MetaRegNet: Metamorphic Image Registration Using Flow-Driven Residual Networks
This work addresses a domain-specific challenge in medical imaging by enabling more accurate registration of images with large pathological regions, which is incremental as it builds on existing deep learning methods for diffeomorphic registration.
The paper tackled the problem of metamorphic image registration for medical images with missing correspondences due to pathologies like tumors, proposing MetaRegNet which uses time-varying flows to handle spatial deformations and intensity variations, showing promising results on brain and liver tumor datasets.
Deep learning based methods provide efficient solutions to medical image registration, including the challenging problem of diffeomorphic image registration. However, most methods register normal image pairs, facing difficulty handling those with missing correspondences, e.g., in the presence of pathology like tumors. We desire an efficient solution to jointly account for spatial deformations and appearance changes in the pathological regions where the correspondences are missing, i.e., finding a solution to metamorphic image registration. Some approaches are proposed to tackle this problem, but they cannot properly handle large pathological regions and deformations around pathologies. In this paper, we propose a deep metamorphic image registration network (MetaRegNet), which adopts time-varying flows to drive spatial diffeomorphic deformations and generate intensity variations. We evaluate MetaRegNet on two datasets, i.e., BraTS 2021 with brain tumors and 3D-IRCADb-01 with liver tumors, showing promising results in registering a healthy and tumor image pair. The source code is available online.