nach oben

Erschienen in:

2017 | Supplement | Buchkapitel

Multimodal Image Registration with Deep Context Reinforcement Learning

verfasst von : Kai Ma, Jiangping Wang, Vivek Singh, Birgi Tamersoy, Yao-Jen Chang, Andreas Wimmer, Terrence Chen

Erschienen in: Medical Image Computing and Computer Assisted Intervention − MICCAI 2017

Verlag: Springer International Publishing

Einloggen

Aktivieren Sie unsere intelligente Suche, um passende Fachinhalte oder Patente zu finden.

search-config

KI-gestützte Suche

Aus

Abstract

Automatic and robust registration between real-time patient imaging and pre-operative data (e.g. CT and MRI) is crucial for computer-aided interventions and AR-based navigation guidance. In this paper, we present a novel approach to automatically align range image of the patient with pre-operative CT images. Unlike existing approaches based on the surface similarity optimization process, our algorithm leverages the contextual information of medical images to resolve data ambiguities and improve robustness. The proposed algorithm is derived from deep reinforcement learning algorithm that automatically learns to extract optimal feature representation to reduce the appearance discrepancy between these two modalities. Quantitative evaluations on 1788 pairs of CT and depth images from real clinical setting demonstrate that the proposed method achieves the state-of-the-art performance.

Sie haben noch keine Lizenz? Dann Informieren Sie sich jetzt über unsere Produkte:

Springer Professional "Wirtschaft+Technik"

Online-Abonnement

Mit Springer Professional "Wirtschaft+Technik" erhalten Sie Zugriff auf:

über 102.000 Bücher
über 537 Zeitschriften

aus folgenden Fachgebieten:

Automobil + Motoren
Bauwesen + Immobilien
Business IT + Informatik
Elektrotechnik + Elektronik
Energie + Nachhaltigkeit
Finance + Banking
Management + Führung
Marketing + Vertrieb
Maschinenbau + Werkstoffe
Versicherung + Risiko

Jetzt Wissensvorsprung sichern!

Jetzt informieren

Springer Professional "Technik"

Online-Abonnement

Mit Springer Professional "Technik" erhalten Sie Zugriff auf:

über 67.000 Bücher
über 390 Zeitschriften

aus folgenden Fachgebieten:

Automobil + Motoren
Bauwesen + Immobilien
Business IT + Informatik
Elektrotechnik + Elektronik
Energie + Nachhaltigkeit
Maschinenbau + Werkstoffe

Jetzt Wissensvorsprung sichern!

Jetzt informieren

Springer Professional "Wirtschaft"

Online-Abonnement

Mit Springer Professional "Wirtschaft" erhalten Sie Zugriff auf:

über 67.000 Bücher
über 340 Zeitschriften

aus folgenden Fachgebieten:

Bauwesen + Immobilien
Business IT + Informatik
Finance + Banking
Management + Führung
Marketing + Vertrieb
Versicherung + Risiko

Jetzt Wissensvorsprung sichern!

Jetzt informieren

Vorheriges Kapitel Nonrigid Image Registration Using Multi-scale 3D Convolutional Neural Networks

Nächstes Kapitel Directional Averages for Motion Segmentation in Discontinuity Preserving Image Registration

Nur mit Berechtigung zugänglich

Achilles, F., Ichim, A.-E., Coskun, H., Tombari, F., Noachtar, S., Navab, N.: Patient MoCap: human pose estimation under blanket occlusion for hospital monitoring applications. In: Ourselin, S., Joskowicz, L., Sabuncu, M.R., Unal, G., Wells, W. (eds.) MICCAI 2016. LNCS, vol. 9900, pp. 491–499. Springer, Cham (2016). doi:10.1007/978-3-319-46720-7_57CrossRef

Bauer, S., Wasza, J., Haase, S., Marosi, N., Hornegger, J.: Multi-modal surface registration for markerless initial patient setup in radiation therapy using Microsoft’s Kinect sensor. In: ICCV Workshops (2011)

Bellman, R.: A Markovian decision process. Indiana Univ. Math. J. 6, 679–684 (1957)MathSciNetCrossRef

Cao, X., Gao, Y., Yang, J., Wu, G., Shen, D.: Learning-based multimodal image registration for prostate cancer radiation therapy. In: Ourselin, S., Joskowicz, L., Sabuncu, M.R., Unal, G., Wells, W. (eds.) MICCAI 2016. LNCS, vol. 9902, pp. 1–9. Springer, Cham (2016). doi:10.1007/978-3-319-46726-9_1CrossRef

Elmi-Terander, A., Skulason, H., Söderman, M., et al.: Surgical navigation technology based on augmented reality and integrated 3D intraoperative imaging: a spine cadaveric feasibility and accuracy study. Spine 41, 303–311 (2016)CrossRef

Ghesu, F.C., Georgescu, B., Mansi, T., Neumann, D., Hornegger, J., Comaniciu, D.: An artificial agent for anatomical landmark detection in medical images. In: Ourselin, S., Joskowicz, L., Sabuncu, M.R., Unal, G., Wells, W. (eds.) MICCAI 2016. LNCS, vol. 9902, pp. 229–237. Springer, Cham (2016). doi:10.1007/978-3-319-46726-9_27CrossRef

Gutiérrez-Becker, B., Mateus, D., Peter, L., Navab, N.: Learning optimization updates for multimodal registration. In: Ourselin, S., Joskowicz, L., Sabuncu, M.R., Unal, G., Wells, W. (eds.) MICCAI 2016. LNCS, vol. 9902, pp. 19–27. Springer, Cham (2016). doi:10.1007/978-3-319-46726-9_3CrossRef

Hasselt, H., Guez, A., Silver, D.: Deep reinforcement learning with double Q-learning. In: AAAI (2016)

Ioffe, S., Szegedy, C.: Batch normalization: accelerating deep network training by reducing internal covariate shift. In: arXiv (2015)

10.

Levine, S., Pastor, P., Krizhevsky, A., Quillen, D.: Learning hand-eye coordination for robotic grasping with deep learning and large-scale data collection. In: ISER (2016)

11.

Liao, R., Miao, S., de Tournemire, P., Grbic, S., Kamen, A., Mansi, T., Comaniciu, D.: An artificial agent for robust image registration. In: AAAI (2017)

12.

Ma, K., Chang, Y.J., Singh, V.K., O’donnell, T., Wels, M., Betz, T., Wimmer, A., Chen, T.: Calibrating RGB-D sensors to medical image scanners. US Patent 9,633,435

13.

Mnih, V., Kavukcuoglu, K., Silver, D., Rusu, A.A., Veness, J., Bellemare, M.G., Graves, A., Riedmiller, M., Fidjeland, A.K., Ostrovski, G., Petersen, S., Beattie, C., Sadik, A., Antonoglou, I., King, H., Kumaran, D., Wierstra, D., Legg, S., Hassabis, D.: Human-level control through deep reinforcement learning. Nature 518, 529–533 (2015)CrossRef

14.

Nutti, B., Kronander, S., Nilsing, M., Maad, K., Svensson, C., Li, H.: Depth sensor-based realtime tumor tracking for accurate radiation therapy. In: Eurographics (2014)

15.

Simonovsky, M., Gutiérrez-Becker, B., Mateus, D., Navab, N., Komodakis, N.: A deep metric for multimodal registration. In: Ourselin, S., Joskowicz, L., Sabuncu, M.R., Unal, G., Wells, W. (eds.) MICCAI 2016. LNCS, vol. 9902, pp. 10–18. Springer, Cham (2016). doi:10.1007/978-3-319-46726-9_2CrossRef

16.

Singh, V., Chang, Y., Ma, K., Wels, M., Soza, G., Chen, T.: Estimating a patient surface model for optimizing the medical scanning workflow. In: Golland, P., Hata, N., Barillot, C., Hornegger, J., Howe, R. (eds.) MICCAI 2014. LNCS, vol. 8673, pp. 472–479. Springer, Cham (2014). doi:10.1007/978-3-319-10404-1_59CrossRef

17.

Toews, M., Zöllei, L., Wells, W.M.: Feature-based alignment of volumetric multi-modal images. Inf. Process. Med. Imaging 23, 25–36 (2013)

18.

Wang, Z., de Freitas, N., Lanctot, M.: Dueling network architectures for deep reinforcement learning. In: ICML (2016)

19.

Xiao, D., Luo, H., Jia, F., Zhang, Y., Li, Y., Guo, X., Cai, W., Fang, C., Fan, Y., Zheng, H., Hu, Q.: A Kinect camera based navigation system for percutaneous abdominal puncture. Phys. Med. Biol. 61, 5687–5705 (2016)CrossRef

Titel: Multimodal Image Registration with Deep Context Reinforcement Learning
verfasst von: Kai Ma
Jiangping Wang
Vivek Singh
Birgi Tamersoy
Yao-Jen Chang
Andreas Wimmer
Terrence Chen
Verlag: Springer International Publishing
Buch: Medical Image Computing and Computer Assisted Intervention − MICCAI 2017
Print ISBN: 978-3-319-66181-0

Electronic ISBN: 978-3-319-66182-7

Copyright-Jahr: 2017
DOI: https://doi.org/10.1007/978-3-319-66182-7_28

Springer Professional

Abstract

Bitte loggen Sie sich ein, um Zugang zu Ihrer Lizenz zu erhalten.

Sie haben noch keine Lizenz? Dann Informieren Sie sich jetzt über unsere Produkte:

Springer Professional "Wirtschaft+Technik"

Springer Professional "Technik"

Springer Professional "Wirtschaft"

Premium Partner