Article

CRISP - Reliable Uncertainty Estimation for Medical Image Segmentation

Authors:

Olivier Bernard,

Mihaela Porumb,

Agisilaos Chartsias,

Pierre-Marc JodoinAuthors Info & Claims

Medical Image Computing and Computer Assisted Intervention – MICCAI 2022: 25th International Conference, Singapore, September 18–22, 2022, Proceedings, Part VIII

Pages 492 - 502

https://doi.org/10.1007/978-3-031-16452-1_47

Published: 18 September 2022 Publication History

Abstract

Accurate uncertainty estimation is a critical need for the medical imaging community. A variety of methods have been proposed, all direct extensions of classification uncertainty estimations techniques. The independent pixel-wise uncertainty estimates, often based on the probabilistic interpretation of neural networks, do not take into account anatomical prior knowledge and consequently provide sub-optimal results to many segmentation tasks. For this reason, we propose CRISP a ContRastive Image Segmentation for uncertainty Prediction method. At its core, CRISP implements a contrastive method to learn a joint latent space which encodes a distribution of valid segmentations and their corresponding images. We use this joint latent space to compare predictions to thousands of latent vectors and provide anatomically consistent uncertainty maps. Comprehensive studies performed on four medical image databases involving different modalities and organs underlines the superiority of our method compared to state-of-the-art approaches. Code is available at: https://github.com/ThierryJudge/CRISP-uncertainty.

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Cited By

Judge TBernard OCho Kim WGomez AChartsias AJodoin P(2023)Asymmetric Contour Uncertainty Estimation for Medical Image SegmentationMedical Image Computing and Computer Assisted Intervention – MICCAI 202310.1007/978-3-031-43898-1_21(210-220)Online publication date: 8-Oct-2023
https://dl.acm.org/doi/10.1007/978-3-031-43898-1_21

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Information & Contributors

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Published In

cover image Guide Proceedings

Medical Image Computing and Computer Assisted Intervention – MICCAI 2022: 25th International Conference, Singapore, September 18–22, 2022, Proceedings, Part VIII

Sep 2022

773 pages

ISBN:978-3-031-16451-4

DOI:10.1007/978-3-031-16452-1

Editors:
Linwei Wang
Rochester Institute of Technology, Rochester, NY, USA
,
Qi Dou
Chinese University of Hong Kong, Hong Kong, Hong Kong
,
P. Thomas Fletcher
University of Virginia, Charlottesville, VA, USA
,
Stefanie Speidel
National Center for Tumor Diseases (NCT/UCC), Dresden, Germany
,
Shuo Li
Case Western Reserve University, Cleveland, OH, USA

© The Author(s), under exclusive license to Springer Nature Switzerland AG 2022.

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Springer-Verlag

Berlin, Heidelberg

Publication History

Published: 18 September 2022

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Cited By

Judge TBernard OCho Kim WGomez AChartsias AJodoin P(2023)Asymmetric Contour Uncertainty Estimation for Medical Image SegmentationMedical Image Computing and Computer Assisted Intervention – MICCAI 202310.1007/978-3-031-43898-1_21(210-220)Online publication date: 8-Oct-2023
https://dl.acm.org/doi/10.1007/978-3-031-43898-1_21

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