Three dimensional fluorescence microscopy image synthesis and segmentation

Chichen Fu, Soonam Lee, David Joon Ho, Shuo Han, Paul Salama, Kenneth Dunn, Edward J. Delp

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

4 Citations (Scopus)

Abstract

Advances in fluorescence microscopy enable acquisition of 3D image volumes with better image quality and deeper penetration into tissue. Segmentation is a required step to characterize and analyze biological structures in the images and recent 3D segmentation using deep learning has achieved promising results. One issue is that deep learning techniques require a large set of groundtruth data which is impractical to annotate manually for large 3D microscopy volumes. This paper describes a 3D deep learning nuclei segmentation method using synthetic 3D volumes for training. A set of synthetic volumes and the corresponding groundtruth are generated using spatially constrained cycle-consistent adversarial networks. Segmentation results demonstrate that our proposed method is capable of segmenting nuclei successfully for various data sets.

Original languageEnglish (US)
Title of host publicationProceedings - 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops, CVPRW 2018
PublisherIEEE Computer Society
Pages2302-2310
Number of pages9
Volume2018-June
ISBN (Electronic)9781538661000
DOIs
StatePublished - Dec 13 2018
Event31st Meeting of the IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops, CVPRW 2018 - Salt Lake City, United States
Duration: Jun 18 2018Jun 22 2018

Other

Other31st Meeting of the IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops, CVPRW 2018
CountryUnited States
CitySalt Lake City
Period6/18/186/22/18

Fingerprint

Fluorescence microscopy
Image quality
Microscopic examination
Tissue
Deep learning

ASJC Scopus subject areas

  • Computer Vision and Pattern Recognition
  • Electrical and Electronic Engineering

Cite this

Fu, C., Lee, S., Ho, D. J., Han, S., Salama, P., Dunn, K., & Delp, E. J. (2018). Three dimensional fluorescence microscopy image synthesis and segmentation. In Proceedings - 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops, CVPRW 2018 (Vol. 2018-June, pp. 2302-2310). [8575470] IEEE Computer Society. https://doi.org/10.1109/CVPRW.2018.00298

Three dimensional fluorescence microscopy image synthesis and segmentation. / Fu, Chichen; Lee, Soonam; Ho, David Joon; Han, Shuo; Salama, Paul; Dunn, Kenneth; Delp, Edward J.

Proceedings - 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops, CVPRW 2018. Vol. 2018-June IEEE Computer Society, 2018. p. 2302-2310 8575470.

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

Fu, C, Lee, S, Ho, DJ, Han, S, Salama, P, Dunn, K & Delp, EJ 2018, Three dimensional fluorescence microscopy image synthesis and segmentation. in Proceedings - 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops, CVPRW 2018. vol. 2018-June, 8575470, IEEE Computer Society, pp. 2302-2310, 31st Meeting of the IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops, CVPRW 2018, Salt Lake City, United States, 6/18/18. https://doi.org/10.1109/CVPRW.2018.00298
Fu C, Lee S, Ho DJ, Han S, Salama P, Dunn K et al. Three dimensional fluorescence microscopy image synthesis and segmentation. In Proceedings - 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops, CVPRW 2018. Vol. 2018-June. IEEE Computer Society. 2018. p. 2302-2310. 8575470 https://doi.org/10.1109/CVPRW.2018.00298
Fu, Chichen ; Lee, Soonam ; Ho, David Joon ; Han, Shuo ; Salama, Paul ; Dunn, Kenneth ; Delp, Edward J. / Three dimensional fluorescence microscopy image synthesis and segmentation. Proceedings - 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops, CVPRW 2018. Vol. 2018-June IEEE Computer Society, 2018. pp. 2302-2310
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