A deep learning-based approach for efficient detection and classification of local Ca²⁺ release events in Full-Frame confocal imaging

Dotti, Prisca; Fernandez-Tenorio, Miguel; Janicek, Radoslav; Márquez-Neila, Pablo; Wullschleger, Marcel; Sznitman, Raphael; Egger, Marcel (2024). A deep learning-based approach for efficient detection and classification of local Ca²⁺ release events in Full-Frame confocal imaging. Cell calcium, 121 Elsevier 10.1016/j.ceca.2024.102893

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The release of Ca2+ ions from intracellular stores plays a crucial role in many cellular processes, acting as a secondary messenger in various cell types, including cardiomyocytes, smooth muscle cells, hepatocytes, and many others. Detecting and classifying associated local Ca2+ release events is particularly important, as these events provide insight into the mechanisms, interplay, and interdependencies of local Ca2+release events underlying global intracellular Ca2+signaling. However, time-consuming and labor-intensive procedures often complicate analysis, especially with low signal-to-noise ratio imaging data. Here, we present an innovative deep learning-based approach for automatically detecting and classifying local Ca2+ release events. This approach is exemplified with rapid full-frame confocal imaging data recorded in isolated cardiomyocytes. To demonstrate the robustness and accuracy of our method, we first use conventional evaluation methods by comparing the intersection between manual annotations and the segmentation of Ca2+ release events provided by the deep learning method, as well as the annotated and recognized instances of individual events. In addition to these methods, we compare the performance of the proposed model with the annotation of six experts in the field. Our model can recognize more than 75 % of the annotated Ca2+ release events and correctly classify more than 75 %. A key result was that there were no significant differences between the annotations produced by human experts and the result of the proposed deep learning model. We conclude that the proposed approach is a robust and time-saving alternative to conventional full-frame confocal imaging analysis of local intracellular Ca2+ events.

Item Type:

Journal Article (Original Article)

Division/Institute:

10 Strategic Research Centers > ARTORG Center for Biomedical Engineering Research > ARTORG Center - AI in Medical Imaging Laboratory
04 Faculty of Medicine > Pre-clinic Human Medicine > Institute of Physiology
10 Strategic Research Centers > ARTORG Center for Biomedical Engineering Research

Graduate School:

Graduate School for Cellular and Biomedical Sciences (GCB)

UniBE Contributor:

Dotti, Prisca Rosa, Fernandez Tenorio, Miguel, Janicek, Radoslav, Márquez Neila, Pablo, Wullschleger, Marcel, Sznitman, Raphael, Egger, Marcel

Subjects:

600 Technology > 610 Medicine & health
500 Science > 570 Life sciences; biology

ISSN:

0143-4160

Publisher:

Elsevier

Language:

English

Submitter:

Prisca Rosa Dotti

Date Deposited:

07 May 2024 07:39

Last Modified:

24 Jul 2024 22:14

Publisher DOI:

10.1016/j.ceca.2024.102893

PubMed ID:

38701707

Uncontrolled Keywords:

AI, Ca events, Ca puffs, Ca sparks, Deep learning, Full-Frame confocal imaging, Ventricular myocytes

BORIS DOI:

10.48350/196575

URI:

https://boris.unibe.ch/id/eprint/196575

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