Abstract
Kidney cancer occurrence increases since 1990’s and its main treatment is surgery. According to this, performing automatic segmentation is an important tool to develop. In this paper, we used a two stages pipeline to get the segmentation of kidney, tumor and cyst. The first stage is used to segment the kidney region to allow us to crop the data. The second stage leverages uncertainty using Monte-Carlo dropout during training by introducing an uncertainty estimate term in the loss function.
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
Arega, T.W., Bricq, S., Legrand, F., Jacquier, A., Lalande, A., Meriaudeau, F.: Automatic uncertainty-based quality controlled T1 mapping and ECV analysis from native and post-contrast cardiac T1 mapping images using Bayesian vision transformer. Med. Image Anal. 86, 102773 (2023). https://doi.org/10.1016/j.media.2023.102773
Arega, T.W., Bricq, S., Meriaudeau, F.: Leveraging uncertainty estimates to improve segmentation performance in cardiac MR. In: Sudre, C.H., et al. (eds.) UNSURE/PIPPI -2021. LNCS, vol. 12959, pp. 24–33. Springer, Cham (2021). https://doi.org/10.1007/978-3-030-87735-4_3
Arega, T.W., Bricq, S., Meriaudeau, F.: Using polynomial loss and uncertainty information for robust left atrial and scar quantification and segmentation. In: Zhuang, X., Li, L., Wang, S., Wu, F. (eds.) LAScarQS 2022. LNCS, vol. 13586, pp. 133–144. Springer, Cham (2022). https://doi.org/10.1007/978-3-031-31778-1_13
Blundell, C., Cornebise, J., Kavukcuoglu, K., Wierstra, D.: Weight uncertainty in neural network. In: International Conference on Machine Learning, pp. 1613–1622. PMLR (2015)
Fortunato, M., Blundell, C., Vinyals, O.: Bayesian recurrent neural networks (2017). http://arxiv.org/abs/1704.02798
Isensee, F., Jaeger, P.F., Kohl, S.A.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). https://doi.org/10.1038/s41592-020-01008-z
Kendall, A., Badrinarayanan, V., Cipolla, R.: Bayesian SegNet: model uncertainty in deep convolutional encoder-decoder architectures for scene understanding (2015). http://arxiv.org/abs/1511.02680
Ng, M., et al.: Estimating uncertainty in neural networks for cardiac MRI segmentation: a benchmark study. IEEE Trans. Biomed. Eng. 70(6), 1955–1966 (2022). https://doi.org/10.1109/TBME.2022.3232730
Zhao, Z., Chen, H., Wang, L.: A coarse-to-fine framework for the 2021 kidney and kidney tumor segmentation challenge. In: Heller, N., Isensee, F., Trofimova, D., Tejpaul, R., Papanikolopoulos, N., Weight, C. (eds.) KiTS 2021. LNCS, vol. 13168, pp. 53–58. Springer, Cham (2022). https://doi.org/10.1007/978-3-030-98385-7_8
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2024 The Author(s), under exclusive license to Springer Nature Switzerland AG
About this paper
Cite this paper
Michaud, J., Arega, T.W., Bricq, S. (2024). Using Uncertainty Information for Kidney Tumor Segmentation. In: Heller, N., et al. Kidney and Kidney Tumor Segmentation. KiTS 2023. Lecture Notes in Computer Science, vol 14540. Springer, Cham. https://doi.org/10.1007/978-3-031-54806-2_8
Download citation
DOI: https://doi.org/10.1007/978-3-031-54806-2_8
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-031-54805-5
Online ISBN: 978-3-031-54806-2
eBook Packages: Computer ScienceComputer Science (R0)
