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Cardiac arrhythmias are one of the most frequent causes of death worldwide. A popular biological model used to study arrhythmogenesis is the cultured cardiac cell monolayer, which provides a good trade-off between physiological relevance and experimental access. Excitation wave patterns are imaged using high-bandwidth detectors, producing large data sets that are typically analyzed manually. To make such analysis less time consuming and less subjective, we have designed and implemented a toolkit for segmentation and tracking of cardiac waves in optical mapping recordings. The toolkit is optimized for high-resolution detectors to accommodate the growing availability of inexpensive high-resolution detectors for life science imaging applications (e.g., scientific CMOS cameras). The software extracts key features of propagating waves, such as wavefront speed and entropy. The methods have been validated using synthetic data, and real-world examples are provided, showing a difference in conduction velocity between two different types of cardiac cell cultures.

Original publication




Journal article


Biophys J

Publication Date





1595 - 1599


Automation, Cells, Cultured, Coculture Techniques, Image Processing, Computer-Assisted, Myocardium, Neurons, Optical Imaging, Software