Stem cell detection based on Convolutional Neural Network via third harmonic generation microscopy images

Gwo Giun Lee, Kuan Wei Haung, Chi Kuang Sun, Yi Hua Liao

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

Stem cell plays an important role in repairing destroyed tissues and keeping human healthy every day; and thus stem cell observation and detection are principle procedures before being analyzed by physicians. In this paper, we proposed a criterion of Computer-Aided Diagnosis (CAD) system to detect stem cells in the stratum basale based on cell segmentation algorithm and intrinsic characteristics of cells, which can provide consistent and accurate results for assisting the assessment of diagnosis. In addition, we utilize Convolutional Neural Networks (CNNs) to recognize basal cells and stem cells since CNN has excellent performance on processing abundant data. Actually, the procedure of acquiring biomedical images is too complicated to collect, hence hand-crafted initialization is adopted to overcome the issue of the lack of training data according to prior knowledge or the suggestion from medical doctors. The experimental results show that the accuracy of hand-crafted initialization is higher than random distribution kernels and the convergence time is shorter also since a better initial condition may lead to better results in optimization theory.

Original languageEnglish
Title of host publicationProceedings of the 2017 International Conference on Orange Technologies, ICOT 2017
EditorsLei Wang, Minghui Dong, Yanfeng Lu, Haizhou Li
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages45-48
Number of pages4
ISBN (Electronic)9781538632758
DOIs
Publication statusPublished - 2018 Apr 10
Event5th International Conference on Orange Technologies, ICOT 2017 - Singapore, Singapore
Duration: 2017 Dec 82017 Dec 10

Publication series

NameProceedings of the 2017 International Conference on Orange Technologies, ICOT 2017
Volume2018-January

Other

Other5th International Conference on Orange Technologies, ICOT 2017
CountrySingapore
CitySingapore
Period17-12-0817-12-10

Fingerprint

third generation
stem cells
Harmonic generation
Stem cells
neural network
Microscopy
harmonic generations
Microscopic examination
Stem Cells
microscopy
Neural networks
social stratum
Hand
cells
physician
physicians
lack
strata
statistical distributions
knowledge

All Science Journal Classification (ASJC) codes

  • Health Informatics
  • Instrumentation
  • Computer Networks and Communications
  • Computer Science Applications
  • Human-Computer Interaction
  • Information Systems
  • Health(social science)

Cite this

Lee, G. G., Haung, K. W., Sun, C. K., & Liao, Y. H. (2018). Stem cell detection based on Convolutional Neural Network via third harmonic generation microscopy images. In L. Wang, M. Dong, Y. Lu, & H. Li (Eds.), Proceedings of the 2017 International Conference on Orange Technologies, ICOT 2017 (pp. 45-48). (Proceedings of the 2017 International Conference on Orange Technologies, ICOT 2017; Vol. 2018-January). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/ICOT.2017.8336085
Lee, Gwo Giun ; Haung, Kuan Wei ; Sun, Chi Kuang ; Liao, Yi Hua. / Stem cell detection based on Convolutional Neural Network via third harmonic generation microscopy images. Proceedings of the 2017 International Conference on Orange Technologies, ICOT 2017. editor / Lei Wang ; Minghui Dong ; Yanfeng Lu ; Haizhou Li. Institute of Electrical and Electronics Engineers Inc., 2018. pp. 45-48 (Proceedings of the 2017 International Conference on Orange Technologies, ICOT 2017).
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title = "Stem cell detection based on Convolutional Neural Network via third harmonic generation microscopy images",
abstract = "Stem cell plays an important role in repairing destroyed tissues and keeping human healthy every day; and thus stem cell observation and detection are principle procedures before being analyzed by physicians. In this paper, we proposed a criterion of Computer-Aided Diagnosis (CAD) system to detect stem cells in the stratum basale based on cell segmentation algorithm and intrinsic characteristics of cells, which can provide consistent and accurate results for assisting the assessment of diagnosis. In addition, we utilize Convolutional Neural Networks (CNNs) to recognize basal cells and stem cells since CNN has excellent performance on processing abundant data. Actually, the procedure of acquiring biomedical images is too complicated to collect, hence hand-crafted initialization is adopted to overcome the issue of the lack of training data according to prior knowledge or the suggestion from medical doctors. The experimental results show that the accuracy of hand-crafted initialization is higher than random distribution kernels and the convergence time is shorter also since a better initial condition may lead to better results in optimization theory.",
author = "Lee, {Gwo Giun} and Haung, {Kuan Wei} and Sun, {Chi Kuang} and Liao, {Yi Hua}",
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Lee, GG, Haung, KW, Sun, CK & Liao, YH 2018, Stem cell detection based on Convolutional Neural Network via third harmonic generation microscopy images. in L Wang, M Dong, Y Lu & H Li (eds), Proceedings of the 2017 International Conference on Orange Technologies, ICOT 2017. Proceedings of the 2017 International Conference on Orange Technologies, ICOT 2017, vol. 2018-January, Institute of Electrical and Electronics Engineers Inc., pp. 45-48, 5th International Conference on Orange Technologies, ICOT 2017, Singapore, Singapore, 17-12-08. https://doi.org/10.1109/ICOT.2017.8336085

Stem cell detection based on Convolutional Neural Network via third harmonic generation microscopy images. / Lee, Gwo Giun; Haung, Kuan Wei; Sun, Chi Kuang; Liao, Yi Hua.

Proceedings of the 2017 International Conference on Orange Technologies, ICOT 2017. ed. / Lei Wang; Minghui Dong; Yanfeng Lu; Haizhou Li. Institute of Electrical and Electronics Engineers Inc., 2018. p. 45-48 (Proceedings of the 2017 International Conference on Orange Technologies, ICOT 2017; Vol. 2018-January).

Research output: Chapter in Book/Report/Conference proceedingConference contribution

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N2 - Stem cell plays an important role in repairing destroyed tissues and keeping human healthy every day; and thus stem cell observation and detection are principle procedures before being analyzed by physicians. In this paper, we proposed a criterion of Computer-Aided Diagnosis (CAD) system to detect stem cells in the stratum basale based on cell segmentation algorithm and intrinsic characteristics of cells, which can provide consistent and accurate results for assisting the assessment of diagnosis. In addition, we utilize Convolutional Neural Networks (CNNs) to recognize basal cells and stem cells since CNN has excellent performance on processing abundant data. Actually, the procedure of acquiring biomedical images is too complicated to collect, hence hand-crafted initialization is adopted to overcome the issue of the lack of training data according to prior knowledge or the suggestion from medical doctors. The experimental results show that the accuracy of hand-crafted initialization is higher than random distribution kernels and the convergence time is shorter also since a better initial condition may lead to better results in optimization theory.

AB - Stem cell plays an important role in repairing destroyed tissues and keeping human healthy every day; and thus stem cell observation and detection are principle procedures before being analyzed by physicians. In this paper, we proposed a criterion of Computer-Aided Diagnosis (CAD) system to detect stem cells in the stratum basale based on cell segmentation algorithm and intrinsic characteristics of cells, which can provide consistent and accurate results for assisting the assessment of diagnosis. In addition, we utilize Convolutional Neural Networks (CNNs) to recognize basal cells and stem cells since CNN has excellent performance on processing abundant data. Actually, the procedure of acquiring biomedical images is too complicated to collect, hence hand-crafted initialization is adopted to overcome the issue of the lack of training data according to prior knowledge or the suggestion from medical doctors. The experimental results show that the accuracy of hand-crafted initialization is higher than random distribution kernels and the convergence time is shorter also since a better initial condition may lead to better results in optimization theory.

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Lee GG, Haung KW, Sun CK, Liao YH. Stem cell detection based on Convolutional Neural Network via third harmonic generation microscopy images. In Wang L, Dong M, Lu Y, Li H, editors, Proceedings of the 2017 International Conference on Orange Technologies, ICOT 2017. Institute of Electrical and Electronics Engineers Inc. 2018. p. 45-48. (Proceedings of the 2017 International Conference on Orange Technologies, ICOT 2017). https://doi.org/10.1109/ICOT.2017.8336085