Abstract
In this paper, we propose an approach for realtime optical flow estimation in ultrasound sequences of vein and arteries based on knowledge distillation. Knowledge distillation is a technique to train a faster, smaller model by learning from cues of larger models. Mobile devices with limited resources could be key in providing effective point-of-care healthcare and motivate the search of more lightweight solutions in the deep learning based image analysis. For ultrasound video analysis, motion correspondences of image contents (anatomies) have to be computed for temporal context and for real time application, fast solutions are required. We use a PWC-Net’s [1] optical flow estimation output to create soft targets to train a PDD-Net [2] as lightweight optical flow estimator. We analyse the students’ performance on the challenging task of fast segmentation propagation of vein and arteries in ultrasound images. Experiments show that even though we did not fine-tune the teachers on this task, a model trained with soft targets outperformed a model trained directly with labels and without a teacher.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Sun, D., Yang, X., Liu, M.-Y., Kautz, J.: PWC-Net: CNNs for optical flow using pyramid, warping, and cost volume. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 8934–8943 (2018)
Heinrich, M.P.: Closing the gap between deep and conventional image registration using probabilistic dense displacement networks. In: Shen, D., et al. (eds.) MICCAI 2019. LNCS, vol. 11769, pp. 50–58. Springer, Cham (2019). https://doi.org/10.1007/978-3-030-32226-7_6
Yao, R., Lin, G., Xia, S., Zhao, J., Zhou, Y.: Video object segmentation and tracking: a survey. ACM Trans. Intell. Syst. Technol. 11 (2020)
Klein, S., Staring, M., Murphy, K., Viergever, M.A., Pluim, J.P.: Elastix: a toolbox for intensity-based medical image registration. IEEE Trans. Med. Imaging 29(1), 196–205 (2009)
Mok, T.C.W., Chung, A.C.S.: Conditional deformable image registration with convolutional neural network. In: De Bruijne, M., et al. (eds.) MICCAI 2021. LNCS, vol. 12904, pp. 35–45. Springer, Cham (2021). https://doi.org/10.1007/978-3-030-87202-1_4
Hu, Y., et al.: Weakly-supervised convolutional neural networks for multimodal image registration. Med. Image Anal. 49, 1–13 (2018)
Noble, J.A.: Reflections on ultrasound image analysis (2016)
De Luca, V., et al.: Evaluation of 2d and 3d ultrasound tracking algorithms and impact on ultrasound-guided liver radiotherapy margins. Med. Phys. 45(11), 4986–5003 (2018)
Liu, F., Liu, D., Tian, J., Xie, X., Yang, X., Wang, K.: Cascaded one-shot deformable convolutional neural networks: developing a deep learning model for respiratory motion estimation in ultrasound sequences. Med. Image Anal. 65, 101793 (2020)
Tanno, R.: AutoDVT: joint real-time classification for vein compressibility analysis in deep vein thrombosis ultrasound diagnostics. In: Frangi, A.F., Schnabel, J.A., Davatzikos, C., Alberola-López, C., Fichtinger, G. (eds.) MICCAI 2018. LNCS, vol. 11071, pp. 905–912. Springer, Cham (2018). https://doi.org/10.1007/978-3-030-00934-2_100
Dosovitskiy, A., et al.: Flownet: learning optical flow with convolutional networks. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2758–2766 (2015)
Ilg, E., Mayer, N., Saikia, T., Keuper, M., Dosovitskiy, A., Brox, T.: FlowNet 2.0: evolution of optical flow estimation with deep networks. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 2462–2470 (2017)
Mok, T.C.W., Chung, A.C.S.: Large deformation diffeomorphic image registration with Laplacian pyramid networks. In: Martel, A.L., et al. (eds.) MICCAI 2020. LNCS, vol. 12263, pp. 211–221. Springer, Cham (2020). https://doi.org/10.1007/978-3-030-59716-0_21
Li, J., Zhao, R., Huang, J.-T., Gong, Y.: Learning small-size DNN with output-distribution-based criteria. In: Fifteenth Annual Conference of the International Speech Communication Association (2014)
Yuan, L., Tay, F.E., Li, G., Wang, T., Feng, J. : Revisiting knowledge distillation via label smoothing regularization. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 3903–3911 (2020)
Kim, T., Oh, J., Kim, N., Cho, S., Yun, S.Y.: Comparing kullback-leibler divergence and mean squared error loss in knowledge distillation, arXiv preprint arXiv:2105.08919 (2021)
Hering, A., et al.: Learn2Reg: comprehensive multi-task medical image registration challenge, dataset and evaluation in the era of deep learning, arXiv preprint arXiv:2112.04489 (2021)
Heinrich, M.P., Oktay, O., Bouteldja, N.: Obelisk-net: fewer layers to solve 3d multi-organ segmentation with sparse deformable convolutions. Med. Image Anal. 54, 1–9 (2019)
Krähenbühl, P., Koltun, V.: Efficient inference in fully connected CRFs with gaussian edge potentials. Adv. Neural Inf. Process. Syst. 24, 109–117 (2011)
Reinke, A., et al.: Common limitations of image processing metrics: a picture story, arXiv preprint arXiv:2104.05642 (2021)
Hofstätter, S., Althammer, S., Schröder, M., Sertkan, M., Hanbury, A.: Improving efficient neural ranking models with cross-architecture knowledge distillation, arXiv preprint arXiv:2010.02666 (2020)
Isensee, F., Jaeger, P.F., Kohl, S.A., Petersen, J., Maier-Hein, K.H.: nnU-Net: a self-configuring method for deep learning-based biomedical image segmentation. Nat. Methods 18(2), 203–211 (2021)
Yan, W., Wang, Y., Li, Z., van der Geest, R.J., Tao, Q.: Left ventricle segmentation via optical-flow-net from short-axis cine MRI: preserving the temporal coherence of cardiac motion. In: Frangi, A.F., Schnabel, J.A., Davatzikos, C., Alberola-López, C., Fichtinger, G. (eds.) MICCAI 2018. LNCS, vol. 11073, pp. 613–621. Springer, Cham (2018). https://doi.org/10.1007/978-3-030-00937-3_70
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2022 The Author(s), under exclusive license to Springer Nature Switzerland AG
About this paper
Cite this paper
Nicke, T., Graf, L., Lauri, M., Mischkewitz, S., Frintrop, S., Heinrich, M.P. (2022). Realtime Optical Flow Estimation on Vein and Artery Ultrasound Sequences Based on Knowledge-Distillation. In: Hering, A., Schnabel, J., Zhang, M., Ferrante, E., Heinrich, M., Rueckert, D. (eds) Biomedical Image Registration. WBIR 2022. Lecture Notes in Computer Science, vol 13386. Springer, Cham. https://doi.org/10.1007/978-3-031-11203-4_15
Download citation
DOI: https://doi.org/10.1007/978-3-031-11203-4_15
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-031-11202-7
Online ISBN: 978-3-031-11203-4
eBook Packages: Computer ScienceComputer Science (R0)