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Towards robust CT-ultrasound registration using deep learning methods. / Sun, Yuanyuan; Moelker, Adriaan; Niessen, Wiro J.; van Walsum, Theo.

Understanding and Interpreting Machine Learning in Medical Image Computing Applications - First International Workshops MLCN 2018, DLF 2018, and iMIMIC 2018, Held in Conjunction with MICCAI 2018, Proceedings. Vol. 11038 LNCS Springer Verlag, 2018. p. 43-51 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 11038 LNCS).

Research output: Chapter in Book/Report/Conference proceedingConference contributionScientificpeer-review

Harvard

Sun, Y, Moelker, A, Niessen, WJ & van Walsum, T 2018, Towards robust CT-ultrasound registration using deep learning methods. in Understanding and Interpreting Machine Learning in Medical Image Computing Applications - First International Workshops MLCN 2018, DLF 2018, and iMIMIC 2018, Held in Conjunction with MICCAI 2018, Proceedings. vol. 11038 LNCS, Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), vol. 11038 LNCS, Springer Verlag, pp. 43-51, 1st International Workshop on Machine Learning in Clinical Neuroimaging, MLCN 2018, 1st International Workshop on Deep Learning Fails, DLF 2018, and 1st International Workshop on Interpretability of Machine Intelligence in Medical Image Computing, iMIMIC 2018, held in conjunction with the 21st International Conference on Medical Imaging and Computer-Assisted Intervention, MICCAI 2018, Granada, Spain, 16/09/18. https://doi.org/10.1007/978-3-030-02628-8_5

APA

Sun, Y., Moelker, A., Niessen, W. J., & van Walsum, T. (2018). Towards robust CT-ultrasound registration using deep learning methods. In Understanding and Interpreting Machine Learning in Medical Image Computing Applications - First International Workshops MLCN 2018, DLF 2018, and iMIMIC 2018, Held in Conjunction with MICCAI 2018, Proceedings (Vol. 11038 LNCS, pp. 43-51). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 11038 LNCS). Springer Verlag. https://doi.org/10.1007/978-3-030-02628-8_5

Vancouver

Sun Y, Moelker A, Niessen WJ, van Walsum T. Towards robust CT-ultrasound registration using deep learning methods. In Understanding and Interpreting Machine Learning in Medical Image Computing Applications - First International Workshops MLCN 2018, DLF 2018, and iMIMIC 2018, Held in Conjunction with MICCAI 2018, Proceedings. Vol. 11038 LNCS. Springer Verlag. 2018. p. 43-51. (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)). https://doi.org/10.1007/978-3-030-02628-8_5

Author

Sun, Yuanyuan ; Moelker, Adriaan ; Niessen, Wiro J. ; van Walsum, Theo. / Towards robust CT-ultrasound registration using deep learning methods. Understanding and Interpreting Machine Learning in Medical Image Computing Applications - First International Workshops MLCN 2018, DLF 2018, and iMIMIC 2018, Held in Conjunction with MICCAI 2018, Proceedings. Vol. 11038 LNCS Springer Verlag, 2018. pp. 43-51 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)).

BibTeX

@inproceedings{3f99c7ae56e34b558beb48bf73830cc5,
title = "Towards robust CT-ultrasound registration using deep learning methods",
abstract = "Multi-modal registration, especially CT/MR to ultrasound (US), is still a challenge, as conventional similarity metrics such as mutual information do not match the imaging characteristics of ultrasound. The main motivation for this work is to investigate whether a deep learning network can be used to directly estimate the displacement between a pair of multi-modal image patches, without explicitly performing similarity metric and optimizer, the two main components in a registration framework. The proposed DVNet is a fully convolutional neural network and is trained using a large set of artificially generated displacement vectors (DVs). The DVNet was evaluated on mono- and simulated multi-modal data, as well as real CT and US liver slices (selected from 3D volumes). The results show that the DVNet is quite robust on the single- and multi-modal (simulated) data, but does not work yet on the real CT and US images.",
keywords = "CNN, CT, Liver, Registration, Ultrasound",
author = "Yuanyuan Sun and Adriaan Moelker and Niessen, {Wiro J.} and {van Walsum}, Theo",
year = "2018",
doi = "10.1007/978-3-030-02628-8_5",
language = "English",
isbn = "978-3-030-02627-1",
volume = "11038 LNCS",
series = "Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)",
publisher = "Springer Verlag",
pages = "43--51",
booktitle = "Understanding and Interpreting Machine Learning in Medical Image Computing Applications - First International Workshops MLCN 2018, DLF 2018, and iMIMIC 2018, Held in Conjunction with MICCAI 2018, Proceedings",

}

RIS

TY - GEN

T1 - Towards robust CT-ultrasound registration using deep learning methods

AU - Sun, Yuanyuan

AU - Moelker, Adriaan

AU - Niessen, Wiro J.

AU - van Walsum, Theo

PY - 2018

Y1 - 2018

N2 - Multi-modal registration, especially CT/MR to ultrasound (US), is still a challenge, as conventional similarity metrics such as mutual information do not match the imaging characteristics of ultrasound. The main motivation for this work is to investigate whether a deep learning network can be used to directly estimate the displacement between a pair of multi-modal image patches, without explicitly performing similarity metric and optimizer, the two main components in a registration framework. The proposed DVNet is a fully convolutional neural network and is trained using a large set of artificially generated displacement vectors (DVs). The DVNet was evaluated on mono- and simulated multi-modal data, as well as real CT and US liver slices (selected from 3D volumes). The results show that the DVNet is quite robust on the single- and multi-modal (simulated) data, but does not work yet on the real CT and US images.

AB - Multi-modal registration, especially CT/MR to ultrasound (US), is still a challenge, as conventional similarity metrics such as mutual information do not match the imaging characteristics of ultrasound. The main motivation for this work is to investigate whether a deep learning network can be used to directly estimate the displacement between a pair of multi-modal image patches, without explicitly performing similarity metric and optimizer, the two main components in a registration framework. The proposed DVNet is a fully convolutional neural network and is trained using a large set of artificially generated displacement vectors (DVs). The DVNet was evaluated on mono- and simulated multi-modal data, as well as real CT and US liver slices (selected from 3D volumes). The results show that the DVNet is quite robust on the single- and multi-modal (simulated) data, but does not work yet on the real CT and US images.

KW - CNN

KW - CT

KW - Liver

KW - Registration

KW - Ultrasound

UR - http://www.scopus.com/inward/record.url?scp=85056452872&partnerID=8YFLogxK

U2 - 10.1007/978-3-030-02628-8_5

DO - 10.1007/978-3-030-02628-8_5

M3 - Conference contribution

SN - 978-3-030-02627-1

VL - 11038 LNCS

T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

SP - 43

EP - 51

BT - Understanding and Interpreting Machine Learning in Medical Image Computing Applications - First International Workshops MLCN 2018, DLF 2018, and iMIMIC 2018, Held in Conjunction with MICCAI 2018, Proceedings

PB - Springer Verlag

ER -

ID: 47534196