  Deformable image registration is a key task in medical image analysis. The
Brain Tumor Sequence Registration challenge (BraTS-Reg) aims at establishing
correspondences between pre-operative and follow-up scans of the same patient
diagnosed with an adult brain diffuse high-grade glioma and intends to address
the challenging task of registering longitudinal data with major tissue
appearance changes. In this work, we proposed a two-stage cascaded network
based on the Inception and TransMorph models. The dataset for each patient was
comprised of a native pre-contrast (T1), a contrast-enhanced T1-weighted
(T1-CE), a T2-weighted (T2), and a Fluid Attenuated Inversion Recovery (FLAIR).
The Inception model was used to fuse the 4 image modalities together and
extract the most relevant information. Then, a variant of the TransMorph
architecture was adapted to generate the displacement fields. The Loss function
was composed of a standard image similarity measure, a diffusion regularizer,
and an edge-map similarity measure added to overcome intensity dependence and
reinforce correct boundary deformation. We observed that the addition of the
Inception module substantially increased the performance of the network.
Additionally, performing an initial affine registration before training the
model showed improved accuracy in the landmark error measurements between pre
and post-operative MRIs. We observed that our best model composed of the
Inception and TransMorph architectures while using an initially affine
registered dataset had the best performance with a median absolute error of
2.91 (initial error = 7.8). We achieved 6th place at the time of model
submission in the final testing phase of the BraTS-Reg challenge.
