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  3. Detection and Classification of Local Ca²⁺ Release Events in Cardiomyocytes Using 3D-UNet Neural Network
 

Detection and Classification of Local Ca²⁺ Release Events in Cardiomyocytes Using 3D-UNet Neural Network

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BORIS DOI
10.48350/170201
Date of Publication
May 2022
Publication Type
Conference Paper
Division/Institute

Institut für Physiolo...

ARTORG Center - Artif...

Author
Dotti, Prisca Rosaorcid-logo
Institut für Physiologie
ARTORG Center - Artificial Intelligence in Medical Image Computing
Márquez Neila, Pablo
ARTORG Center - Artificial Intelligence in Medical Image Computing
Fernandez Tenorio, Miguel
Institut für Physiologie
Janicek, Radoslav
Institut für Physiologie
Wullschleger, Marcelorcid-logo
Institut für Physiologie
Meyer zu Westram, Till
ARTORG Center - Artificial Intelligence in Medical Image Computing
Sznitman, Raphaelorcid-logo
ARTORG Center - Artificial Intelligence in Medical Image Computing
Egger, Marcel
Institut für Physiologie
Subject(s)

600 - Technology::610...

Language
English
Description
Global Ca²⁺ increase in the cytosol of cardiomyocytes is crucial for the contraction of the heart. Malfunctioning of proteins involved in this process can trigger local events (e.g., sparks and puffs) and global events (e.g., waves). These are thought to be involved in the development of arrhythmia. Therefore, it is important to detect and classify local Ca²⁺ release events. We present a novel approach, based on a 3D U‐Net architecture, to perform these tasks in a fully automated fashion. We employed data obtained with fast xyt confocal imaging of cardiomyocytes where such subcellular Ca²⁺ events are manually annotated and trained the neural network to infer comparable segmentation as output. Despite the relatively small amount of available data and the challenges that it exhibits, we obtained qualitatively promising results.
Handle
https://boris-portal.unibe.ch/handle/20.500.12422/85228
Project(s)
Bern Data Science Day 2022-05-06
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poster_BDSD22_dotti_V4.pdfimageAdobe PDF1.29 MBotherOpen
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