ArticleBMC bioinformatics2025
CRISP: correlation-refined image segmentation process.
Article in BMC bioinformatics, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.
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Who cites it
3 citing papers in PubMed.
- Noise Sources and Strategies for Signal Quality Improvement in Biological Imaging: A Review Focused on Calcium and Cell Membrane Voltage Imaging.Biosensors · 2026Review
- Glucokinase activity controls peripherally located subpopulations of β-cells that lead islet CaeLife · 2025Article
- Glucokinase activity controls peripherally-located subpopulations of β-cells that lead islet CabioRxiv : the preprint server for biology · 2024Article
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4 authors.
Funding
Abstract
backgroundCalcium imaging enables real-time recording of cellular activity across various biological contexts. To assess the activity of individual cells, researchers must segment images into the individual cells. While intensity-based threshold algorithms allow for automatic image segmentation in sparsely packed tissues, they perform poorly in densely packed organs such as cardiomyocytes or the pancreatic islet. To study these tissues, investigators typically manually outline the cells based on visual inspection. This manual cell masking introduces potential user error. To address this error, we developed the Correlation-Refined Image Segmentation Process (CRISP). CRISP utilizes interpixel correlations to refine user drawn cell masks (cell mask refinement) or automatically masks cells by identifying the largest circle that captures only pixels within the cell (semi-minor axis identification).
resultsCRISP cell mask refinement had an area under the receiver operating curve of 0.835, indicating good model performance on the training data set. CRISP had 77% accuracy when testing on a separate data set, which came from a different mouse model imaged with a different microscope than the training data set. CRISP cell mask refinement significantly improved the accuracy of functional network analysis compared to non-CRISP refined cell masks. CRISP automated semi-minor axis identification had an area under the receiver operating curve under the curve of 0.989, indicating strong model performance.
conclusionsInaccurate cell masking can result in inaccurate scientific interpretations of calcium images. Utilizing interpixel correlations, we developed two transparent algorithms that can be used for image segmentation in densely packed tissues. These algorithms may allow for more accurate and reproducible cell masking.
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