotherapy
- Swiss National Science Foundation
- United States Department of Defense