ArXiv Preprint
Registration of pre-operative and follow-up brain MRI scans is challenging
due to the large variation of tissue appearance and missing correspondences in
tumour recurrence regions caused by tumour mass effect. Although recent deep
learning-based deformable registration methods have achieved remarkable success
in various medical applications, most of them are not capable of registering
images with pathologies. In this paper, we propose a 3-step registration
pipeline for pre-operative and follow-up brain MRI scans that consists of 1) a
multi-level affine registration, 2) a conditional deep Laplacian pyramid image
registration network (cLapIRN) with forward-backward consistency constraint,
and 3) a non-linear instance optimization method. We apply the method to the
Brain Tumor Sequence Registration (BraTS-Reg) Challenge. Our method achieves
accurate and robust registration of brain MRI scans with pathologies, which
achieves a median absolute error of 1.64 mm and 88% of successful registration
rate in the validation set of BraTS-Reg challenge.
Tony C. W. Mok, Albert C. S. Chung
2022-10-20
