Abstract
Denoising diffusion probabilistic models (DDPM) for medical image segmentation are still a challenging task due to the lack of the ability to parse the reliability of multi-modality medical images. In this paper, we propose a novel evidence-identified DDPM (EI-DDPM) with contextual discounting for tumor segmentation by integrating multi-modality medical images. Advanced compared to previous work, the EI-DDPM deploys the DDPM-based framework for segmentation tasks under the condition of multi-modality medical images and parses the reliability of multi-modality medical images through contextual discounted evidence theory. We apply EI-DDPM on a BraTS 2021 dataset with 1251 subjects and a liver MRI dataset with 238 subjects. The extensive experiment proved the superiority of EI-DDPM, which outperforms the state-of-the-art methods.
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
References
Baid, U., et al.: The rsna-asnr-miccai brats 2021 benchmark on brain tumor segmentation and radiogenomic classification. arXiv preprint arXiv:2107.02314 (2021)
Bakas, S., et al.: Advancing the cancer genome atlas glioma MRI collections with expert segmentation labels and radiomic features. Sci. Data 4(1), 1–13 (2017)
Balogh, J., et al.: Hepatocellular carcinoma: a review. Journal of hepatocellular carcinoma, pp. 41–53 (2016)
Baltrušaitis, T., Ahuja, C., Morency, L.P.: Multimodal machine learning: a survey and taxonomy. IEEE Trans. Pattern Anal. Mach. Intell. 41(2), 423–443 (2018)
Chen, J., et al.: Transunet: transformers make strong encoders for medical image segmentation. arXiv preprint arXiv:2102.04306 (2021)
Denoeux, T., Kanjanatarakul, O., Sriboonchitta, S.: A new evidential k-nearest neighbor rule based on contextual discounting with partially supervised learning. Int. J. Approximate Reasoning 113, 287–302 (2019)
Ho, J., Jain, A., Abbeel, P.: Denoising diffusion probabilistic models. Adv. Neural. Inf. Process. Syst. 33, 6840–6851 (2020)
Huang, L., Ruan, S., Decazes, P., Denœux, T.: Lymphoma segmentation from 3d PET-CT images using a deep evidential network. Int. J. Approximate Reasoning 149, 39–60 (2022)
Kim, B., Oh, Y., Ye, J.C.: Diffusion adversarial representation learning for self-supervised vessel segmentation. arXiv preprint arXiv:2209.14566 (2022)
Lian, C., Ruan, S., Denoeux, T., Li, H., Vera, P.: Joint tumor segmentation in PET-CT images using co-clustering and fusion based on belief functions. IEEE Trans. Image Process. 28(2), 755–766 (2018)
Lim, K.Y., Mandava, R.: A multi-phase semi-automatic approach for multisequence brain tumor image segmentation. Expert Syst. Appl. 112, 288–300 (2018)
Menze, B.H., et al.: The multimodal brain tumor image segmentation benchmark (brats). IEEE Trans. Med. Imaging 34(10), 1993–2024 (2014)
Mercier, D., Quost, B., Denœux, T.: Refined modeling of sensor reliability in the belief function framework using contextual discounting. Inf. Fusion 9(2), 246–258 (2008)
Nichol, A.Q., Dhariwal, P.: Improved denoising diffusion probabilistic models. In: International Conference on Machine Learning, pp. 8162–8171. PMLR (2021)
Qu, T., et al.: M3net: a multi-scale multi-view framework for multi-phase pancreas segmentation based on cross-phase non-local attention. Med. Image Anal. 75, 102232 (2022)
Raju, A., et al.: Co-heterogeneous and adaptive segmentation from multi-source and multi-phase ct imaging data: a study on pathological liver and lesion segmentation. In: European Conference on Computer Vision. pp. 448–465. Springer (2020)
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
Shafer, G.: A mathematical theory of evidence, vol. 42. Princeton University Press (1976)
Wang, J., et al.: Tensor-based sparse representations of multi-phase medical images for classification of focal liver lesions. Pattern Recogn. Lett. 130, 207–215 (2020)
Wolleb, J., Sandkühler, R., Bieder, F., Valmaggia, P., Cattin, P.C.: Diffusion models for implicit image segmentation ensembles. In: International Conference on Medical Imaging with Deep Learning, pp. 1336–1348. PMLR (2022)
Zhang, L., et al.: Robust pancreatic ductal adenocarcinoma segmentation with multi-institutional multi-phase partially-annotated CT scans. In: Martel, A.L., et al. (eds.) MICCAI 2020. LNCS, vol. 12264, pp. 491–500. Springer, Cham (2020). https://doi.org/10.1007/978-3-030-59719-1_48
Zhao, J., et al.: United adversarial learning for liver tumor segmentation and detection of multi-modality non-contrast MRI. Med. Image Anal. 73, 102154 (2021)
Zhou, Y., et al.: Hyper-pairing network for multi-phase pancreatic ductal adenocarcinoma segmentation. In: Shen, D., et al. (eds.) MICCAI 2019. LNCS, vol. 11765, pp. 155–163. Springer, Cham (2019). https://doi.org/10.1007/978-3-030-32245-8_18
Acknowledgements
This work is partly supported by the China Scholarship Council (No. 202008370191)
Author information
Authors and Affiliations
Corresponding author
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
Zhao, J., Li, S. (2023). Learning Reliability of Multi-modality Medical Images for Tumor Segmentation via Evidence-Identified Denoising Diffusion Probabilistic Models. 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_65
Download citation
DOI: https://doi.org/10.1007/978-3-031-43901-8_65
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)
