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Is Segmentation Uncertainty Useful? / Czolbe, Steffen; Arnavaz, Kasra; Krause, Oswin; Feragen, Aasa.
Information Processing in Medical Imaging - 27th International Conference, IPMI 2021, Proceedings. ed. / Aasa Feragen; Stefan Sommer; Julia Schnabel; Mads Nielsen. Springer, 2021. p. 715-726 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), Vol. 12729 LNCS).
Research output: Chapter in Book/Report/Conference proceeding › Article in proceedings › Research › peer-review
Harvard
Czolbe, S, Arnavaz, K, Krause, O & Feragen, A 2021,
Is Segmentation Uncertainty Useful? in A Feragen, S Sommer, J Schnabel & M Nielsen (eds),
Information Processing in Medical Imaging - 27th International Conference, IPMI 2021, Proceedings. Springer, Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), vol. 12729 LNCS, pp. 715-726, 27th International Conference on Information Processing in Medical Imaging, IPMI 2021, Virtual, Online,
28/06/2021.
https://doi.org/10.1007/978-3-030-78191-0_55
APA
Czolbe, S., Arnavaz, K., Krause, O., & Feragen, A. (2021).
Is Segmentation Uncertainty Useful? In A. Feragen, S. Sommer, J. Schnabel, & M. Nielsen (Eds.),
Information Processing in Medical Imaging - 27th International Conference, IPMI 2021, Proceedings (pp. 715-726). Springer. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) Vol. 12729 LNCS
https://doi.org/10.1007/978-3-030-78191-0_55
Vancouver
Czolbe S, Arnavaz K, Krause O, Feragen A.
Is Segmentation Uncertainty Useful? In Feragen A, Sommer S, Schnabel J, Nielsen M, editors, Information Processing in Medical Imaging - 27th International Conference, IPMI 2021, Proceedings. Springer. 2021. p. 715-726. (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), Vol. 12729 LNCS).
https://doi.org/10.1007/978-3-030-78191-0_55
Author
Czolbe, Steffen ; Arnavaz, Kasra ; Krause, Oswin ; Feragen, Aasa. / Is Segmentation Uncertainty Useful?. Information Processing in Medical Imaging - 27th International Conference, IPMI 2021, Proceedings. editor / Aasa Feragen ; Stefan Sommer ; Julia Schnabel ; Mads Nielsen. Springer, 2021. pp. 715-726 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), Vol. 12729 LNCS).
Bibtex
@inproceedings{860a37b5806b460f8307f2d471bdd0ad,
title = "Is Segmentation Uncertainty Useful?",
abstract = "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.",
keywords = "Active learning, Image segmentation, Uncertainty quantification",
author = "Steffen Czolbe and Kasra Arnavaz and Oswin Krause and Aasa Feragen",
note = "Publisher Copyright: {\textcopyright} 2021, Springer Nature Switzerland AG.; 27th International Conference on Information Processing in Medical Imaging, IPMI 2021 ; Conference date: 28-06-2021 Through 30-06-2021",
year = "2021",
doi = "10.1007/978-3-030-78191-0_55",
language = "English",
isbn = "9783030781903",
series = "Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)",
publisher = "Springer",
pages = "715--726",
editor = "Aasa Feragen and Stefan Sommer and Julia Schnabel and Mads Nielsen",
booktitle = "Information Processing in Medical Imaging - 27th International Conference, IPMI 2021, Proceedings",
address = "Switzerland",
}
RIS
TY - GEN
T1 - Is Segmentation Uncertainty Useful?
AU - Czolbe, Steffen
AU - Arnavaz, Kasra
AU - Krause, Oswin
AU - Feragen, Aasa
N1 - Publisher Copyright:
© 2021, Springer Nature Switzerland AG.
PY - 2021
Y1 - 2021
N2 - 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.
AB - 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.
KW - Active learning
KW - Image segmentation
KW - Uncertainty quantification
UR - http://www.scopus.com/inward/record.url?scp=85111417208&partnerID=8YFLogxK
U2 - 10.1007/978-3-030-78191-0_55
DO - 10.1007/978-3-030-78191-0_55
M3 - Article in proceedings
AN - SCOPUS:85111417208
SN - 9783030781903
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 715
EP - 726
BT - Information Processing in Medical Imaging - 27th International Conference, IPMI 2021, Proceedings
A2 - Feragen, Aasa
A2 - Sommer, Stefan
A2 - Schnabel, Julia
A2 - Nielsen, Mads
PB - Springer
T2 - 27th International Conference on Information Processing in Medical Imaging, IPMI 2021
Y2 - 28 June 2021 through 30 June 2021
ER -
