Is Segmentation Uncertainty Useful?
Research output: Chapter in Book/Report/Conference proceeding › Article in proceedings › Research › peer-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.
|Title of host publication||Information Processing in Medical Imaging - 27th International Conference, IPMI 2021, Proceedings|
|Editors||Aasa Feragen, Stefan Sommer, Julia Schnabel, Mads Nielsen|
|Publication status||Published - 2021|
|Event||27th International Conference on Information Processing in Medical Imaging, IPMI 2021 - Virtual, Online|
Duration: 28 Jun 2021 → 30 Jun 2021
|Conference||27th International Conference on Information Processing in Medical Imaging, IPMI 2021|
|Periode||28/06/2021 → 30/06/2021|
|Series||Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)|
© 2021, Springer Nature Switzerland AG.
- Active learning, Image segmentation, Uncertainty quantification