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CRISP: correlation-refined image segmentation process
Jennifer K Briggs1, Erli Jin2, Matthew J Merrins2
1Department of Bioengineering, University of Colorado Anschutz Medical Campus, Aurora, USA. Jennifer.briggs@cuanschutz.edu.
BMC Bioinformatics
|May 26, 2025
Summary
We developed the Correlation-Refined Image Segmentation Process (CRISP) to improve cell segmentation in dense tissues. CRISP offers accurate cell masking, enhancing the reliability of calcium imaging analysis.
Area of Science:
- Cellular biology
- Bioimaging
- Computational biology
Background:
- Calcium imaging is vital for real-time cellular activity monitoring.
- Traditional segmentation methods fail in dense tissues like cardiomyocytes.
- Manual cell masking is prone to user error.
Purpose of the Study:
- To develop an automated and accurate cell segmentation method for dense tissues.
- To improve the reliability of calcium imaging analysis.
Main Methods:
- Developed the Correlation-Refined Image Segmentation Process (CRISP).
- CRISP uses interpixel correlations for cell mask refinement.
- CRISP offers automated semi-minor axis identification for cell masking.
Main Results:
- CRISP cell mask refinement achieved an AUC of 0.835.
- CRISP demonstrated 77% accuracy on independent datasets.
- Automated semi-minor axis identification reached an AUC of 0.989.
- CRISP significantly improved functional network analysis accuracy.
Conclusions:
- Accurate cell masking is crucial for valid scientific interpretation of calcium imaging data.
- CRISP provides transparent algorithms for precise cell segmentation in challenging tissues.
- These algorithms enhance accuracy and reproducibility in biological research.
