MetaRegNet: Metamorphic Image Registration Using Flow-Driven Residual Networks
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Abstract (arXiv)
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.
Key facts. Cited by 2 papers in the map (data through 13 Oct 2026). Builds on 11 of its 36 references that are in the map. First posted on arXiv 16 Mar 2023.
Builds on
11 of 36 references.
References · 11
- 2015U-Net: Convolutional Networks for Biomedical Image Segmentationcited by 19,656
- 2021The RSNA-ASNR-MICCAI BraTS 2021 Benchmark on Brain Tumor Segmentation and Radiogenomic Classificationcited by 514
- 2018Unsupervised Learning for Fast Probabilistic Diffeomorphic Registrationcited by 116
- 2017Quicksilver: Fast Predictive Image Registration - a Deep Learning Approachcited by 113
- 2020Fast Symmetric Diffeomorphic Image Registration with Convolutional Neural Networkscited by 107
- 2018Deforming Autoencoders: Unsupervised Disentangling of Shape and Appearancecited by 58
- 2022Unsupervised Deformable Image Registration with Absent Correspondences in Pre-operative and Post-Recurrence Brain Tumor MRI Scanscited by 15
- 2020A Deep Network for Joint Registration and Reconstruction of Images with Pathologiescited by 9
- 2017Brain Extraction from Normal and Pathological Images: A Joint PCA/Image-Reconstruction Approachcited by 6
- 2021Metamorphic image registration using a semi-Lagrangian schemecited by 6
- 2022A deep residual learning implementation of Metamorphosiscited by 6
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Citing papers per year: 2026: 2.
2 papers.