Abstract
Recent improvements in deep learning have brought great progress in ultrasonic lesion quantification. However, the learning-based scheme performs properly only when a certain level of similarity between train and test condition is ensured. However, real-world test condition expects diverse untrained probe geometry from various manufacturers, which undermines the credibility of learning-based ultrasonic approaches. In this paper, we present a meta-learned deformable sensor generalization network that generates consistent attenuation coefficient (AC) image regardless of the probe condition. The proposed method was assessed through numerical simulation and in-vivo breast patient measurements. The numerical simulation shows that the proposed network outperforms existing state-of-the-art domain generalization methods for the AC reconstruction under unseen probe conditions. In in-vivo studies, the proposed network provides consistent AC images irrespective of various probe conditions and demonstrates great clinical potential in differential breast cancer diagnosis.
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
Arribas, E.M., Whitman, G.J., De, B.N.: 2016 screening breast ultrasound: where are we today? Curr. Breast Cancer Rep. 8, 221–9 (2016)
Goss, S.A., Johnston, R.L., Dunn, F.: Comprehensive compilation of empirical ultrasonic properties of mammalian tissues. J. Acoust. Soc. Am. 64, 423–457 (1978)
Li, C., Duric, N., Littrup, P., Huang, L.: In vivo breast sound-speed imaging with ultrasound tomography. Ultrasound Med. Biol. 35(10), 1615–1628 (2009)
Nam, K., Zagzebski, J.A., Hall, T.J.: Quantitative assessment of in vivo breast masses using ultrasound attenuation and backscatter. Ultras. Imaging. 35, 46–61 (2013)
Sanabria, S.J., Ozkan, E., Rominger, M., Goksel, O.: Spatial domain reconstruction for imaging speed-of-sound with pulse-echo ultrasound: simulation and in vivo study. Phys. Med. Biol. 63(21), 215015 (2018)
Rau, R., Unal, O., Schweizer, D., Vishnevskiy, V., Goksel, O.: Attenuation imaging with pulse-echo ultrasound based on an acoustic reflector. In: Shen, D., et al. (eds.) MICCAI 2019. LNCS, vol. 11768, pp. 601–609. Springer, Cham (2019). https://doi.org/10.1007/978-3-030-32254-0_67
Feigin, M., Zwecker, M., Freedman, D., Anthony, B.W.: Detecting muscle activation using ultrasound speed of sound inversion with deep learning. In: 2020 42nd Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), pp. 2092–2095. IEEE (2020)
Oh, S.H., Kim, M.-G., Kim, Y., Kwon, H., Bae, H.-M.: A neural framework for multi-variable lesion quantification through B-mode style transfer. In: de Bruijne, M., et al. (eds.) MICCAI 2021. LNCS, vol. 12906, pp. 222–231. Springer, Cham (2021). https://doi.org/10.1007/978-3-030-87231-1_22
Wang, J., Lan, C., Liu, C., Ouyang, Y., Qin, T.: Generalizing to unseen domains: a survey on domain generalization. arXiv preprint (2021). arXiv:2103.03097
Ahmed, S., Kamal, U., Hassan., K.: SWE-Net: a deep learning approach for shear wave elastography and lesion segmentation using single push acoustic radiation force Ultrasonics, 110 (2021)
Shen, L., Zhao, W., Capaldi, D., Pauly, J., Xing, L.: A geometry-informed deep learning framework for ultra-sparse 3D tomographic image reconstruction. arXiv preprint arXiv:2105.11692 (2021)
Oh, S., Kim, M. -G., Kim, Y., Bae, H.-M.: A learned representation for multi variable ultrasound lesion quantification. In: ISBI, pp. 1177–1181. IEEE (2021)
Mast, T.D.: Empirical relationships between acoustic parameters in human soft tissues. Acoust. Res. Lett. Online 1(37), 37–43 (2000)
Balakrishnan, G., Zhao, A., Sabuncu, M.R., Guttag, J., Dalca, A.V.: Voxelmorph: a learning framework for deformable medical image registration. IEEE TMI (2019)
Jaderberg, M., Karen, S., Andrew, Z.: Jaderberg, M., et al.: Spatial transformer networks. In: NIPS, pp. 2017–2025 (2015)
Wang, J., et al.: Deep high-resolution representation learning for visual recognition. IEEE Trans. Pattern Anal. Mach. Intell. (2020)
Li, D., Yang, Y., Song, Y.Z., Hospedales, T.M.: Learning to generalize: meta-learning for domain generalization. In: AAAI (2018)
Balaji, Y., Sankaranarayanan, S., Chellappa, R.: Metareg: towards domain generalization using meta-regularization. In: NeurIPS, pp. 998–1008 (2018)
Krogh, A., Hertz, J.A.: A simple weight decay can improve generalization. In: Advances in Neural Information Processing Systems, pp. 950–957 (1992)
Kingma, D., Ba, J.: Adam: a method for stochastic optimization. In: ICLR (2014)
Ghifary, M., Bastiaan Kleijn, W., Zhang, M., Balduzzi, D.: Domain generalization for object recognition with multi-task autoencoders. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2551–2559 (2015)
Li, D., Zhang, J., Yang, Y., Liu, C., Song, Y.Z., Hospedales, T.M.: Episodic training for domain generalization. arXiv preprint arXiv:1902.00113 (2019)
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
1 Electronic supplementary material
Below is the link to the electronic supplementary material.
Rights and permissions
Copyright information
© 2022 The Author(s), under exclusive license to Springer Nature Switzerland AG
About this paper
Cite this paper
Oh, S., Kim, MG., Kim, Y., Jung, G., Kwon, H., Bae, HM. (2022). Sensor Geometry Generalization to Untrained Conditions in Quantitative Ultrasound Imaging. In: Wang, L., Dou, Q., Fletcher, P.T., Speidel, S., Li, S. (eds) Medical Image Computing and Computer Assisted Intervention – MICCAI 2022. MICCAI 2022. Lecture Notes in Computer Science, vol 13436. Springer, Cham. https://doi.org/10.1007/978-3-031-16446-0_74
Download citation
DOI: https://doi.org/10.1007/978-3-031-16446-0_74
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-031-16445-3
Online ISBN: 978-3-031-16446-0
eBook Packages: Computer ScienceComputer Science (R0)
