Tubule segmentation of fluorescence microscopy images based on convolutional neural networks with inhomogeneity correction

Soonam Lee, Chichen Fu, Paul Salama, Kenneth Dunn, Edward J. Delp

Research output: Contribution to journalConference article

3 Citations (Scopus)

Abstract

Fluorescence microscopy has become a widely used tool for studying various biological structures of in vivo tissue or cells. However, quantitative analysis of these biological structures re- mains a challenge due to their complexity which is exacerbated by distortions caused by lens aberrations and light scattering. Moreover, manual quantification of such image volumes is an in- tractable and error-prone process, making the need for automated image analysis methods crucial. This paper describes a segmen- tation method for tubular structures in fluorescence microscopy images using convolutional neural networks with data augmen- tation and inhomogeneity correction. The segmentation results of the proposed method are visually and numerically compared with other microscopy segmentation methods. Experimental re- sults indicate that the proposed method has better performance with correctly segmenting and identifying multiple tubular struc- tures compared to other methods.

Original languageEnglish (US)
Article numberS11
JournalIS and T International Symposium on Electronic Imaging Science and Technology
VolumePart F138654
DOIs
StatePublished - Jan 1 2018
Event16th Computational Imaging Conference, COMIG 2018 - Burlingame, United States
Duration: Jan 28 2018Feb 1 2018

Fingerprint

Fluorescence microscopy
inhomogeneity
microscopy
Neural networks
fluorescence
Aberrations
Light scattering
Image analysis
Lenses
Microscopic examination
Tissue
image analysis
quantitative analysis
aberration
light scattering
Chemical analysis
lenses
augmentation
cells
scattering

ASJC Scopus subject areas

  • Computer Graphics and Computer-Aided Design
  • Computer Science Applications
  • Human-Computer Interaction
  • Software
  • Electrical and Electronic Engineering
  • Atomic and Molecular Physics, and Optics

Cite this

Tubule segmentation of fluorescence microscopy images based on convolutional neural networks with inhomogeneity correction. / Lee, Soonam; Fu, Chichen; Salama, Paul; Dunn, Kenneth; Delp, Edward J.

In: IS and T International Symposium on Electronic Imaging Science and Technology, Vol. Part F138654, S11, 01.01.2018.

Research output: Contribution to journalConference article

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