Using Deep Learning to Identify Cell and Particle in Live-Cell Time-lapse Images

Hui Jun Cheng, Chun Yuan Lin, Cheng Xian Wu, Che Lun Hung, Wei Hsiang Chen, Chuan Yi Tang

研究成果: Conference contribution同行評審

1 引文 斯高帕斯(Scopus)

摘要

Live-cell time-lapse images generated by biological experiments are useful for observing activities, even for proposing novel hypotheses. In past work, we had proposed a particle-cell relation mining method, abbreviate to PCRM, which involved identifying particles and cells as objects from live-cell time-lapse images at first. Then PCRM is used to track the pathways of particles to calculate the measures as distances between the particles and cells. Finally, the relationship of particles and cells can be quantified by PCRM. The PCRM is useful for biologists to prove their hypotheses. However,it is very time-consuming when identifying the objects among a large number of biological images. Hence, in this paper, we propose a method using deep learning technology, abbreviated to PCOD, to accelerate the particle and cell identification. The PCOD method achieves the accuracies of 90.2% and 99.9% for particles and cells identification, respectively. In this way, the overall particles and cells can be identified in real time.

原文English
主出版物標題Proceedings - 2018 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2018
編輯Harald Schmidt, David Griol, Haiying Wang, Jan Baumbach, Huiru Zheng, Zoraida Callejas, Xiaohua Hu, Julie Dickerson, Le Zhang
發行者Institute of Electrical and Electronics Engineers Inc.
頁面1327-1331
頁數5
ISBN(電子)9781538654880
DOIs
出版狀態Published - 21 1月 2019
事件2018 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2018 - Madrid, Spain
持續時間: 3 12月 20186 12月 2018

出版系列

名字Proceedings - 2018 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2018

Conference

Conference2018 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2018
國家/地區Spain
城市Madrid
期間3/12/186/12/18

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