|J. Esteban, M. Grimm, M. Unberath, G. Zahnd, N. Navab
Towards fully automatic X-ray to CT registration
22nd International Conference on Medical Image Computing and Computer Assisted Intervention (MICCAI), Shenzhen, China, 2019 (bib)
|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 102ms. Evaluated on 6 pelvis anatomies (486 images in total), the mean Target Registration Error was 15.0+-7.3mm. When used to initialize the BOBYQA optimizer with normalized cross-correlation, the average (+-STD) projection distance was 3.4 +- 2.3mm, and the registration success rate (projection distance <2.5% of the detector width) greater than 97%.</TD>
|This material is presented to ensure timely dissemination of scholarly and technical work. Copyright and all rights therein are retained by authors or by other copyright holders. All persons copying this information are expected to adhere to the terms and constraints invoked by each authors copyright. In most cases, these works may not be reposted without the explicit permission of the copyright holder.