Toward Automatic Cardiomyocyte Clustering and Counting through Hesitant Fuzzy Sets
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...
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doaj-eac10917339f40c38ded7d3d8a157e452020-11-24T22:11:20ZengMDPI AGApplied Sciences2076-34172019-07-01914287510.3390/app9142875app9142875Toward Automatic Cardiomyocyte Clustering and Counting through Hesitant Fuzzy SetsJiayao Wang0Olamide Timothy Tawose1Linhua Jiang2Dongfang Zhao3School of Optical-Electrical and Computer Engineering, University of Shanghai for Science and Technology, Shanghai 200093, ChinaDepartment of Computer Science and Engineering, University of Nevada, Reno, NV 89557, USASchool of Optical-Electrical and Computer Engineering, University of Shanghai for Science and Technology, Shanghai 200093, ChinaDepartment of Computer Science and Engineering, University of Nevada, Reno, NV 89557, USAThe 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.https://www.mdpi.com/2076-3417/9/14/2875cardiomyocytecell countingcell clusteringhesitant fuzzy set |
collection |
DOAJ |
language |
English |
format |
Article |
sources |
DOAJ |
author |
Jiayao Wang Olamide Timothy Tawose Linhua Jiang Dongfang Zhao |
spellingShingle |
Jiayao Wang Olamide Timothy Tawose Linhua Jiang Dongfang Zhao Toward Automatic Cardiomyocyte Clustering and Counting through Hesitant Fuzzy Sets Applied Sciences cardiomyocyte cell counting cell clustering hesitant fuzzy set |
author_facet |
Jiayao Wang Olamide Timothy Tawose Linhua Jiang Dongfang Zhao |
author_sort |
Jiayao Wang |
title |
Toward Automatic Cardiomyocyte Clustering and Counting through Hesitant Fuzzy Sets |
title_short |
Toward Automatic Cardiomyocyte Clustering and Counting through Hesitant Fuzzy Sets |
title_full |
Toward Automatic Cardiomyocyte Clustering and Counting through Hesitant Fuzzy Sets |
title_fullStr |
Toward Automatic Cardiomyocyte Clustering and Counting through Hesitant Fuzzy Sets |
title_full_unstemmed |
Toward Automatic Cardiomyocyte Clustering and Counting through Hesitant Fuzzy Sets |
title_sort |
toward automatic cardiomyocyte clustering and counting through hesitant fuzzy sets |
publisher |
MDPI AG |
series |
Applied Sciences |
issn |
2076-3417 |
publishDate |
2019-07-01 |
description |
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. |
topic |
cardiomyocyte cell counting cell clustering hesitant fuzzy set |
url |
https://www.mdpi.com/2076-3417/9/14/2875 |
work_keys_str_mv |
AT jiayaowang towardautomaticcardiomyocyteclusteringandcountingthroughhesitantfuzzysets AT olamidetimothytawose towardautomaticcardiomyocyteclusteringandcountingthroughhesitantfuzzysets AT linhuajiang towardautomaticcardiomyocyteclusteringandcountingthroughhesitantfuzzysets AT dongfangzhao towardautomaticcardiomyocyteclusteringandcountingthroughhesitantfuzzysets |
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1725806156261097472 |