Abstract
In this work, we create a point cloud-based framework based on Free Point Transformers (FPTs) for 2D/3D registration of untracked ultrasound (US) sweeps. Applications include outpatient follow-up assessments and intraoperative scenarios like ultrasound-guided navigation. Through a simple modification in displacement prediction representation, we enhance registration results by more than 25% w.r.t. prior work while preserving the model-free paradigm, maintaining network parameters, and only marginally increasing computation time. Experiments on the SegThy dataset, featuring manually segmented anatomies on MR (magnetic resoncance) scans in the thyroid gland area, demonstrate our method’s effectiveness. We simulate numerous realistic ultrasound sweeps, aiming to register them back into the MR volume. Beyond methodological contributions, our fast registration framework strives to enable clinically capable systems, advancing ultrasound-guided surgery.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
Similar content being viewed by others
References
Prevost R, Salehi M, Jagoda S, Kumar N, Sprung J, Ladikos A et al. 3D freehand ultrasound without external tracking using deep learning. MedIA. 2018;48:187–202.
Wein W, Lupetti M, Zettinig O, Jagoda S, Salehi M, Markova V et al. Three-dimensional thyroid assessment from untracked 2D ultrasound clips. Proc MICCAI. 2020:514–23.
Ferrante E, Paragios N. Non-rigid 2D-3D medical image registration using Markov random fields. Proc MICCAI. 2013:163–70.
Markova V, Ronchetti M, Wein W, Zettinig O, Prevost R. Global Multi-modal 2D/3D registration via Local descriptors learning. Proc MICCAI. 2022:269–79.
Baum ZM, Hu Y, Barratt DC. Real-time multimodal image registration with partial intraoperative point-set data. MedIA. 2021;74.
Qi CR, Su H, Mo K, Guibas LJ. Pointnet: deep learning on point sets for 3d classification and segmentation. Proc CVPR. 2017:652–60.
Sun X, Xiao B, Wei F, Liang S, Wei Y. Integral human pose regression. Proc ECCV. 2018:529–45.
Krönke M, Eilers C, Dimova D, Köhler M, Buschner G, Schweiger L et al. Tracked 3D ultrasound and deep neural network-based thyroid segmentation reduce interobserver variability in thyroid volumetry. PloS One. 2022;17(7).
Isensee F, Jaeger PF, Kohl SA, Petersen J, Maier-Hein KH. nnU-net: a self-configuring method for deep learning-based biomedical image segmentation.Nat Methods. 2021;18(2):203– 11.
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2024 Der/die Autor(en), exklusiv lizenziert an Springer Fachmedien Wiesbaden GmbH, ein Teil von Springer Nature
About this paper
Cite this paper
Hansen, L., Lichtenstein, J., Heinrich, M.P. (2024). Displacement Representation for Conditional Point Cloud Registration. In: Maier, A., Deserno, T.M., Handels, H., Maier-Hein, K., Palm, C., Tolxdorff, T. (eds) Bildverarbeitung für die Medizin 2024. BVM 2024. Informatik aktuell. Springer Vieweg, Wiesbaden. https://doi.org/10.1007/978-3-658-44037-4_14
Download citation
DOI: https://doi.org/10.1007/978-3-658-44037-4_14
Published:
Publisher Name: Springer Vieweg, Wiesbaden
Print ISBN: 978-3-658-44036-7
Online ISBN: 978-3-658-44037-4
eBook Packages: Computer Science and Engineering (German Language)