Is Segmentation Uncertainty Useful?

Research output: Chapter in Book/Report/Conference proceedingArticle in proceedingsResearchpeer-review

Probabilistic image segmentation encodes varying prediction confidence and inherent ambiguity in the segmentation problem. While different probabilistic segmentation models are designed to capture different aspects of segmentation uncertainty and ambiguity, these modelling differences are rarely discussed in the context of applications of uncertainty. We consider two common use cases of segmentation uncertainty, namely assessment of segmentation quality and active learning. We consider four established strategies for probabilistic segmentation, discuss their modelling capabilities, and investigate their performance in these two tasks. We find that for all models and both tasks, returned uncertainty correlates positively with segmentation error, but does not prove to be useful for active learning.

Original languageEnglish
Title of host publicationInformation Processing in Medical Imaging - 27th International Conference, IPMI 2021, Proceedings
EditorsAasa Feragen, Stefan Sommer, Julia Schnabel, Mads Nielsen
Publication date2021
ISBN (Print)9783030781903
Publication statusPublished - 2021
Event27th International Conference on Information Processing in Medical Imaging, IPMI 2021 - Virtual, Online
Duration: 28 Jun 202130 Jun 2021


Conference27th International Conference on Information Processing in Medical Imaging, IPMI 2021
ByVirtual, Online
SeriesLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume12729 LNCS

Bibliographical note

Publisher Copyright:
© 2021, Springer Nature Switzerland AG.

    Research areas

  • Active learning, Image segmentation, Uncertainty quantification


ID: 282750651