Nuclei Segmentation of Fluorescence Microscopy Images Using Three Dimensional Convolutional Neural Networks

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

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

9 Scopus citations

Abstract

Fluorescence microscopy enables one to visualize subcellular structures of living tissue or cells in three dimensions. This is especially true for two-photon microscopy using near-infrared light which can image deeper into tissue. To characterize and analyze biological structures, nuclei segmentation is a prerequisite step. Due to the complexity and size of the image data sets, manual segmentation is prohibitive. This paper describes a fully 3D nuclei segmentation method using three dimensional convolutional neural networks. To train the network, synthetic volumes with corresponding labeled volumes are automatically generated. Our results from multiple data sets demonstrate that our method can successfully segment nuclei in 3D.

Original languageEnglish (US)
Title of host publicationProceedings - 30th IEEE Conference on Computer Vision and Pattern Recognition Workshops, CVPRW 2017
PublisherIEEE Computer Society
Pages834-842
Number of pages9
Volume2017-July
ISBN (Electronic)9781538607336
DOIs
StatePublished - Aug 22 2017
Event30th IEEE Conference on Computer Vision and Pattern Recognition Workshops, CVPRW 2017 - Honolulu, United States
Duration: Jul 21 2017Jul 26 2017

Other

Other30th IEEE Conference on Computer Vision and Pattern Recognition Workshops, CVPRW 2017
CountryUnited States
CityHonolulu
Period7/21/177/26/17

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ASJC Scopus subject areas

  • Computer Vision and Pattern Recognition
  • Electrical and Electronic Engineering

Cite this

Ho, D. J., Fu, C., Salama, P., Dunn, K., & Delp, E. J. (2017). Nuclei Segmentation of Fluorescence Microscopy Images Using Three Dimensional Convolutional Neural Networks. In Proceedings - 30th IEEE Conference on Computer Vision and Pattern Recognition Workshops, CVPRW 2017 (Vol. 2017-July, pp. 834-842). [8014850] IEEE Computer Society. https://doi.org/10.1109/CVPRW.2017.116