Nuclei detection and segmentation of fluorescence microscopy images using three dimensional convolutional neural networks

David Joon Ho, Chicken Fu, Paul Salama, Kenneth W. Dunn, Edward J. Delp

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

7 Scopus citations

Abstract

Recent advance in fluorescence microscopy enables acquisition of 3D image volumes with better quality and deeper penetration into tissue. In this paper, we describe a 3D method which can detect and segment nuclei in fluorescence microscopy images using convolutional neural networks (CNN). For nuclei detection, a 3D adaptive histogram equalization, a 3D distance transform, and a 3D classification CNN are used to find centers of nuclei. For nuclei segmentation, a 3D segmentation CNN is used which is trained from automatically generated synthetic microscopy volumes and their synthetic ground truth volumes. Our method outperforms other 3D segmentation methods and can detect nuclei successfully on multiple data sets.

Original languageEnglish (US)
Title of host publication2018 IEEE 15th International Symposium on Biomedical Imaging, ISBI 2018
PublisherIEEE Computer Society
Pages418-422
Number of pages5
ISBN (Electronic)9781538636367
DOIs
StatePublished - May 23 2018
Event15th IEEE International Symposium on Biomedical Imaging, ISBI 2018 - Washington, United States
Duration: Apr 4 2018Apr 7 2018

Publication series

NameProceedings - International Symposium on Biomedical Imaging
Volume2018-April
ISSN (Print)1945-7928
ISSN (Electronic)1945-8452

Other

Other15th IEEE International Symposium on Biomedical Imaging, ISBI 2018
CountryUnited States
CityWashington
Period4/4/184/7/18

Keywords

  • Convolutional neural network
  • Fluorescence microscopy
  • Nuclei detection
  • Nuclei segmentation
  • Synthetic volumes

ASJC Scopus subject areas

  • Biomedical Engineering
  • Radiology Nuclear Medicine and imaging

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  • Cite this

    Ho, D. J., Fu, C., Salama, P., Dunn, K. W., & Delp, E. J. (2018). Nuclei detection and segmentation of fluorescence microscopy images using three dimensional convolutional neural networks. In 2018 IEEE 15th International Symposium on Biomedical Imaging, ISBI 2018 (pp. 418-422). (Proceedings - International Symposium on Biomedical Imaging; Vol. 2018-April). IEEE Computer Society. https://doi.org/10.1109/ISBI.2018.8363606