Learning an Attention Model for Robust 2-D/3-D Registration Using Point-To-Plane Correspondences

Schaffert R, Wang J, Fischer P, Borsdorf A, Maier A (2020)

Publication Type: Journal article

Publication year: 2020

Journal

IEEE Transactions on Medical Imaging Institute of Electrical and Electronics Engineers (IEEE)

Book Volume: 39

Pages Range: 3159-3174

Journal Issue: 10

DOI: 10.1109/TMI.2020.2988410

Abstract

Minimally invasive procedures rely on image guidance for navigation at the operation site to avoid large surgical incisions. X-ray images are often used for guidance, but important structures may be not well visible. These structures can be overlaid from pre-operative 3-D images and accurate alignment can be established using 2-D/3-D registration. Registration based on the point-to-plane correspondence model was recently proposed and shown to achieve state-of-the-art performance. However, registration may still fail in challenging cases due to a large portion of outliers. In this paper, we describe a learning-based correspondence weighting scheme to improve the registration performance. By learning an attention model, inlier correspondences get higher attention in the motion estimation while the outlier correspondences are suppressed. Instead of using per-correspondence labels, our objective function allows to train the model directly by minimizing the registration error. We demonstrate a highly increased robustness, e.g. increasing the success rate from 84.9% to 97.0% for spine registration. In contrast to previously proposed learning-based methods, we also achieve a high accuracy of around 0.5mm mean re-projection distance. In addition, our method requires a relatively small amount of training data, is able to learn from simulated data, and generalizes to images with additional structures which are not present during training. Furthermore, a single model can be trained for both, different views and different anatomical structures.

Authors with CRIS profile

Roman Schaffert Lehrstuhl für Informatik 14 (Bild- und Sprachverarbeitung) Andreas Maier Lehrstuhl für Informatik 5 (Mustererkennung)

Involved external institutions

Siemens Healthineers DE Germany (DE)

How to cite

APA:

Schaffert, R., Wang, J., Fischer, P., Borsdorf, A., & Maier, A. (2020). Learning an Attention Model for Robust 2-D/3-D Registration Using Point-To-Plane Correspondences. IEEE Transactions on Medical Imaging, 39(10), 3159-3174. https://doi.org/10.1109/TMI.2020.2988410

MLA:

Schaffert, Roman, et al. "Learning an Attention Model for Robust 2-D/3-D Registration Using Point-To-Plane Correspondences." IEEE Transactions on Medical Imaging 39.10 (2020): 3159-3174.

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