Abstract
Robust and accurate segmentation for elongated physiological structures is challenging, especially in the ambiguous region, such as the corneal endothelium microscope image with uneven illumination or the fundus image with disease interference. In this paper, we present a spatial and scale uncertainty-aware network (SSU-Net) that fully uses both spatial and scale uncertainty to highlight ambiguous regions and integrate hierarchical structure contexts. First, we estimate epistemic and aleatoric spatial uncertainty maps using Monte Carlo dropout to approximate Bayesian networks. Based on these spatial uncertainty maps, we propose the gated soft uncertainty-aware (GSUA) module to guide the model to focus on ambiguous regions. Second, we extract the uncertainty under different scales and propose the multi-scale uncertainty-aware (MSUA) fusion module to integrate structure contexts from hierarchical predictions, strengthening the final prediction. Finally, we visualize the uncertainty map of final prediction, providing interpretability for segmentation results. Experiment results show that the SSU-Net performs best on cornea endothelial cell and retinal vessel segmentation tasks. Moreover, compared with counterpart uncertainty-based methods, SSU-Net is more accurate and robust.
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
Chen, J., et al.: Transunet: transformers make strong encoders for medical image segmentation. arXiv preprint arXiv:2102.04306 (2021)
Gal, Y., Ghahramani, Z.: Dropout as a bayesian approximation: Representing model uncertainty in deep learning. In: Balcan, M.F., Weinberger, K.Q. (eds.) Proceedings of ICML. Proceedings of Machine Learning Research, vol. 48, pp. 1050–1059. PMLR, New York, New York, USA (20–22 Jun 2016)
Gawlikowski, J., et al.: A survey of uncertainty in deep neural networks. arXiv preprint arXiv:2107.03342 (2021)
Guo, Changlu, et al.: Sa-unet: spatial attention u-net for retinal vessel segmentation. In: Proceedings of ICPR, pp. 1236–1242 (2021)
Jin, K., et al.: Fives: a fundus image dataset for artificial intelligence based vessel segmentation. Sci. Data 9(1), 475 (2022)
Kendall, A., Gal, Y.: What uncertainties do we need in Bayesian deep learning for computer vision? Proc. NeurIPS 30 (2017)
Kohl, S., et al.: A probabilistic u-net for segmentation of ambiguous images. Proc. of NeurIPS 31 (2018)
Lakshminarayanan, B., et al.: Simple and scalable predictive uncertainty estimation using deep ensembles. Proc. of NeurIPS 30 (2017)
Lee, J., et al.: Method to minimize the errors of AI: quantifying and exploiting uncertainty of deep learning in brain tumor segmentation. Sensors 22(6), 2406 (2022)
Leibig, C., et al.: Leveraging uncertainty information from deep neural networks for disease detection. Sci. Rep. 7(1), 1–14 (2017)
Li, L., et al.: Iternet: Retinal image segmentation utilizing structural redundancy in vessel networks. In: Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision, pp. 3656–3665 (2020)
Liu, W., et al.: Full-resolution network and dual-threshold iteration for retinal vessel and coronary angiograph segmentation. IEEE J. Biomed. Health Inform. 26(9), 4623–4634 (2022)
Mehrtash, A., et al.: Confidence calibration and predictive uncertainty estimation for deep medical image segmentation. IEEE Trans. Med. Imaging 39(12), 3868–3878 (2020)
Mou, L., et al.: Cs2-net: deep learning segmentation of curvilinear structures in medical imaging. Med. Image Anal. 67, 101874 (2021)
Neal, R.M.: Bayesian learning for neural networks. IEEE Trans. Neural Netw. (1994)
Oktay, O., et al.: Attention u-net: learning where to look for the pancreas. arXiv preprint arXiv:1804.03999 (2018)
Pearce, T., et al.: Understanding softmax confidence and uncertainty. arXiv preprint arXiv:2106.04972 (2021)
Pidaparthy, H., et al.: Automatic play segmentation of hockey videos. In: Proceedings of CVPR, pp. 4585–4593 (2021)
Ronneberger, O., Fischer, P., Brox, T.: U-Net: convolutional networks for biomedical image segmentation. In: Navab, N., Hornegger, J., Wells, W.M., Frangi, A.F. (eds.) MICCAI 2015. LNCS, vol. 9351, pp. 234–241. Springer, Cham (2015). https://doi.org/10.1007/978-3-319-24574-4_28
Ruggeri, A., et al.: A system for the automatic estimation of morphometric parameters of corneal endothelium in Alizarine red-stained images. Br. J. Ophthalmol. 94(5), 643–647 (2010)
Selig, B., et al.: Fully automatic evaluation of the corneal endothelium from in vivo confocal microscopy. BMC Med. Imaging 15(1), 1–15 (2015)
Taha, A.A., Hanbury, A.: Metrics for evaluating 3D medical image segmentation: analysis, selection, and tool. BMC Med. Imaging 15(1), 1–28 (2015)
Wang, L., et al.: Medical matting: a new perspective on medical segmentation with uncertainty. In: Proceedings of MICCAI, pp. 573–583 (2021)
Xie, Y., et al.: Uncertainty-aware cascade network for ultrasound image segmentation with ambiguous boundary. In: Proceedings of MICCAI, pp. 268–278 (2022)
Yang, H., et al.: Uncertainty-guided lung nodule segmentation with feature-aware attention. In: Proceedings of MICCAI, pp. 44–54 (2022)
Zhang, Y., et al.: A multi-branch hybrid transformer network for corneal endothelial cell segmentation. In: Proceedings of MICCAI, pp. 99–108 (2021)
Zhao, Y., et al.: Automated tortuosity analysis of nerve fibers in corneal confocal microscopy. IEEE Trans. Med. Imaging 39(9), 2725–2737 (2020)
Zhou, L., et al.: D-linknet: linknet with pretrained encoder and dilated convolution for high resolution satellite imagery road extraction. In: Proceedings of CVPR, pp. 182–186 (June 2018)
Acknowledgements
This work was supported in part by General Program of National Natural Science Foundation of China (Grant No. 82272086), Guangdong Provincial Department of Education (Grant No. 2020ZDZX3043), Shenzhen Natural Science Fund (JCYJ20200109140820699), the Stable Support Plan Program (20200925174052004), the National Research Foundation, Singapore under its AI Singapore Programme (AISG Award No: AISG2-TC-2021-003), and A*STAR Advanced Manufacturing and Engineering (AME) Programmatic Fund (A20H4b0141) Central Research Fund (CRF).
Author information
Authors and Affiliations
Corresponding authors
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2023 The Author(s), under exclusive license to Springer Nature Switzerland AG
About this paper
Cite this paper
Zhang, Y. et al. (2023). Elongated Physiological Structure Segmentation via Spatial and Scale Uncertainty-Aware Network. In: Greenspan, H., et al. Medical Image Computing and Computer Assisted Intervention – MICCAI 2023. MICCAI 2023. Lecture Notes in Computer Science, vol 14223. Springer, Cham. https://doi.org/10.1007/978-3-031-43901-8_31
Download citation
DOI: https://doi.org/10.1007/978-3-031-43901-8_31
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-031-43900-1
Online ISBN: 978-3-031-43901-8
eBook Packages: Computer ScienceComputer Science (R0)
