Towards Fully Automatic X-Ray to CT Registration

Esteban J, Grimm M, Unberath M, Zahnd G, Navab N (2019)

Publication Type: Conference contribution

Publication year: 2019

Journal

Lecture Notes in Computer Science Springer Verlag

Publisher: Springer Science and Business Media Deutschland GmbH

Book Volume: 11769 LNCS

Pages Range: 631-639

Conference Proceedings Title: Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

Event location: Shenzhen, CHN

ISBN: 9783030322250

DOI: 10.1007/978-3-030-32226-7_70

Abstract

The main challenge preventing a fully-automatic X-ray to CT registration is an initialization scheme that brings the X-ray pose within the capture range of existing intensity-based registration methods. By providing such an automatic initialization, the present study introduces the first end-to-end fully-automatic registration framework. A network is first trained once on artificial X-rays to extract 2D landmarks resulting from the projection of CT-labels. A patient-specific refinement scheme is then carried out: candidate points detected from a new set of artificial X-rays are back-projected onto the patient CT and merged into a refined meaningful set of landmarks used for network re-training. This network-landmarks combination is finally exploited for intraoperative pose-initialization with a runtime of 102 ms. Evaluated on 6 pelvis anatomies (486 images in total), the mean Target Registration Error was 15.0 ± 7.3 mm. When used to initialize the BOBYQA optimizer with normalized cross-correlation, the average (± STD) projection distance was 3.4 ± 2.3 mm, and the registration success rate (projection distance < 2.5 % of the detector width) greater than 97 %.

Involved external institutions

Technische Universität München (TUM) DE Germany (DE) Johns Hopkins University (JHU) US United States (USA) (US)

How to cite

APA:

Esteban, J., Grimm, M., Unberath, M., Zahnd, G., & Navab, N. (2019). Towards Fully Automatic X-Ray to CT Registration. In Dinggang Shen, Pew-Thian Yap, Tianming Liu, Terry M. Peters, Ali Khan, Lawrence H. Staib, Caroline Essert, Sean Zhou (Eds.), Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (pp. 631-639). Shenzhen, CHN: Springer Science and Business Media Deutschland GmbH.

MLA:

Esteban, Javier, et al. "Towards Fully Automatic X-Ray to CT Registration." Proceedings of the 22nd International Conference on Medical Image Computing and Computer-Assisted Intervention, MICCAI 2019, Shenzhen, CHN Ed. Dinggang Shen, Pew-Thian Yap, Tianming Liu, Terry M. Peters, Ali Khan, Lawrence H. Staib, Caroline Essert, Sean Zhou, Springer Science and Business Media Deutschland GmbH, 2019. 631-639.

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