# A deep residual learning implementation of Metamorphosis

arXiv: https://arxiv.org/abs/2202.00676 · HTML: https://mlsanity.com/paper/2202.00676/

## Key facts

- Cited by 6 papers in the map (data through 14 Oct 2026).
- Builds on 5 of its 19 references that are in the map.
- First posted on arXiv 1 Feb 2022.

## Builds on (most cited first, 5 of 5)

- [Deep Residual Learning for Image Recognition](https://mlsanity.com/paper/1512.03385/) (2015)
- [VoxelMorph: A Learning Framework for Deformable Medical Image Registration](https://mlsanity.com/paper/1809.05231/) (2018)
- [ResNet-LDDMM: Advancing the LDDMM Framework using Deep Residual Networks](https://mlsanity.com/paper/2102.07951/) (2021)
- [Metamorphic image registration using a semi-Lagrangian scheme](https://mlsanity.com/paper/2106.08817/) (2021)
- [Coupling Brain-Tumor Biophysical Models and Diffeomorphic Image Registration](https://mlsanity.com/paper/1710.06420/) (2017)

## Built on by (most cited first, 6 of 6)

- [A survey on deep learning in medical image registration: new technologies, uncertainty, evaluation metrics, and beyond](https://mlsanity.com/paper/2307.15615/) (2023)
- [Double InfoGAN for Contrastive Analysis](https://mlsanity.com/paper/2401.17776/) (2024)
- [MetaRegNet: Metamorphic Image Registration Using Flow-Driven Residual Networks](https://mlsanity.com/paper/2303.09088/) (2023)
- [Deep learning in medical image registration: introduction and survey](https://mlsanity.com/paper/2309.00727/) (2023)
- [Level set based particle filter driven by optical flow: an application to track the salt boundary from X-ray CT time-series](https://mlsanity.com/paper/2202.08717/) (2022)
- [Benchmarking Intra-Patient 3D Deformable Multimodal Image Registration](https://mlsanity.com/paper/2609.15669/) (2026)

## Abstract (arXiv)

In medical imaging, most of the image registration methods implicitly assume a one-to-one correspondence between the source and target images (i.e., diffeomorphism). However, this is not necessarily the case when dealing with pathological medical images (e.g., presence of a tumor, lesion, etc.). To cope with this issue, the Metamorphosis model has been proposed. It modifies both the shape and the appearance of an image to deal with the geometrical and topological differences. However, the high computational time and load have hampered its applications so far. Here, we propose a deep residual learning implementation of Metamorphosis that drastically reduces the computational time at inference. Furthermore, we also show that the proposed framework can easily integrate prior knowledge of the localization of topological changes (e.g., segmentation masks) that can act as spatial regularization to correctly disentangle appearance and shape changes. We test our method on the BraTS 2021 dataset, showing that it outperforms current state-of-the-art methods in the alignment of images with brain tumors.