Robust Image Registration with Absent Correspondences in Pre-operative and Follow-up Brain MRI Scans of Diffuse Glioma Patients

• URL: http://arxiv.org/abs/2210.11045v1
• Date: Thu, 20 Oct 2022 06:37:40 GMT
• Title: Robust Image Registration with Absent Correspondences in Pre-operative and Follow-up Brain MRI Scans of Diffuse Glioma Patients
• Authors: Tony C. W. Mok and Albert C. S. Chung
• Abstract summary: We propose a 3-step registration pipeline for pre-operative and follow-up brain MRI scans. Our method achieves a median absolute error of 1.64 mm and 88% of successful registration rate in the validation set of BraTS-Reg challenge.
• Score: 11.4219428942199
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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.

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