{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,30]],"date-time":"2026-07-30T09:42:13Z","timestamp":1785404533443,"version":"3.56.0"},"reference-count":31,"publisher":"MDPI AG","issue":"7","license":[{"start":{"date-parts":[[2026,6,25]],"date-time":"2026-06-25T00:00:00Z","timestamp":1782345600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"crossref","award":["12204273"],"award-info":[{"award-number":["12204273"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"crossref"}]},{"award":["12204273"],"award-info":[{"award-number":["12204273"]}],"id":[{"id":"https:\/\/ror.org\/01h0zpd94","id-type":"ROR","asserted-by":"publisher"}]},{"name":"Natural Science Foundation of Shandong Province, China","award":["ZR2024MF107"],"award-info":[{"award-number":["ZR2024MF107"]}]},{"name":"Natural Science Foundation of Shandong Province, China","award":["ZR2021MH278"],"award-info":[{"award-number":["ZR2021MH278"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Bioengineering"],"abstract":"<jats:p>Beat-level synchrony among cardiomyocyte clusters is a critical indicator of cardiac electromechanical function. Traditional invasive approaches have substantial limitations, and conventional computer vision methods are poorly suited for resolving densely packed, adherent clusters. To address these challenges, we developed an analysis framework to characterize the beating characteristics of cardiomyocyte clusters from microscopic imaging data. Specifically, we propose CardioSegNet, a multi-task deep learning model that combines attention mechanisms with three prediction heads (semantic segmentation, contour detection, and distance transform), followed by a watershed algorithm to achieve high-accuracy cluster-level segmentation of cardiomyocyte clusters. The Pixel-Difference method is applied to extract time-series beating signals from each segmented cluster and compute several dynamic parameters, including beating amplitude, period, frequency, and the Beat Rate Irregularity (BRI). We further introduce PeriodAwareNAPTDij to quantify the beating synchrony among different clusters. Our experimental results show that CardioSegNet achieves a Dice coefficient of 0.8868 and an HD95 of 93.02 \u00b5m on an independent test set, demonstrating strong segmentation performance. The cardiomyocyte populations are not uniformly globally synchronized; rather, they consist of multiple local subgroups with high internal synchrony, and the degree of synchronization between clusters is positively correlated with their physical distance. This label-free analytical pipeline provides an efficient tool for myocardial function evaluation and cardiotoxicity screening in vitro.<\/jats:p>","DOI":"10.3390\/bioengineering13070742","type":"journal-article","created":{"date-parts":[[2026,6,30]],"date-time":"2026-06-30T05:58:04Z","timestamp":1782799084000},"page":"742","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["A Multi-Task Deep Learning Framework for Characterizing Beating Behavior and Synchrony in Cardiomyocyte Clusters"],"prefix":"10.3390","volume":"13","author":[{"given":"Tianxin","family":"Wang","sequence":"first","affiliation":[{"name":"School of Integrated Circuits, Shandong University, Jinan 250100, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Xinjie","family":"Liu","sequence":"additional","affiliation":[{"name":"School of Software, Shandong University, Jinan 250100, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Fangshuo","family":"Zhang","sequence":"additional","affiliation":[{"name":"School of Integrated Circuits, Shandong University, Jinan 250100, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Qianwen","family":"Guo","sequence":"additional","affiliation":[{"name":"State Key Laboratory for Innovation and Transformation of Luobing Theory, Key Laboratory of Cardiovascular Remodeling and Function Research of MOE, NHC, CAMS and Shandong Province, Department of Cardiology, Qilu Hospital of Shandong University, Jinan 250012, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Xiaoyu","family":"Li","sequence":"additional","affiliation":[{"name":"School of Clinical Medical Sciences, Clinical Medicine, Cheeloo College of Medicine, Shandong University, Jinan 250012, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yuanyuan","family":"Sun","sequence":"additional","affiliation":[{"name":"State Key Laboratory for Innovation and Transformation of Luobing Theory, Key Laboratory of Cardiovascular Remodeling and Function Research of MOE, NHC, CAMS and Shandong Province, Department of Cardiology, Qilu Hospital of Shandong University, Jinan 250012, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-0443-547X","authenticated-orcid":false,"given":"Jingjing","family":"Xu","sequence":"additional","affiliation":[{"name":"School of Integrated Circuits, Shandong University, Jinan 250100, China"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2026,6,25]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","unstructured":"Dhein, S., and Salameh, A. 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