Abstract
Deep learning (DL) methods typically require large datasets to effectively learn data distributions. However, in the medical field, data is often limited in quantity, and acquiring labeled data can be costly. To mitigate this data scarcity, data augmentation techniques are commonly employed. Among these techniques, generative models play a pivotal role in expanding datasets. However, when it comes to ultrasound (US) imaging, the authenticity of generated data often diminishes due to the oversight of ultrasound physics.
We propose a novel approach to improve the quality of generated US images by introducing a physics-based diffusion model that is specifically designed for this image modality. The proposed model incorporates an US-specific scheduler scheme that mimics the natural behavior of sound wave propagation in ultrasound imaging. Our analysis demonstrates how the proposed method aids in modeling the attenuation dynamics in US imaging. We present both qualitative and quantitative results based on standard generative model metrics, showing that our proposed method results in overall more plausible images. Our code is available at github.com/marinadominguez/diffusion-for-us-images.
M. Domínguez and Y. Velikova—Shared first authorship.
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
Asgariandehkordi, H., Goudarzi, S., Basarab, A., Rivaz, H.: Deep ultrasound denoising using diffusion probabilistic models (2023)
Bi, Y., Jiang, Z., Clarenbach, R., Ghotbi, R., Karlas, A., Navab, N.: Mi-segnet: Mutual information-based us segmentation for unseen domain generalization (03 2023)
Detlefsen, N.S., Borovec, J., Schock, J., Harsh, A., Koker, T., Di Liello, L., Stancl, D., Quan, C., Grechkin, M., Falcon, W.: TorchMetrics - Measuring Reproducibility in PyTorch (Feb 2022). https://doi.org/10.21105/joss.04101, https://www.pytorchlightning.ai, repository code available at https://github.com/Lightning-AI/torchmetrics
Dhariwal, P., Nichol, A.: Diffusion models beat gans on image synthesis (2021)
Goudarzi, S., Rivaz, H.: Deep ultrasound denoising without clean data. In: Medical Imaging 2023: Ultrasonic Imaging and Tomography. vol. 12470, pp. 131–136. SPIE (2023)
Grogan, S., Mount, C.: Ultrasound physics and instrumentation (Mar 2023), updated 2023 Mar 27. In: StatPearls [Internet]. Treasure Island (FL): StatPearls Publishing; 2024 Jan-. Available from: https://www.ncbi.nlm.nih.gov/books/NBK570593/
Heusel, M., Ramsauer, H., Unterthiner, T., Nessler, B., Hochreiter, S.: Gans trained by a two time-scale update rule converge to a local nash equilibrium. arXiv preprint arXiv:1706.08500 (Dec 2017)
Ho, J., Jain, A., Abbeel, P.: Denoising diffusion probabilistic models (2020)
Kazerouni, A., Aghdam, E.K., Heidari, M., Azad, R., Fayyaz, M., Hacihaliloglu, I., Merhof, D.: Diffusion models for medical image analysis: A comprehensive survey (2022)
Kebaili, A., Lapuyade-Lahorgue, J., Ruan, S.: Deep learning approaches for data augmentation in medical imaging: A review. Journal of Imaging (2023), correspondence: su.ruan@univ-rouen.fr
Kroenke, M., Eilers, C., Dimova, D., Köhler, M., Buschner, G., Schweiger, L., Konstantinidou, L., Makowski, M., Nagarajah, J., Navab, N., Weber, W., Wendler, T.: Tracked 3d ultrasound and deep neural network-based thyroid segmentation reduce interobserver variability in thyroid volumetry. PLOS ONE 17, e0268550 (07 2022). https://doi.org/10.1371/journal.pone.0268550
Leclerc, S., Smistad, E., Pedrosa, J., Østvik, A., Cervenansky, F., Espinosa, F., Espeland, T., Berg, E.A.R., Jodoin, P.M., Grenier, T., Lartizien, C., D’hooge, J., Løvstakken, L., Bernard, O.: Deep learning for segmentation using an open large-scale dataset in 2d echocardiography. IEEE Transactions on Medical Imaging 38, 2198–2210 (2019), https://api.semanticscholar.org/CorpusID:73510235
Luo, C.: Understanding diffusion models: A unified perspective (2022)
Nichol, A., Dhariwal, P.: Improved denoising diffusion probabilistic models (2021)
Rodriguez-Molares, A., Rindal, O., D’hooge, J., Masoy, S., Austeng, A., Lediju Bell, M., Torp, H.: The generalized contrast-to-noise ratio: A formal definition for lesion detectability. IEEE Trans Ultrason Ferroelectr Freq Control 67(4), 745–759 (Apr 2020). https://doi.org/10.1109/TUFFC.2019.2956855, epub 2019 Nov 29. PMID: 31796398; PMCID: PMC8354776
van de Schaft, V., van Sloun, R.J.: Ultrasound speckle suppression and denoising using mri-derived normalizing flow priors. arXiv preprint arXiv:2112.13110 (2021)
Seitzer, M.: pytorch-fid: FID Score for PyTorch. https://github.com/mseitzer/pytorch-fid (August 2020), version 0.3.0
Stojanovski, D., Hermida, U., Lamata, P., Beqiri, A., Gomez, A.: Echo from noise: Synthetic ultrasound image generation using diffusion models for real image segmentation (2022)
Szabo, T.L.: Essentials of Ultrasound Imaging. Elsevier (2021)
Tang, F., Ding, J., Wang, L., Xian, M., Ning, C.: Multi-level global context cross consistency model for semi-supervised ultrasound image segmentation with diffusion model (2023)
Tirindelli, M., Eilers, C., Simson, W., Paschali, M., Azampour, M.F., Navab, N.: Rethinking ultrasound augmentation: A physics-inspired approach. In: Medical Image Computing and Computer Assisted Intervention–MICCAI 2021: 24th International Conference, Strasbourg, France, September 27–October 1, 2021, Proceedings, Part VIII 24. pp. 690–700. Springer (2021)
Velikova, Y., Azampour, M.F., Simson, W., Gonzalez Duque, V., Navab, N.: Lotus: learning to optimize task-based us representations. In: International Conference on Medical Image Computing and Computer-Assisted Intervention. pp. 435–445. Springer Nature Switzerland Cham (2023)
Velikova, Y., Simson, W., Azampour, M.F., Paprottka, P., Navab, N.: Cactuss: Common anatomical ct-us space for us examinations. International Journal of Computer Assisted Radiology and Surgery pp. 1–9 (2024)
Wang, W., Bao, J., Zhou, W., Chen, D., Chen, D., Yuan, L., Li, H.: Semantic image synthesis via diffusion models (2022)
Zhang, L., Zhang, J.: Ultrasound image denoising using generative adversarial networks with residual dense connectivity and weighted joint loss. PeerJ Computer Science 8, e873 (2022)
Zhang, R., Isola, P., Efros, A.A., Shechtman, E., Wang, O.: The unreasonable effectiveness of deep features as a perceptual metric. In: CVPR (2018)
Acknowledgments
We thank the team at ImFusion (ImFusion GmbH, Munich, Germany) for providing their software to us.
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Ethics declarations
Disclosure of Interests
The authors have no competing interests to declare that are relevant to the content of this article.
1 Electronic supplementary material
Below is the link to the electronic supplementary material.
Rights and permissions
Copyright information
© 2024 The Author(s), under exclusive license to Springer Nature Switzerland AG
About this paper
Cite this paper
Domínguez, M., Velikova, Y., Navab, N., Azampour, M.F. (2024). Diffusion as Sound Propagation: Physics-Inspired Model for Ultrasound Image Generation. In: Linguraru, M.G., et al. Medical Image Computing and Computer Assisted Intervention – MICCAI 2024. MICCAI 2024. Lecture Notes in Computer Science, vol 15004. Springer, Cham. https://doi.org/10.1007/978-3-031-72083-3_57
Download citation
DOI: https://doi.org/10.1007/978-3-031-72083-3_57
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-031-72082-6
Online ISBN: 978-3-031-72083-3
eBook Packages: Computer ScienceComputer Science (R0)
