Abstract
Artificial intelligence has been used with great success for the segmentation of anatomical structures in medical imaging. We use these achievements to improve classical registration schemes. Particularly, we derive geometrical features such as centroids and principal axes of segments and use those in a combined approach. A smart filtering of the features results in a two phase preregistration, followed in a third phase by an intensity guided registration. We also propose to use a regularization, which enables a coupling of all components of the 3D transformation in a unified framework. Finally, we show how easily our approach can be applied even to challenging 3D medical data.
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References
Modersitzki J. Numerical Methods for Image Registration. New York: Oxford University Press, 2004.
Wasserthal J, Breit HC, Meyer MT, Pradella M, Hinck D, Sauter AWet al. TotalSegmentator: Robust segmentation of 104 anatomic structures inCTimages. Radiol: Artifl Intell. 2023;5(5).
Sorzano COS, Thévenaz P, Unser M. Elastic registration of biological images using vectorspline regularization. IEEE Trans Biomedi Engin. 2005;52(4):652–63.
Haber E, Heldmann S, Modersitzki J. A scale-space approach to landmark constrained image registration. Proceedings of the Second International Conference on Scale Space Methods and Variational Methods in Computer Vision (SSVM). Springer LNCS, 2009:1–12.
Amodei L, Benbourhim M. A vector spline approximation. J Approx Theory. 1991;67(1):51– 79.
Dodu F, Rabut C. Vectorial interpolation using radial-basis-like functions. Comput & Math Appl. 2002;43(3-5):393–411.
Modersitzki J. FAIR: Flexible Algorithms for Image Registration. SIAM, 2009.
Hering A, Murphy K, Ginneken B. Lean2Regchallenge: CT lung registration-training data . 2020.
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© 2024 Der/die Autor(en), exklusiv lizenziert an Springer Fachmedien Wiesbaden GmbH, ein Teil von Springer Nature
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Neuber, S., Schulz, P.F., Kuckertz, S., Modersitzki, J. (2024). Segmentation-inspired Image 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_59
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DOI: https://doi.org/10.1007/978-3-658-44037-4_59
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