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

doi:10.1109/BIBM.2018.8621567

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

Department of Computer Science and Information Engineering

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

1 Scopus citations

Abstract

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.

Original language	English
Title of host publication	Proceedings - 2018 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2018
Editors	Harald Schmidt, David Griol, Haiying Wang, Jan Baumbach, Huiru Zheng, Zoraida Callejas, Xiaohua Hu, Julie Dickerson, Le Zhang
Publisher	Institute of Electrical and Electronics Engineers Inc.
Pages	1327-1331
Number of pages	5
ISBN (Electronic)	9781538654880
DOIs	https://doi.org/10.1109/BIBM.2018.8621567
State	Published - 21 01 2019
Event	2018 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2018 - Madrid, Spain Duration: 03 12 2018 → 06 12 2018

Publication series

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

Conference

Conference	2018 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2018
Country/Territory	Spain
City	Madrid
Period	03/12/18 → 06/12/18

Bibliographical note

Publisher Copyright:
© 2018 IEEE.

Keywords

CNN
Deep Learning
live-cell time-lapse image
particle and cell identification
real-time object identification

Access to Document

10.1109/BIBM.2018.8621567

Cite this

Cheng, H. J., Lin, C. Y., Wu, C. X., Hung, C. L., Chen, W. H., & Tang, C. Y. (2019). Using Deep Learning to Identify Cell and Particle in Live-Cell Time-lapse Images. In H. Schmidt, D. Griol, H. Wang, J. Baumbach, H. Zheng, Z. Callejas, X. Hu, J. Dickerson, & L. Zhang (Eds.), Proceedings - 2018 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2018 (pp. 1327-1331). Article 8621567 (Proceedings - 2018 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2018). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/BIBM.2018.8621567

Cheng, Hui Jun ; Lin, Chun Yuan ; Wu, Cheng Xian et al. / Using Deep Learning to Identify Cell and Particle in Live-Cell Time-lapse Images. Proceedings - 2018 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2018. editor / Harald Schmidt ; David Griol ; Haiying Wang ; Jan Baumbach ; Huiru Zheng ; Zoraida Callejas ; Xiaohua Hu ; Julie Dickerson ; Le Zhang. Institute of Electrical and Electronics Engineers Inc., 2019. pp. 1327-1331 (Proceedings - 2018 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2018).

@inproceedings{fa3a6ca4e779486282e1573a2cbd1167,

title = "Using Deep Learning to Identify Cell and Particle in Live-Cell Time-lapse Images",

abstract = "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.",

keywords = "CNN, Deep Learning, live-cell time-lapse image, particle and cell identification, real-time object identification",

author = "Cheng, \{Hui Jun\} and Lin, \{Chun Yuan\} and Wu, \{Cheng Xian\} and Hung, \{Che Lun\} and Chen, \{Wei Hsiang\} and Tang, \{Chuan Yi\}",

note = "Publisher Copyright: {\textcopyright} 2018 IEEE.; 2018 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2018 ; Conference date: 03-12-2018 Through 06-12-2018",

year = "2019",

month = jan,

day = "21",

doi = "10.1109/BIBM.2018.8621567",

language = "英语",

series = "Proceedings - 2018 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2018",

publisher = "Institute of Electrical and Electronics Engineers Inc.",

pages = "1327--1331",

editor = "Harald Schmidt and David Griol and Haiying Wang and Jan Baumbach and Huiru Zheng and Zoraida Callejas and Xiaohua Hu and Julie Dickerson and Le Zhang",

booktitle = "Proceedings - 2018 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2018",

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Cheng, HJ, Lin, CY, Wu, CX, Hung, CL, Chen, WH & Tang, CY 2019, Using Deep Learning to Identify Cell and Particle in Live-Cell Time-lapse Images. in H Schmidt, D Griol, H Wang, J Baumbach, H Zheng, Z Callejas, X Hu, J Dickerson & L Zhang (eds), Proceedings - 2018 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2018., 8621567, Proceedings - 2018 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2018, Institute of Electrical and Electronics Engineers Inc., pp. 1327-1331, 2018 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2018, Madrid, Spain, 03/12/18. https://doi.org/10.1109/BIBM.2018.8621567

Using Deep Learning to Identify Cell and Particle in Live-Cell Time-lapse Images. / Cheng, Hui Jun; Lin, Chun Yuan; Wu, Cheng Xian et al.
Proceedings - 2018 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2018. ed. / Harald Schmidt; David Griol; Haiying Wang; Jan Baumbach; Huiru Zheng; Zoraida Callejas; Xiaohua Hu; Julie Dickerson; Le Zhang. Institute of Electrical and Electronics Engineers Inc., 2019. p. 1327-1331 8621567 (Proceedings - 2018 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2018).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

TY - GEN

T1 - Using Deep Learning to Identify Cell and Particle in Live-Cell Time-lapse Images

AU - Cheng, Hui Jun

AU - Lin, Chun Yuan

AU - Wu, Cheng Xian

AU - Hung, Che Lun

AU - Chen, Wei Hsiang

AU - Tang, Chuan Yi

PY - 2019/1/21

Y1 - 2019/1/21

N2 - 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.

AB - 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.

KW - CNN

KW - Deep Learning

KW - live-cell time-lapse image

KW - particle and cell identification

KW - real-time object identification

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DO - 10.1109/BIBM.2018.8621567

M3 - 会议稿件

AN - SCOPUS:85062484224

T3 - Proceedings - 2018 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2018

SP - 1327

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

A2 - Schmidt, Harald

A2 - Griol, David

A2 - Wang, Haiying

A2 - Baumbach, Jan

A2 - Zheng, Huiru

A2 - Callejas, Zoraida

A2 - Hu, Xiaohua

A2 - Dickerson, Julie

A2 - Zhang, Le

PB - Institute of Electrical and Electronics Engineers Inc.

T2 - 2018 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2018

Y2 - 3 December 2018 through 6 December 2018

ER -

Cheng HJ, Lin CY, Wu CX, Hung CL, Chen WH, Tang CY. Using Deep Learning to Identify Cell and Particle in Live-Cell Time-lapse Images. In Schmidt H, Griol D, Wang H, Baumbach J, Zheng H, Callejas Z, Hu X, Dickerson J, Zhang L, editors, Proceedings - 2018 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2018. Institute of Electrical and Electronics Engineers Inc. 2019. p. 1327-1331. 8621567. (Proceedings - 2018 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2018). doi: 10.1109/BIBM.2018.8621567

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

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Keywords

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