Paper ID: 2306.10611

Deep learning-based group-wise registration for longitudinal MRI analysis in glioma

Claudia Chinea Hammecher, Karin van Garderen, Marion Smits, Pieter Wesseling, Bart Westerman, Pim French, Mathilde Kouwenhoven, Roel Verhaak, Frans Vos, Esther Bron, Bo Li

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.

Submitted: Jun 18, 2023