We propose LiftReg, a 2D/3D deformable registration approach. LiftReg is a deep registration framework which is trained using sets of digitally reconstructed radiographs (DRR) and computed tomography (CT) image pairs. By using simulated training data, LiftReg can use a high-quality CT-CT image similarity measure, which helps the network to learn a high-quality deformation space. To further improve registration quality and to address the inherent depth ambiguities of very limited angle acquisitions, we propose to use features extracted from the backprojected 2D images and a statistical deformation model. We test our approach on the DirLab lung registration dataset and show that it outperforms an existing learning-based pairwise registration approach.
CITATION STYLE
Tian, L., Lee, Y. Z., San José Estépar, R., & Niethammer, M. (2022). LiftReg: Limited Angle 2D/3D Deformable Registration. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 13436 LNCS, pp. 207–216). Springer Science and Business Media Deutschland GmbH. https://doi.org/10.1007/978-3-031-16446-0_20
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