Abstract
Recent researches on cancer segmentation in dynamic contrast enhanced magnetic resonance imaging (DCE-MRI) usually resort to the combination of temporal kinetic characteristics and deep learning to improve segmentation performance. However, the difficulty in accessing complete temporal sequences, especially post-contrast images, hinders segmentation performance, generalization ability and clinical application of existing methods. In this work, we propose a diffusion kinetic model (DKM) that implicitly exploits hemodynamic priors in DCE-MRI and effectively generates high-quality segmentation maps only requiring pre-contrast images. We specifically consider the underlying relation between hemodynamic response function (HRF) and denoising diffusion process (DDP), which displays remarkable results for realistic image generation. Our proposed DKM consists of a diffusion module (DM) and segmentation module (SM) so that DKM is able to learn cancer hemodynamic information and provide a latent kinetic code to facilitate segmentation performance. Once the DM is pretrained, the latent code estimated from the DM is simply incorporated into the SM, which enables DKM to automatically and accurately annotate cancers with pre-contrast images. To our best knowledge, this is the first work exploring the relationship between HRF and DDP for dynamic MRI segmentation. We evaluate the proposed method for tumor segmentation on public breast cancer DCE-MRI dataset. Compared to the existing state-of-the-art approaches with complete sequences, our method yields higher segmentation performance even with pre-contrast images. The source code will be available on https://github.com/Medical-AI-Lab-of-JNU/DKM.
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References
Fan, M., Xia, P., Clarke, R., Wang, Y., Li, L.: Radiogenomic signatures reveal multiscale intratumour heterogeneity associated with biological functions and survival in breast cancer. Nat. Commun. 11(1), 4861 (2020)
Vidal, J., Vilanova, J.C., Martí, R., et al.: A U-net ensemble for breast lesion segmentation in DCE MRI. Comput. Biol. Med. 140, 105093 (2022)
Qiao, M., et al.: Three-dimensional breast tumor segmentation on DCE-MRI with a multilabel attention-guided joint-phase-learning network. Comput. Med. Imaging Graph. 90, 101909 (2021)
Nalepa, J., et al.: Fully-automated deep learning-powered system for DCE-MRI analysis of brain tumors. Artif. Intell. Med. 102, 101769 (2020)
Lv, T., et al.: A hybrid hemodynamic knowledge-powered and feature reconstruction-guided scheme for breast cancer segmentation based on dce-mri. Med. Image Anal. 82, 102572 (2022)
Wang, S., et al.: Breast tumor segmentation in DCE-MRI with tumor sensitive synthesis. IEEE Trans. Neural Netw. Learn. Syst. (2021)
Rombach, R., Blattmann, A., Lorenz, D., Esser, P., Ommer, B.: High-resolution image synthesis with latent diffusion models. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 10684–10695 (2022)
Ho, J., Jain, A., Abbeel, P.: Denoising diffusion probabilistic models. Adv. Neural. Inf. Process. Syst. 33, 6840–6851 (2020)
Baranchuk, D., Rubachev, I., Voynov, A., Khrulkov, V., Babenko, A.: Label-efficient semantic segmentation with diffusion models. In: International Conference on Learning Representation (ICLR) (2022)
Fernandez, V., et al.: Can segmentation models be trained with fully synthetically generated data? In: Zhao, C., Svoboda, D., Wolterink, J.M., Escobar, M. (eds.) SASHIMI 2022. LNCS, vol. 13570, pp. 79–90. Springer, Cham (2022). https://doi.org/10.1007/978-3-031-16980-9_8
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)
Junde, W., et al.: Medsegdiff: medical image segmentation with diffusion probabilistic model (2023)
Newitt, D., Hylton, N.: Single site breast DCE-MRI data and segmentations from patients undergoing neoadjuvant chemotherapy. Cancer Imaging Arch. 2 (2016)
Kim, B., Ye, J.C.: Diffusion deformable model for 4D temporal medical image generation. In: Wang, L., Dou, Q., Fletcher, P.T., Speidel, S., Li, S. (eds.) MICCAI 2022. LNCS, vol. 13431, pp. 539–548. Springer, Cham (2022). https://doi.org/10.1007/978-3-031-16431-6_51
Wang, Z., Bovik, A.C., Sheikh, H.R., Simoncelli, E.P.: Image quality assessment: from error visibility to structural similarity. IEEE Trans. Image Process. 13(4), 600–612 (2004)
Lin, J., Lin, H., Zhang, Z., Yiwen, X., Zhao, T.: SSIM-variation-based complexity optimization for versatile video coding. IEEE Signal Process. Lett. 29, 2617–2621 (2022)
Fu, J., et al.: Dual attention network for scene segmentation. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 3146–3154 (2019)
Ibtehaz, N., Rahman, M.S.: Multiresunet: rethinking the U-net architecture for multimodal biomedical image segmentation. Neural Netw. 121, 74–87 (2020)
Zhou, Y., et al.: Multi-task learning for segmentation and classification of tumors in 3D automated breast ultrasound images. Med. Image Anal. 70, 101918 (2021)
Denner, S., et al.: Spatio-temporal learning from longitudinal data for multiple sclerosis lesion segmentation. In: Crimi, A., Bakas, S. (eds.) BrainLes 2020. LNCS, vol. 12658, pp. 111–121. Springer, Cham (2021). https://doi.org/10.1007/978-3-030-72084-1_11
Khaled, R., Vidal, J., Martí, R.: Deep learning based segmentation of breast lesions in DCE-MRI. In: Del Bimbo, A., et al. (eds.) ICPR 2021. LNCS, vol. 12661, pp. 417–430. Springer, Cham (2021). https://doi.org/10.1007/978-3-030-68763-2_32
Acknowledgements
This work is supported in part by the National Key R &D Program of China under Grants 2021YFE0203700 and 2018YFA0701700, the Postgraduate Research & Practice Innovation Program of Jiangsu Province KYCX23_2524, and is supported by National Natural Science Foundation of China grants 61602007, U21A20521 and 61731008, Zhejiang Provincial Natural Science Foundation of China (LZ15F010001), Jiangsu Provincial Maternal and Child Health Research Project (F202034), Wuxi Health Commission Precision Medicine Project (J202106), Jiangsu Provincial Six Talent Peaks Project (YY-124), and the Science and Technology Development Fund, Macau SAR (File no. 0004/2019/AFJ and 0011/2019/AKP).
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Lv, T., Liu, Y., Miao, K., Li, L., Pan, X. (2023). Diffusion Kinetic Model for Breast Cancer Segmentation in Incomplete DCE-MRI. 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_10
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DOI: https://doi.org/10.1007/978-3-031-43901-8_10
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