Skip to main content
bioRxiv
  • Home
  • About
  • Submit
  • ALERTS / RSS
Advanced Search
New Results

Deep learning pipeline for cell edge segmentation of time-lapse live cell images

Chuangqi Wang, Xitong Zhang, Hee June Choi, Bolun Lin, Yudong Yu, Carly Whittle, Madison Ryan, Yenyu Chen, Kwonmoo Lee
doi: https://doi.org/10.1101/191858
Chuangqi Wang
1Department of Biomedical Engineering, Worcester Polytechnic Institute, Massachusetts, 01609, USA
  • Find this author on Google Scholar
  • Find this author on PubMed
  • Search for this author on this site
Xitong Zhang
2Department of Computer Science, Worcester Polytechnic Institute, Massachusetts, 01609, USA
  • Find this author on Google Scholar
  • Find this author on PubMed
  • Search for this author on this site
Hee June Choi
1Department of Biomedical Engineering, Worcester Polytechnic Institute, Massachusetts, 01609, USA
  • Find this author on Google Scholar
  • Find this author on PubMed
  • Search for this author on this site
Bolun Lin
2Department of Computer Science, Worcester Polytechnic Institute, Massachusetts, 01609, USA
  • Find this author on Google Scholar
  • Find this author on PubMed
  • Search for this author on this site
Yudong Yu
3Robotics Engineering Program, Worcester Polytechnic Institute, Massachusetts, 01609, USA
  • Find this author on Google Scholar
  • Find this author on PubMed
  • Search for this author on this site
Carly Whittle
1Department of Biomedical Engineering, Worcester Polytechnic Institute, Massachusetts, 01609, USA
  • Find this author on Google Scholar
  • Find this author on PubMed
  • Search for this author on this site
Madison Ryan
1Department of Biomedical Engineering, Worcester Polytechnic Institute, Massachusetts, 01609, USA
  • Find this author on Google Scholar
  • Find this author on PubMed
  • Search for this author on this site
Yenyu Chen
1Department of Biomedical Engineering, Worcester Polytechnic Institute, Massachusetts, 01609, USA
  • Find this author on Google Scholar
  • Find this author on PubMed
  • Search for this author on this site
Kwonmoo Lee
1Department of Biomedical Engineering, Worcester Polytechnic Institute, Massachusetts, 01609, USA
4Department of Electrical and Computer Engineering, Worcester Polytechnic Institute, Massachusetts, 01609, USA
5Bioinformatics and Computational Biology Program, Worcester Polytechnic Institute, Massachusetts, 01609, USA
  • Find this author on Google Scholar
  • Find this author on PubMed
  • Search for this author on this site
  • For correspondence: klee@wpi.edu
  • Abstract
  • Full Text
  • Info/History
  • Metrics
  • Preview PDF
Loading

Abstract

Quantitative live cell imaging has been widely used to study various dynamical processes in cell biology. Phase contrast microscopy is a popular imaging modality for live cell imaging since it does not require labeling and cause any phototoxicity to live cells. However, phase contrast images have posed significant challenges for accurate image segmentation due to complex image features. Fluorescence live cell imaging has also been used to monitor the dynamics of specific molecules in live cells. But unlike immunofluorescence imaging, fluorescence live cell images are highly prone to noise, low contrast, and uneven illumination. These often lead to erroneous cell segmentation, hindering quantitative analyses of dynamical cellular processes. Although deep learning has been successfully applied in image segmentation by automatically learning hierarchical features directly from raw data, it typically requires large datasets and high computational cost to train deep neural networks. These make it challenging to apply deep learning in routine laboratory settings. In this paper, we evaluate a deep learning-based segmentation pipeline for time-lapse live cell movies, which uses small efforts to prepare the training set by leveraging the temporal coherence of time-lapse image sequences. We train deep neural networks using a small portion of images in the movies, and then predict cell edges for the entire image frames of the same movies. To further increase segmentation accuracy using small numbers of training frames, we integrate VGG16 pretrained model with the U-Net structure (VGG16-U-Net) for neural network training. Using live cell movies from phase contrast, Total Internal Reflection Fluorescence (TIRF), and spinning disk confocal microscopes, we demonstrate that the labeling of cell edges in small portions (5∼10%) can provide enough training data for the deep learning segmentation. Particularly, VGG16-U-Net produces significantly more accurate segmentation than U-Net by increasing the recall performance. We expect that our deep learning segmentation pipeline will facilitate quantitative analyses of challenging high-resolution live cell movies.

Copyright 
The copyright holder for this preprint is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made available under a CC-BY-NC-ND 4.0 International license.
Back to top
PreviousNext
Posted August 27, 2019.
Download PDF
Email

Thank you for your interest in spreading the word about bioRxiv.

NOTE: Your email address is requested solely to identify you as the sender of this article.

Enter multiple addresses on separate lines or separate them with commas.
Deep learning pipeline for cell edge segmentation of time-lapse live cell images
(Your Name) has forwarded a page to you from bioRxiv
(Your Name) thought you would like to see this page from the bioRxiv website.
CAPTCHA
This question is for testing whether or not you are a human visitor and to prevent automated spam submissions.
Share
Deep learning pipeline for cell edge segmentation of time-lapse live cell images
Chuangqi Wang, Xitong Zhang, Hee June Choi, Bolun Lin, Yudong Yu, Carly Whittle, Madison Ryan, Yenyu Chen, Kwonmoo Lee
bioRxiv 191858; doi: https://doi.org/10.1101/191858
Digg logo Reddit logo Twitter logo Facebook logo Google logo LinkedIn logo Mendeley logo
Citation Tools
Deep learning pipeline for cell edge segmentation of time-lapse live cell images
Chuangqi Wang, Xitong Zhang, Hee June Choi, Bolun Lin, Yudong Yu, Carly Whittle, Madison Ryan, Yenyu Chen, Kwonmoo Lee
bioRxiv 191858; doi: https://doi.org/10.1101/191858

Citation Manager Formats

  • BibTeX
  • Bookends
  • EasyBib
  • EndNote (tagged)
  • EndNote 8 (xml)
  • Medlars
  • Mendeley
  • Papers
  • RefWorks Tagged
  • Ref Manager
  • RIS
  • Zotero
  • Tweet Widget
  • Facebook Like
  • Google Plus One

Subject Area

  • Bioinformatics
Subject Areas
All Articles
  • Animal Behavior and Cognition (3698)
  • Biochemistry (7809)
  • Bioengineering (5689)
  • Bioinformatics (21330)
  • Biophysics (10595)
  • Cancer Biology (8199)
  • Cell Biology (11961)
  • Clinical Trials (138)
  • Developmental Biology (6777)
  • Ecology (10419)
  • Epidemiology (2065)
  • Evolutionary Biology (13900)
  • Genetics (9726)
  • Genomics (13094)
  • Immunology (8164)
  • Microbiology (20058)
  • Molecular Biology (7871)
  • Neuroscience (43147)
  • Paleontology (321)
  • Pathology (1280)
  • Pharmacology and Toxicology (2264)
  • Physiology (3362)
  • Plant Biology (7246)
  • Scientific Communication and Education (1315)
  • Synthetic Biology (2010)
  • Systems Biology (5547)
  • Zoology (1132)