Abstract
Aleatoric uncertainty estimation is a critical step in medical image segmentation. Most techniques for estimating aleatoric uncertainty for segmentation purposes assume a Gaussian distribution over the neural network’s logit value modeling the uncertainty in the predicted class. However, in many cases, such as image segmentation, there is no uncertainty about the presence of a specific structure, but rather about the precise outline of that structure. For this reason, we explicitly model the location uncertainty by redefining the conventional per-pixel segmentation task as a contour regression problem. This allows for modeling the uncertainty of contour points using a more appropriate multivariate distribution. Additionally, as contour uncertainty may be asymmetric, we use a multivariate skewed Gaussian distribution. In addition to being directly interpretable, our uncertainty estimation method outperforms previous methods on three datasets using two different image modalities. Code is available at: https://github.com/ThierryJudge/contouring-uncertainty.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Similar content being viewed by others
References
Ayhan, M.S., Berens, P.: Test-time data augmentation for estimation of heteroscedastic aleatoric uncertainty in deep neural networks. In: International Conference on Medical Imaging with Deep Learning (2018)
Azzalini, A.: Institute of Mathematical Statistics Monographs: The Skew-Normal and Related Families Series Number 3. Cambridge University Press, Cambridge (2013)
Baumgartner, C.F., et al.: PHiSeg: capturing uncertainty in medical image segmentation. In: Shen, D., et al. (eds.) MICCAI 2019. LNCS, vol. 11765, pp. 119–127. Springer, Cham (2019). https://doi.org/10.1007/978-3-030-32245-8_14
Bernard, O., et al.: Deep learning techniques for automatic MRI cardiac multi-structures segmentation and diagnosis: is the problem solved? IEEE Trans. Med. Imaging 37(11), 2514–2525 (2018)
Blundell, C., Cornebise, J., Kavukcuoglu, K., Wierstra, D.: Weight uncertainty in neural network. In: Bach, F., Blei, D. (eds.) Proceedings of the 32nd International Conference on Machine Learning. Proceedings of Machine Learning Research, vol. 37, pp. 1613–1622. PMLR, Lille, France, 07–09 July 2015
Camarasa, R., et al.: Quantitative comparison of Monte-Carlo dropout uncertainty measures for multi-class segmentation. In: Sudre, C.H., et al. (eds.) UNSURE/GRAIL -2020. LNCS, vol. 12443, pp. 32–41. Springer, Cham (2020). https://doi.org/10.1007/978-3-030-60365-6_4
Chen, L., Papandreou, G., Schroff, F., Adam, H.: Rethinking atrous convolution for semantic image segmentation. CoRR abs/1706.05587 (2017)
Corbière, C., Thome, N., Bar-Hen, A., Cord, M., Pérez, P.: Addressing failure prediction by learning model confidence. In: Advances in Neural Information Processing Systems, vol. 32, pp. 2902–2913. Curran Associates, Inc. (2019)
DeVries, T., Taylor, G.W.: Leveraging uncertainty estimates for predicting segmentation quality. CoRR abs/1807.00502 (2018)
Gaggion, N., Mansilla, L., Mosquera, C., Milone, D.H., Ferrante, E.: Improving anatomical plausibility in medical image segmentation via hybrid graph neural networks: applications to chest x-ray analysis. IEEE Trans. Med. Imaging (2022)
Gal, Y., Ghahramani, Z.: Bayesian convolutional neural networks with Bernoulli approximate variational inference. arXiv abs/1506.02158 (2015)
Gal, Y., Ghahramani, Z.: Dropout as a Bayesian approximation: representing model uncertainty in deep learning. In: Proceedings of the 33rd International Conference on International Conference on Machine Learning. ICML’16, vol. 48, pp. 1050–1059. JMLR.org (2016)
Gomez, A., et al.: Left ventricle contouring of apical three-chamber views on 2d echocardiography. In: Aylward, S., Noble, J.A., Hu, Y., Lee, S.L., Baum, Z., Min, Z. (eds.) ASMUS 2022. LNCS, vol. 13565, pp. 96–105. Springer, Cham (2022). https://doi.org/10.1007/978-3-031-16902-1_10
Isensee, F., Jaeger, P.F., Kohl, S.A., Petersen, J., Maier-Hein, K.H.: NNU-net: a self-configuring method for deep learning-based biomedical image segmentation. Nat. Methods 18(2), 203–211 (2021)
Judge, T., Bernard, O., Porumb, M., Chartsias, A., Beqiri, A., Jodoin, P.M.: Crisp - reliable uncertainty estimation for medical image segmentation. In: Wang, L., Dou, Q., Fletcher, P.T., Speidel, S., Li, S. (eds.) MICCAI 2022MICCAI 2022. LNCS, vol. 13438, pp. 492–502. Springer, Cham (2022). https://doi.org/10.1007/978-3-031-16452-1_47
Kendall, A., Gal, Y.: What uncertainties do we need in Bayesian deep learning for computer vision? In: Guyon, I., et al. (eds.) Advances in Neural Information Processing Systems, vol. 30, pp. 5574–5584. Curran Associates, Inc. (2017)
Kingma, D.P., Ba, J.: Adam: a method for stochastic optimization. In: Bengio, Y., LeCun, Y. (eds.) 3rd International Conference on Learning Representations, ICLR 2015, San Diego, CA, USA, 7–9 May 2015, Conference Track Proceedings (2015)
Kohl, S., et al.: A probabilistic u-net for segmentation of ambiguous images. In: Advances in Neural Information Processing Systems, vol. 31. Curran Associates, Inc. (2018)
Lakshminarayanan, B., Pritzel, A., Blundell, C.: Simple and scalable predictive uncertainty estimation using deep ensembles. In: Guyon, I., et al. (eds.) Advances in Neural Information Processing Systems, vol. 30. Curran Associates, Inc. (2017)
Leclerc, S., et al.: Deep learning for segmentation using an open large-scale dataset in 2D echocardiography. IEEE Trans. Med. Imaging 38(9), 2198–2210 (2019)
Nibali, A., He, Z., Morgan, S., Prendergast, L.: Numerical coordinate regression with convolutional neural networks. arXiv preprint arXiv:1801.07372 (2018)
Niculescu-Mizil, A., Caruana, R.: Predicting good probabilities with supervised learning. In: Proceedings of the 22nd International Conference on Machine Learning. ICML ’05, pp. 625–632. Association for Computing Machinery, New York, NY, USA (2005)
Pakdaman Naeini, M., Cooper, G., Hauskrecht, M.: Obtaining well calibrated probabilities using Bayesian binning. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 29, no. 1, February 2015
Paszke, A., Chaurasia, A., Kim, S., Culurciello, E.: Enet: a deep neural network architecture for real-time semantic segmentation. CoRR abs/1606.02147 (2016)
Schobs, L.A., Swift, A.J., Lu, H.: Uncertainty estimation for heatmap-based landmark localization. IEEE Trans. Med. Imaging 42(4), 1021–1034 (2023)
Shiraishi, J., et al.: Development of a digital image database for chest radiographs with and without a lung nodule: receiver operating characteristic analysis of radiologists’ detection of pulmonary nodules. Am. J. Roentgenol. 174, 71–74 (2000)
Thaler, F., Payer, C., Urschler, M., Štern, D.: Modeling annotation uncertainty with Gaussian heatmaps in landmark localization. Mach. Learn. Biomed. Imaging 1, 1–27 (2021)
Tornetta, G.N.: Entropy methods for the confidence assessment of probabilistic classification models. Statistica (Bologna) 81(4), 383–398 (2021)
Wang, G., Li, W., Aertsen, M., Deprest, J., Ourselin, S., Vercauteren, T.: Aleatoric uncertainty estimation with test-time augmentation for medical image segmentation with convolutional neural networks. Neurocomputing 338, 34–45 (2019)
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
1 Electronic supplementary material
Below is the link to the electronic supplementary material.
Rights and permissions
Copyright information
© 2023 The Author(s), under exclusive license to Springer Nature Switzerland AG
About this paper
Cite this paper
Judge, T., Bernard, O., Cho Kim, WJ., Gomez, A., Chartsias, A., Jodoin, PM. (2023). Asymmetric Contour Uncertainty Estimation for Medical Image Segmentation. In: Greenspan, H., et al. Medical Image Computing and Computer Assisted Intervention – MICCAI 2023. MICCAI 2023. Lecture Notes in Computer Science, vol 14222. Springer, Cham. https://doi.org/10.1007/978-3-031-43898-1_21
Download citation
DOI: https://doi.org/10.1007/978-3-031-43898-1_21
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-031-43897-4
Online ISBN: 978-3-031-43898-1
eBook Packages: Computer ScienceComputer Science (R0)
