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Training Nuclei Detection Algorithms with Simple Annotations
Fraunhofer Mevis.
Fraunhofer Mevis.
Sectra AB.
Linköping University, Department of Science and Technology, Media and Information Technology. Linköping University, Center for Medical Image Science and Visualization (CMIV). Linköping University, Faculty of Science & Engineering.ORCID iD: 0000-0002-9368-0177
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2017 (English)In: Journal of Pathology Informatics, ISSN 2229-5089, E-ISSN 2153-3539, Vol. 8Article in journal (Refereed) Published
Abstract [en]

Background:

Generating good training datasets is essential for machine learning-based nuclei detection methods. However, creating exhaustive nuclei contour annotations, to derive optimal training data from, is often infeasible.

Methods:

We compared different approaches for training nuclei detection methods solely based on nucleus center markers. Such markers contain less accurate information, especially with regard to nuclear boundaries, but can be produced much easier and in greater quantities. The approaches use different automated sample extraction methods to derive image positions and class labels from nucleus center markers. In addition, the approaches use different automated sample selection methods to improve the detection quality of the classification algorithm and reduce the run time of the training process. We evaluated the approaches based on a previously published generic nuclei detection algorithm and a set of Ki-67-stained breast cancer images.

Results:

A Voronoi tessellation-based sample extraction method produced the best performing training sets. However, subsampling of the extracted training samples was crucial. Even simple class balancing improved the detection quality considerably. The incorporation of active learning led to a further increase in detection quality.

Conclusions:

With appropriate sample extraction and selection methods, nuclei detection algorithms trained on the basis of simple center marker annotations can produce comparable quality to algorithms trained on conventionally created training sets.

Place, publisher, year, edition, pages
2017. Vol. 8
Keyword [en]
Active learning, machine learning, nuclei detection, training set generation
National Category
Signal Processing
Identifiers
URN: urn:nbn:se:liu:diva-140749DOI: 10.4103/jpi.jpi_3_17OAI: oai:DiVA.org:liu-140749DiVA: diva2:1140183
Funder
VINNOVA, 2014-04257
Available from: 2017-09-11 Created: 2017-09-11 Last updated: 2017-09-18

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Lundström, Claes
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Media and Information TechnologyCenter for Medical Image Science and Visualization (CMIV)Faculty of Science & Engineering
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CiteExportLink to record
Permanent link

Direct link
Cite
Citation style
  • apa
  • ieee
  • modern-language-association-8th-edition
  • 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