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Open AccessArticle

Toward Automatic Cardiomyocyte Clustering and Counting through Hesitant Fuzzy Sets

1
School of Optical-Electrical and Computer Engineering, University of Shanghai for Science and Technology, Shanghai 200093, China
2
Department of Computer Science and Engineering, University of Nevada, Reno, NV 89557, USA
3
Department of Computer Science, University of California, Davis, CA 95616, USA
*
Authors to whom correspondence should be addressed.
Appl. Sci. 2019, 9(14), 2875; https://doi.org/10.3390/app9142875
Received: 31 May 2019 / Revised: 10 July 2019 / Accepted: 14 July 2019 / Published: 18 July 2019
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Abstract

The isolation and observation of cardiomyocytes serve as the fundamental approach to cardiovascular research. The state-of-the-practice for the isolation and observation relies on manual operation of the entire culture process. Such a manual approach not only incurs high human errors, but also takes a long period of time. This paper proposes a new computer-aided paradigm to automatically, accurately, and efficiently perform the clustering and counting of cardiomyocytes, one of the key procedures for evaluating the success rate of cardiomyocytes isolation and the quality of culture medium. The key challenge of the proposed method lies in the unique, rod-like shape of cardiomyocytes, which has been hardly addressed in literature. Our proposed method employs a novel algorithm inspired by hesitant fuzzy sets and integrates an efficient implementation into the whole process of analyzing cardiomyocytes. The system, along with the data extracted from adult rats’ cardiomyocytes, has been experimentally evaluated with Matlab, showing promising results. The false accept rate (FAR) and the false reject rate (FRR) are as low as 1.46% and 1.97%, respectively. The accuracy rate is up to 98.7%—20% higher than the manual approach—and the processing time is reduced from tens of seconds to 3–5 s—an order of magnitude performance improvement. View Full-Text
Keywords: cardiomyocyte; cell counting; cell clustering; hesitant fuzzy set cardiomyocyte; cell counting; cell clustering; hesitant fuzzy set
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Wang, J.; Tawose, O.T.; Jiang, L.; Zhao, D. Toward Automatic Cardiomyocyte Clustering and Counting through Hesitant Fuzzy Sets. Appl. Sci. 2019, 9, 2875.

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