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.
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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)
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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
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