The Importance of Realistic Training Deformations for Respiratory CT Registration

Onderzoeksoutput: Hoofdstuk in Boek/Rapport/CongresprocedureConferentiebijdrageAcademicpeer review

Samenvatting

Deep learning enables fast deformable medical image registration but requires large training datasets, which are currently scarce. To overcome this, synthetic deformations can be generated to create and augment the training data. We propose a method that incorporates prior knowledge of the physiological motion to generate more realistic deformations. Specifically, our method is developed on thoracic computed tomography scans and incorporates respiratory motion. We evaluated the effect of various synthetic deformation methods on deep learning-based registration performance, achieving better performance when trained on realistic deformations, compared to when trained on random deformations. In general, the inclusion of realistic deformations, either real or synthetic, was found to be essential for achieving good registration performance.

Originele taal-2Engels
Titel2023 IEEE International Symposium on Biomedical Imaging, ISBI 2023
UitgeverijIEEE Computer Society
ISBN van elektronische versie9781665473583
DOI's
StatusGepubliceerd - 2023
Evenement20th IEEE International Symposium on Biomedical Imaging, ISBI 2023 - Cartagena, Colombia
Duur: 18 apr. 202321 apr. 2023

Congres

Congres20th IEEE International Symposium on Biomedical Imaging, ISBI 2023
Land/RegioColombia
StadCartagena
Periode18/04/2321/04/23

Bibliografische nota

Publisher Copyright:
© 2023 IEEE.

Financiering

This work was partially funded by the Irène Curie Fellowship program and the Eindhoven Artificial Intelligence Systems Institute.

FinanciersFinanciernummer
EAISI

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