uu.seUppsala University Publications
Change search
CiteExportLink to record
Permanent link

Direct link
Cite
Citation style
  • apa
  • ieee
  • modern-language-association
  • vancouver
  • Other style
More styles
Language
  • de-DE
  • en-GB
  • en-US
  • fi-FI
  • nn-NO
  • nn-NB
  • sv-SE
  • Other locale
More languages
Output format
  • html
  • text
  • asciidoc
  • rtf
SynQuant: An Automatic Tool to Quantify Synapses from Microscopy Images
Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Division of Visual Information and Interaction. Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Computerized Image Analysis and Human-Computer Interaction. Uppsala University, Science for Life Laboratory, SciLifeLab.ORCID iD: 0000-0002-6699-4015
Show others and affiliations
2019 (English)In: Bioinformatics, ISSN 1367-4803, E-ISSN 1367-4811Article in journal (Refereed) Published
Abstract [en]

Synapses are essential to neural signal transmission. Therefore, quantification of synapses and related neurites from images is vital to gain insights into the underlying pathways of brain functionality and diseases. Despite the wide availability of synaptic punctum imaging data, several issues are impeding satisfactory quantification of these structures by current tools. First, the antibodies used for labeling synapses are not perfectly specific to synapses. These antibodies may exist in neurites or other cell compartments. Second, the brightness of different neurites and synaptic puncta is heterogeneous due to the variation of antibody concentration and synapse-intrinsic differences. Third, images often have low signal to noise ratio due to constraints of experiment facilities and availability of sensitive antibodies. These issues make the detection of synapses challenging and necessitates developing a new tool to easily and accurately quantify synapses.We present an automatic probability-principled synapse detection algorithm and integrate it into our synapse quantification tool SynQuant. Derived from the theory of order statistics, our method controls the false discovery rate and improves the power of detecting synapses. SynQuant is unsupervised, works for both 2D and 3D data, and can handle multiple staining channels. Through extensive experiments on one synthetic and three real data sets with ground truth annotation or manually labeling, SynQuant was demonstrated to outperform peer specialized unsupervised synapse detection tools as well as generic spot detection methods.Supplementary data are available at Bioinformatics online. Java source code, Fiji plug-in, and test data are available at https://github.com/yu-lab-vt/SynQuant.

Place, publisher, year, edition, pages
2019.
National Category
Computer and Information Sciences
Research subject
Computerized Image Processing
Identifiers
URN: urn:nbn:se:uu:diva-395144DOI: 10.1093/bioinformatics/btz760OAI: oai:DiVA.org:uu-395144DiVA, id: diva2:1360541
Note

btz760

Available from: 2019-10-14 Created: 2019-10-14 Last updated: 2019-10-14

Open Access in DiVA

No full text in DiVA

Other links

Publisher's full texthttps://doi.org/10.1093/bioinformatics/btz760

Authority records BETA

Ranefall, Petter

Search in DiVA

By author/editor
Ranefall, Petter
By organisation
Division of Visual Information and InteractionComputerized Image Analysis and Human-Computer InteractionScience for Life Laboratory, SciLifeLab
In the same journal
Bioinformatics
Computer and Information Sciences

Search outside of DiVA

GoogleGoogle Scholar

doi
urn-nbn

Altmetric score

doi
urn-nbn
Total: 7 hits
CiteExportLink to record
Permanent link

Direct link
Cite
Citation style
  • apa
  • ieee
  • modern-language-association
  • vancouver
  • Other style
More styles
Language
  • de-DE
  • en-GB
  • en-US
  • fi-FI
  • nn-NO
  • nn-NB
  • sv-SE
  • Other locale
More languages
Output format
  • html
  • text
  • asciidoc
  • rtf