The Importance of Realistic Training Deformations for Respiratory CT Registration

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 physiolog...

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Bibliographic Details
Published in:	2023 IEEE 20th International Symposium on Biomedical Imaging (ISBI) pp. 1 - 5
Main Authors:	Kolenbrander, Iris D., Huijben, Evi M. C., Bergmans, Johanna N. A., van Bussel, Kim F. M., Pluim, Josien P. W., van Eijnatten, Maureen A. J. M.
Format:	Conference Proceeding
Language:	English
Published:	IEEE 18-04-2023
Subjects:	Computed tomography convolutional neural networks Deep learning deformable image registration Deformation Image registration Neural networks realistic deformations synthetic data Training Training data
Online Access:	Get full text
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Summary:	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.
ISSN:	1945-8452
DOI:	10.1109/ISBI53787.2023.10230687