1. Deep learning-based group-wise registration for longitudinal MRI analysis in glioma
- Author
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Hammecher, Claudia Chinea, van Garderen, Karin, Smits, Marion, Wesseling, Pieter, Westerman, Bart, French, Pim, Kouwenhoven, Mathilde, Verhaak, Roel, Vos, Frans, Bron, Esther, and Li, Bo
- Subjects
Electrical Engineering and Systems Science - Image and Video Processing ,Computer Science - Computer Vision and Pattern Recognition ,Computer Science - Machine Learning - Abstract
Glioma growth may be quantified with longitudinal image registration. However, the large mass-effects and tissue changes across images pose an added challenge. Here, we propose a longitudinal, learning-based, and groupwise registration method for the accurate and unbiased registration of glioma MRI. We evaluate on a dataset from the Glioma Longitudinal AnalySiS consortium and compare it to classical registration methods. We achieve comparable Dice coefficients, with more detailed registrations, while significantly reducing the runtime to under a minute. The proposed methods may serve as an alternative to classical toolboxes, to provide further insight into glioma growth., Comment: Digital poster presented at the annual meeting of the International Society for Magnetic Resonance in Medicine (ISMRM) 2023. A 6 minute video about this work is available for browsing by the conference website (Program number: 4361)
- Published
- 2023