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

A Recurrent Neural Network Approach for Automated Neural Tracing in Multispectral 3D Images

Yan Yan, Douglas H. Roossien, Benjamin V. Sadis, Jason J. Corso, Dawen Cai
doi: https://doi.org/10.1101/230441
Yan Yan
1Department of Cell and Developmental Biology, University of Michigan Medical School, Ann Arbor, MI, USA
2Department of Electrical Engineering and Computer Science, University of Michigan School of Engineering, Ann Arbor, MI, USA
3Department of Computer Science, Texas State University, San Marcos, TX, USA
  • Find this author on Google Scholar
  • Find this author on PubMed
  • Search for this author on this site
Douglas H. Roossien
1Department of Cell and Developmental Biology, University of Michigan Medical School, Ann Arbor, MI, USA
  • Find this author on Google Scholar
  • Find this author on PubMed
  • Search for this author on this site
Benjamin V. Sadis
1Department of Cell and Developmental Biology, University of Michigan Medical School, Ann Arbor, MI, USA
  • Find this author on Google Scholar
  • Find this author on PubMed
  • Search for this author on this site
Jason J. Corso
2Department of Electrical Engineering and Computer Science, University of Michigan School of Engineering, Ann Arbor, MI, USA
  • Find this author on Google Scholar
  • Find this author on PubMed
  • Search for this author on this site
Dawen Cai
1Department of Cell and Developmental Biology, University of Michigan Medical School, Ann Arbor, MI, USA
  • Find this author on Google Scholar
  • Find this author on PubMed
  • Search for this author on this site
  • For correspondence: dwcai@umich.edu
  • Abstract
  • Full Text
  • Info/History
  • Metrics
  • Preview PDF
Loading

Abstract

Neuronal morphology reconstruction in fluorescence microscopy 3D images is essential for analyzing neuronal cell type and connectivity. Manual tracing of neurons in these images is time consuming and subjective. Automated tracing is highly desired yet is one of the foremost challenges in computational neuroscience. The multispectral labeling technique, Brainbow utilizes high dimensional spectral information to distinguish intermingled neuronal processes. It is particular interesting to develop new algorithms to include the spectral information into the tracing process. Recently, deep learning approaches achieved state-of-the-art in different computer vision and medical imaging applications. To benefit from the power of deep learning, in this paper, we propose an automated neural tracing approach in multispectral 3D Brainbow images based on recurrent neural net-work. We first adopt VBM4D approach to denoise multispectral 3D images. Then we generate cubes as training samples along the ground truth, manually traced paths. These cubes are the input to the recur-rent neural network. The proposed approach is simple and effective. The approach can be implemented with the deep learning toolbox ‘Keras’ in 100 lines. Finally, to evaluate our approach, we computed the average and standard deviation of DIADEM metric from the ground truth results to our tracing results, and from our tracing results to the ground truth results. Extensive experimental results on the collected dataset demonstrate that the proposed approach performs well in Brainbow labeled mouse brain images.

Copyright 
The copyright holder for this preprint is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. All rights reserved. No reuse allowed without permission.
Back to top
PreviousNext
Posted April 30, 2018.
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.
A Recurrent Neural Network Approach for Automated Neural Tracing in Multispectral 3D 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
A Recurrent Neural Network Approach for Automated Neural Tracing in Multispectral 3D Images
Yan Yan, Douglas H. Roossien, Benjamin V. Sadis, Jason J. Corso, Dawen Cai
bioRxiv 230441; doi: https://doi.org/10.1101/230441
Digg logo Reddit logo Twitter logo CiteULike logo Facebook logo Google logo Mendeley logo
Citation Tools
A Recurrent Neural Network Approach for Automated Neural Tracing in Multispectral 3D Images
Yan Yan, Douglas H. Roossien, Benjamin V. Sadis, Jason J. Corso, Dawen Cai
bioRxiv 230441; doi: https://doi.org/10.1101/230441

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

  • Neuroscience
Subject Areas
All Articles
  • Animal Behavior and Cognition (2533)
  • Biochemistry (4975)
  • Bioengineering (3486)
  • Bioinformatics (15229)
  • Biophysics (6908)
  • Cancer Biology (5395)
  • Cell Biology (7751)
  • Clinical Trials (138)
  • Developmental Biology (4539)
  • Ecology (7157)
  • Epidemiology (2059)
  • Evolutionary Biology (10233)
  • Genetics (7516)
  • Genomics (9790)
  • Immunology (4860)
  • Microbiology (13231)
  • Molecular Biology (5142)
  • Neuroscience (29464)
  • Paleontology (203)
  • Pathology (838)
  • Pharmacology and Toxicology (1465)
  • Physiology (2142)
  • Plant Biology (4754)
  • Scientific Communication and Education (1013)
  • Synthetic Biology (1338)
  • Systems Biology (4014)
  • Zoology (768)