CRISP: correlation-refined image segmentation process

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...

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Vydané v:BMC bioinformatics Ročník 26; číslo 1; s. 135 - 14
Hlavní autori: Briggs, Jennifer K., Jin, Erli, Merrins, Matthew J., Benninger, Richard K. P.
Médium: Journal Article
Jazyk:English
Vydavateľské údaje: London BioMed Central 26.05.2025
BioMed Central Ltd
Springer Nature B.V
BMC
Predmet:
Algorithms
Animals
Automated image segmentation
Automation
Bioinformatics
Biological activity
Biomedical and Life Sciences
Calcium
Calcium - metabolism
Calcium imaging
Calcium, Dietary
Cardiomyocytes
Cell masking
Cells
Computational Biology/Bioinformatics
Computational methods in microscopy image analysis
Computer Appl. in Life Sciences
Correlation
Datasets
Deep learning
Glucose
Image processing
Image Processing, Computer-Assisted - methods
Image segmentation
Laboratory animals
Life Sciences
Machine learning
Masking
Masks
Medical examination
Methods
Mice
Microarrays
Microscope and microscopy
Network analysis
Real time
Software
Time series
United States
Automated image segmentation
Calcium imaging
Correlation
Cell masking
ISSN:1471-2105, 1471-2105
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Shrnutí: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.
Bibliografia:ObjectType-Article-1
SourceType-Scholarly Journals-1
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ISSN:1471-2105
1471-2105
DOI:10.1186/s12859-025-06150-z