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.