# Preserving Tumor Volumes for Unsupervised Medical Image Registration

arXiv: https://arxiv.org/abs/2309.10153 · HTML: https://mlsanity.com/paper/2309.10153/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- Builds on 19 of its 40 references that are in the map.
- First posted on arXiv 18 Sep 2023.

## Builds on (most cited first, 19 of 19)

- [An Unsupervised Learning Model for Deformable Medical Image Registration](https://mlsanity.com/paper/1802.02604/) (2018)
- [SynthMorph: learning contrast-invariant registration without acquired images](https://mlsanity.com/paper/2004.10282/) (2020)
- [Large Deformation Diffeomorphic Image Registration with Laplacian Pyramid Networks](https://mlsanity.com/paper/2006.16148/) (2020)
- [Recursive Cascaded Networks for Unsupervised Medical Image Registration](https://mlsanity.com/paper/1907.12353/) (2019)
- [CycleMorph: Cycle Consistent Unsupervised Deformable Image Registration](https://mlsanity.com/paper/2008.05772/) (2020)
- [Unsupervised Multi-Modal Image Registration via Geometry Preserving Image-to-Image Translation](https://mlsanity.com/paper/2003.08073/) (2020)
- [Unsupervised 3D End-to-End Medical Image Registration with Volume Tweening Network](https://mlsanity.com/paper/1902.05020/) (2019)
- [XMorpher: Full Transformer for Deformable Medical Image Registration via Cross Attention](https://mlsanity.com/paper/2206.07349/) (2022)
- [Label-Free Segmentation of COVID-19 Lesions in Lung CT](https://mlsanity.com/paper/2009.06456/) (2020)
- [The Brain Tumor Sequence Registration (BraTS-Reg) Challenge: Establishing Correspondence Between Pre-Operative and Follow-up MRI Scans of Diffuse Glioma Patients](https://mlsanity.com/paper/2112.06979/) (2021)
- [Anatomy-aware and acquisition-agnostic joint registration with SynthMorph](https://mlsanity.com/paper/2301.11329/) (2023)
- [Unsupervised Deformable Image Registration with Absent Correspondences in Pre-operative and Post-Recurrence Brain Tumor MRI Scans](https://mlsanity.com/paper/2206.03900/) (2022)
- [Brain Tumor Sequence Registration with Non-iterative Coarse-to-fine Networks and Dual Deep Supervision](https://mlsanity.com/paper/2211.07876/) (2022)
- [Deformer: Towards Displacement Field Learning for Unsupervised Medical Image Registration](https://mlsanity.com/paper/2207.03180/) (2022)
- [Unsupervised Method for Intra-patient Registration of Brain Magnetic Resonance Images based on Objective Function Weighting by Inverse Consistency: Contribution to the BraTS-Reg Challenge](https://mlsanity.com/paper/2211.07386/) (2022)
- [Joint segmentation and discontinuity-preserving deformable registration: Application to cardiac cine-MR images](https://mlsanity.com/paper/2211.13828/) (2022)
- [Efficient Registration of Pathological Images: A Joint PCA/Image-Reconstruction Approach](https://mlsanity.com/paper/1704.00036/) (2017)
- [A Transformer-based Network for Deformable Medical Image Registration](https://mlsanity.com/paper/2202.12104/) (2022)
- [Unsupervised End-to-end Learning for Deformable Medical Image Registration](https://mlsanity.com/paper/1711.08608/) (2017)

## Built on by (most cited first, 3 of 3)

- [Ordinal Learning: Longitudinal Attention Alignment Model for Predicting Time to Future Breast Cancer Events from Mammograms](https://mlsanity.com/paper/2409.06887/) (2024)
- [Deep-Learning Atlas Registration for Melanoma Brain Metastases: Preserving Pathology While Enabling Cohort-Level Analyses](https://mlsanity.com/paper/2602.12933/) (2026)
- [DrivenMorph: Bridging Attention Mechanism and Variational Image Registration via Difference Modeling](https://mlsanity.com/paper/2606.30183/) (2026)

## Abstract (arXiv)

Medical image registration is a critical task that estimates the spatial correspondence between pairs of images. However, current traditional and deep-learning-based methods rely on similarity measures to generate a deforming field, which often results in disproportionate volume changes in dissimilar regions, especially in tumor regions. These changes can significantly alter the tumor size and underlying anatomy, which limits the practical use of image registration in clinical diagnosis. To address this issue, we have formulated image registration with tumors as a constraint problem that preserves tumor volumes while maximizing image similarity in other normal regions. Our proposed strategy involves a two-stage process. In the first stage, we use similarity-based registration to identify potential tumor regions by their volume change, generating a soft tumor mask accordingly. In the second stage, we propose a volume-preserving registration with a novel adaptive volume-preserving loss that penalizes the change in size adaptively based on the masks calculated from the previous stage. Our approach balances image similarity and volume preservation in different regions, i.e., normal and tumor regions, by using soft tumor masks to adjust the imposition of volume-preserving loss on each one. This ensures that the tumor volume is preserved during the registration process. We have evaluated our strategy on various datasets and network architectures, demonstrating that our method successfully preserves the tumor volume while achieving comparable registration results with state-of-the-art methods. Our codes is available at: urlhttps://dddraxxx.github.io/Volume-Preserving-Registration/.