Uncertainty Estimation in Landmark Localization Based on Gaussian Heatmaps

Christian Payer*, Martin Urschler, Horst Bischof, Darko Stern

*Corresponding author for this work

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

Abstract

In landmark localization, due to ambiguities in defining their exact position, landmark annotations may suffer from both large inter- and intra-observer variabilites, which result in uncertain annotations. Therefore, predicting a single coordinate for a landmark is not sufficient for modeling the distribution of possible landmark locations. We propose to learn the Gaussian covariances of target heatmaps, such that covariances for pointed heatmaps correspond to more certain landmarks and covariances for flat heatmaps to more uncertain or ambiguous landmarks. By fitting Gaussian functions to the predicted heatmaps, our method is able to obtain landmark location distributions, which model location uncertainties. We show on a dataset of left hand radiographs and on a dataset of lateral cephalograms that the predicted uncertainties correlate with the landmark error, as well as inter-observer variabilities
Original languageEnglish
Title of host publicationUncertainty for Safe Utilization of Machine Learning in Medical Imaging, and Graphs in Biomedical Image Analysis
Subtitle of host publicationSecond International Workshop, UNSURE 2020, and Third International Workshop, GRAIL 2020, Held in Conjunction with MICCAI 2020, Lima, Peru, October 8, 2020, Proceedings
Place of PublicationCham
PublisherSpringer
Pages42-51
Number of pages10
ISBN (Electronic)978-3-030-60365-6
ISBN (Print)978-3-030-60364-9
DOIs
Publication statusPublished - 8 Oct 2020
Event2nd International Workshop on Uncertainty for Safe Utilization of Machine Learning in Medical Imaging: UNSURE 2020 - Virtual, Lima, Peru
Duration: 8 Oct 2020 → …

Publication series

Name Lecture Notes in Computer Science
Volume12443

Workshop

Workshop2nd International Workshop on Uncertainty for Safe Utilization of Machine Learning in Medical Imaging
Country/TerritoryPeru
CityVirtual, Lima
Period8/10/20 → …

Keywords

  • Landmark localization
  • Uncertainty estimation

ASJC Scopus subject areas

  • Theoretical Computer Science
  • Computer Science(all)

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