Year: 2025
Type: article
Abstract: BACKGROUND: Calcium 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). RESULTS: CRISP 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. CONCLUSIONS: Inaccurate 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.
Source: BMC Bioinformatics
Language: English
Subfield: Biophysics
Domain: Life Sciences
Open Access status: gold
Estimated APC paid: $2,990
Awards: G1008305623, G1104028309, G1340380193, G3139468727, G3777195701, Graduate Research Fellowship Program (GRFP), Emergent Multi-Cellular Properties Regulating Pancreatic Islet Function, G9000028996, G9000126259, University of Colorado Anschutz Medical Campus DRC, Developing ultrasound contrast agents and signal processing for structural and functional imaging in type 1 diabetes, Mechanisms of islet compensation for β-cell mass deficiency in diabetes, G905512125
OpenAlex ID: W4410758689