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International Journal of Computer Assisted Radiology and Surgery

06-09-2024 | Computed Tomography | Original Article

Global registration of kidneys in 3D ultrasound and CT images

Authors: William Ndzimbong, Nicolas Thome, Cyril Fourniol, Yvonne Keeza, Benoît Sauer, Jacques Marescaux, Daniel George, Alexandre Hostettler, Toby Collins

Published in: International Journal of Computer Assisted Radiology and Surgery

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Abstract

Purpose

Automatic registration between abdominal ultrasound (US) and computed tomography (CT) images is needed to enhance interventional guidance of renal procedures, but it remains an open research challenge. We propose a novel method that doesn’t require an initial registration estimate (a global method) and also handles registration ambiguity caused by the organ’s natural symmetry. Combined with a registration refinement algorithm, this method achieves robust and accurate kidney registration while avoiding manual initialization.

Methods

We propose solving global registration in a three-step approach: (1) Automatic anatomical landmark localization, where 2 deep neural networks (DNNs) localize a set of landmarks in each modality. (2) Registration hypothesis generation, where potential registrations are computed from the landmarks with a deterministic variant of RANSAC. Due to the Kidney’s strong bilateral symmetry, there are usually 2 compatible solutions. Finally, in Step (3), the correct solution is determined automatically, using a DNN classifier that resolves the geometric ambiguity. The registration may then be iteratively improved with a registration refinement method. Results are presented with state-of-the-art surface-based refinement—Bayesian coherent point drift (BCPD).

Results

This automatic global registration approach gives better results than various competitive state-of-the-art methods, which, additionally, require organ segmentation. The results obtained on 59 pairs of 3D US/CT kidney images show that the proposed method, combined with BCPD refinement, achieves a target registration error (TRE) of an internal kidney landmark (the renal pelvis) of 5.78 mm and an average nearest neighbor surface distance (nndist) of 2.42 mm.

Conclusion

This work presents the first approach for automatic kidney registration in US and CT images, which doesn’t require an initial manual registration estimate to be known a priori. The results show a fully automatic registration approach with performances comparable to manual methods is feasible.
Appendix
Available only for authorised users
Footnotes
1
The TRUSTED Dataset is described in the arXiv pre-print at https://​arxiv.​org/​pdf/​2310.​12646.
 
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Metadata
Title
Global registration of kidneys in 3D ultrasound and CT images
Authors
William Ndzimbong
Nicolas Thome
Cyril Fourniol
Yvonne Keeza
Benoît Sauer
Jacques Marescaux
Daniel George
Alexandre Hostettler
Toby Collins
Publication date
06-09-2024
Publisher
Springer International Publishing
Published in
International Journal of Computer Assisted Radiology and Surgery
Print ISSN: 1861-6410
Electronic ISSN: 1861-6429
DOI
https://doi.org/10.1007/s11548-024-03255-3