Abstract
High resolution images provided by Ultra High Frequency Ultrasound (UHFUS) scanners permit the vessel-based measurement of the Intimal-Media Thickness (IMT) in small vessels, such as those in the hand. However, it is challenging to precisely determine vessels in UHFUS sequences due to severe speckle noise obfuscating their boundaries. Current level set-based approaches are unable to identify poorly delineated boundaries and are not robust against varying speckle noise. While recent neural network-based methods, including recurrent neural networks, have shown promise at segmenting vessel contours, they are application specific and do not generalize to datasets acquired from different scanners, such as a traditional High Frequency Ultrasound (HFUS) machine, with different scan settings. Our goal for a segmentation approach was the accurate localization of vessel contours, and generalization to new data within and across biomedical imaging modalities. In this paper, we propose a novel ultrasound vessel segmentation network (USVS-Net) architecture that assimilates features extracted at different scales using Convolutional Long Short Term Memory (ConvLSTM) and segments vessel boundaries accurately. We show the results of our approach on UHFUS and HFUS sequences. To show broader applicability beyond US, we also trained and tested our approach on a Chest X-Ray dataset. To the best of our knowledge, this is the first learning-based approach to segment deforming vessel contours in both UHFUS and HFUS sequences.
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
Mohler III, E.R., et al.: High frequency ultrasound for evaluation of intimal thickness. J. Am. Soc. Echocardiogr. 22(10), 1129–1133 (2009)
Gorantla, V., et al.: Acute and chronic rejection in upper extremity transplantation: what have we learned? Hand Clin. 27(4), 481–493 (2011)
Menchon-Lara, R.M., et al.: Fully automatic segmentation of ultrasound common carotid artery images based on machine learning. Neurocomputing 151(1), 161–167 (2015)
Shin, J.Y., et al.: Automating carotid intima-media thickness video interpretation with convolutional neural networks. In: CVPR, pp. 2526–2535 (2016)
Chaniot, J., et al.: Vessel segmentation in high-frequency 2D/3D ultrasound images. In: IEEE International Ultrasonics Symposium, pp. 1–4 (2016)
Mathai, T.S., Jin, L., Gorantla, V., Galeotti, J.: Fast vessel segmentation and tracking in ultra high-frequency ultrasound images. In: Frangi, A.F., Schnabel, J.A., Davatzikos, C., Alberola-López, C., Fichtinger, G. (eds.) MICCAI 2018. LNCS, vol. 11073, pp. 746–754. Springer, Cham (2018). https://doi.org/10.1007/978-3-030-00937-3_85
Ronneberger, O., Fischer, P., Brox, T.: U-Net: convolutional networks for biomedical image segmentation. In: Navab, N., Hornegger, J., Wells, W.M., Frangi, A.F. (eds.) MICCAI 2015. LNCS, vol. 9351, pp. 234–241. Springer, Cham (2015). https://doi.org/10.1007/978-3-319-24574-4_28
He, K., et al.: Deep residual learning for image recognition. In: IEEE CVPR, pp. 770–778 (2016)
Koltun, V., et al.: Multi-scale context aggregation by dilated convolutions. In: ICLR (2016)
Arbelle, S., et al.: Microscopy cell segmentation via convolutional LSTM networks. In: IEEE ISBI, pp. 1008–1012 (2019)
Milletari, F., Rieke, N., Baust, M., Esposito, M., Navab, N.: CFCM: segmentation via coarse to fine context memory. In: Frangi, A.F., Schnabel, J.A., Davatzikos, C., Alberola-López, C., Fichtinger, G. (eds.) MICCAI 2018. LNCS, vol. 11073, pp. 667–674. Springer, Cham (2018). https://doi.org/10.1007/978-3-030-00937-3_76
Gao, Y., et al.: Fully convolutional structured LSTM networks for joint 4D medical image segmentation. In: IEEE ISBI, pp. 1104–1108 (2018)
Zhang, D., et al.: A multi-level convolutional LSTM model for the segmentation of left ventricle myocardium in infarcted porcine cine MR images. In: IEEE ISBI, pp. 470–473 (2018)
Zhao, C., et al.: Predicting tongue motion in unlabeled ultrasound videos using convolutional LSTM neural network. In: IEEE ICASSP, pp. 5926–5930 (2019)
Basty, N., Grau, V.: Super resolution of cardiac cine MRI sequences using deep learning. In: Stoyanov, D., et al. (eds.) RAMBO/BIA/TIA -2018. LNCS, vol. 11040, pp. 23–31. Springer, Cham (2018). https://doi.org/10.1007/978-3-030-00946-5_3
Apostolopoulos, S., De Zanet, S., Ciller, C., Wolf, S., Sznitman, R.: Pathological OCT retinal layer segmentation using branch residual U-shape networks. In: Descoteaux, M., Maier-Hein, L., Franz, A., Jannin, P., Collins, D.L., Duchesne, S. (eds.) MICCAI 2017. LNCS, vol. 10435, pp. 294–301. Springer, Cham (2017). https://doi.org/10.1007/978-3-319-66179-7_34
Mathai, T.S., et al.: Learning to segment corneal tissue interfaces in OCT images. In: IEEE ISBI, pp. 1432–1436 (2019)
Jaeger, S., et al.: Two public chest X-ray datasets for computer-aided screening of pulmonary diseases. Quant. Imaging Med Surg. 4(6), 475–477 (2014)
Kingma, D., et al.: Adam: a method for stochastic optimization. In: ICLR (2015)
Acknowledgements
These awards helped us in gathering data and designing initial algorithms: NIH 1R01EY021641, DOD awards W81XWH-14-1-0371 and W81XWH-14-1-0370, NVIDIA Corporation GPU donations, Carnegie Mellon Center for Machine Learning in Health (CMLH). Patent pending, US 62/860,392.
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2019 Springer Nature Switzerland AG
About this paper
Cite this paper
Mathai, T.S., Gorantla, V., Galeotti, J. (2019). Segmentation of Vessels in Ultra High Frequency Ultrasound Sequences Using Contextual Memory. In: Shen, D., et al. Medical Image Computing and Computer Assisted Intervention – MICCAI 2019. MICCAI 2019. Lecture Notes in Computer Science(), vol 11765. Springer, Cham. https://doi.org/10.1007/978-3-030-32245-8_20
Download citation
DOI: https://doi.org/10.1007/978-3-030-32245-8_20
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-030-32244-1
Online ISBN: 978-3-030-32245-8
eBook Packages: Computer ScienceComputer Science (R0)
