A framework for generating realistic synthetic sequences of total internal reflection fluorescence microscopy images

Seyed Hamid Rezatofighi, William T.E. Pitkeathly, Stephen Gould, Richard Hartley, Katarina Mele, William E. Hughes, James G. Burchfield

Research output: Chapter in Book/Report/Conference proceedingConference PaperResearchpeer-review

6 Citations (Scopus)

Abstract

Since generation of reliable ground truth annotation of fluorescence microscopy sequences is usually a laborious and expensive task, many proposed detection and tracking methods have been evaluated using synthetic data with known ground truth. However, differences between real and synthetic images may lead to inaccurate judgment about the performance of an algorithm. In this paper, we present a framework for generating realistic synthetic sequences of total internal reflection fluorescence microscope (TIRFM) through simulation of the image formation process and accurate measurement and dynamic models. The sequences generated using this framework appropriately reflect the complexities existing in real TIRFM sequences.

Original languageEnglish
Title of host publicationISBI 2013 - 2013 IEEE 10th International Symposium on Biomedical Imaging
Subtitle of host publicationFrom Nano to Macro
PublisherIEEE, Institute of Electrical and Electronics Engineers
Pages157-160
Number of pages4
ISBN (Print)9781467364546
DOIs
Publication statusPublished - 2013
Externally publishedYes
EventIEEE International Symposium on Biomedical Imaging (ISBI) 2013 - San Francisco, United States of America
Duration: 7 Apr 201311 Apr 2013
Conference number: 10th
https://ieeexplore.ieee.org/xpl/conhome/6548349/proceeding (Proceedings)

Publication series

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

Conference

ConferenceIEEE International Symposium on Biomedical Imaging (ISBI) 2013
Abbreviated titleISBI 2013
Country/TerritoryUnited States of America
CitySan Francisco
Period7/04/1311/04/13
Internet address

Keywords

  • Background estimation
  • Dynamic model
  • Synthetic data
  • TIRFM
  • Vesicle shape deformation

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