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.
Highlights
According to the World Health Organization (WHO), more than 12 million people die of heart disease each year [1]
Because the cardiac organ function is directly related to the cardiovascular function, studying the characteristics of cardiomyocytes is one of the fundamental approaches to studying heart diseases and cardiovascular diseases
In studying cardiomyocytes during medical cell research, one crucial step is to isolate and observe cardiomyocytes because the cardiomyocytes isolated from adult animals carry many characteristics representing disease-relevant models that are not available in cells from other developmental stages
Summary
According to the World Health Organization (WHO), more than 12 million people die of heart disease each year [1]. We employ the hesitant fuzzy set theory to assign the specific classification attributes and confidence intervals of adult rat cardiomyocytes, enabling an automatic clustering of cardiomyocytes that allows researchers to quickly understand the state of cardiac cells in the culture. This kind of cell clustering based on hesitant fuzzy sets is applicable to the statistical changes of cell morphology in the culture process of cardiac muscle cells, and potentially useful for the clustering of other types of cells whose shape are arbitrary.
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