Robust Image Registration with Absent Correspondences in Pre-operative and Follow-up Brain MRI Scans of Diffuse Glioma Patients
This addresses a critical problem for medical imaging in brain tumor patients by enabling robust registration despite missing correspondences, though it is incremental as it builds on existing deep learning methods.
The paper tackles the challenge of registering pre-operative and follow-up brain MRI scans with pathologies like diffuse glioma, where tissue appearance varies and correspondences are missing, achieving a median absolute error of 1.64 mm and 88% success rate, ranking first in the 2022 MICCAI BraTS-Reg challenge.
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. Our method ranks 1st place in the 2022 MICCAI BraTS-Reg challenge.