Pathology-related automated hippocampus segmentation accuracy

  • M. Liedlgruber
  • , K. Butz
  • , Yvonne Höller
  • , G. Kuchukhidze
  • , A. Taylor
  • , A. Thomschewski
  • , O. Tomasi
  • , E. Trinka
  • , A. Uhl

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

Abstract

Hippocampal segmentation accuracy of out-of-the-box software tools (FreeSurfer, AHEAD, BrainParser) is analysed wrt. potential variability in populations with different pathologies. Findings confirm variabilities wrt. different pathologies but also human rater ground truth and single pathologies exhibit significant variability as well.

Original languageEnglish
Title of host publicationBildverarbeitung fur die Medizin 2017
Subtitle of host publicationAlgorithmen – Systeme – Anwendungen - Proceedings des Workshops
EditorsKlaus Hermann Maier-Hein, Heinz Handels, Thomas Martin Deserno, Thomas Tolxdorff
PublisherKluwer Academic Publishers
Pages128-133
Number of pages6
ISBN (Print)9783662543443
DOIs
Publication statusPublished - 2017
Externally publishedYes
EventWorkshops on Image processing for the medicine, 2017 - Heidelberg, Germany
Duration: 12 Mar 201714 Mar 2017

Publication series

NameInformatik aktuell

Conference

ConferenceWorkshops on Image processing for the medicine, 2017
Country/TerritoryGermany
CityHeidelberg
Period12/03/1714/03/17

Bibliographical note

Funding Information: This work has been funded by the Austrian Science Fund (FWF) under Project No. KLI 00012. Publisher Copyright: © Springer-Verlag GmbH Deutschland 2017.

Fingerprint

Dive into the research topics of 'Pathology-related automated hippocampus segmentation accuracy'. Together they form a unique fingerprint.

Cite this