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
N1 - Publisher Copyright:
© 2018 IEEE.
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
UR - http://www.scopus.com/inward/record.url?scp=85062484224&partnerID=8YFLogxK
U2 - 10.1109/BIBM.2018.8621567
DO - 10.1109/BIBM.2018.8621567
M3 - Conference contribution
AN - SCOPUS:85062484224
T3 - Proceedings - 2018 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2018
SP - 1327
EP - 1331
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 -