# 3D Convolutional Neural Networks Image Registration Based on Efficient Supervised Learning from Artificial Deformations

arXiv: https://arxiv.org/abs/1908.10235 · HTML: https://mlsanity.com/paper/1908.10235/

## Key facts

- Cited by 13 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 27 Aug 2019.

## Built on by (most cited first, 13 of 13)

- [Joint Registration and Segmentation via Multi-Task Learning for Adaptive Radiotherapy of Prostate Cancer](https://mlsanity.com/paper/2105.01844/) (2021)
- [Deep Learning for Medical Image Registration: A Comprehensive Review](https://mlsanity.com/paper/2204.11341/) (2022)
- [Recursive Refinement Network for Deformable Lung Registration between Exhale and Inhale CT Scans](https://mlsanity.com/paper/2106.07608/) (2021)
- [CartiMorph: a framework for automated knee articular cartilage morphometrics](https://mlsanity.com/paper/2308.01981/) (2023)
- [ORRN: An ODE-based Recursive Registration Network for Deformable Respiratory Motion Estimation with Lung 4DCT Images](https://mlsanity.com/paper/2305.14673/) (2023)
- [EfficientMorph: Parameter-Efficient Transformer-Based Architecture for 3D Image Registration](https://mlsanity.com/paper/2403.11026/) (2024)
- [Learning Homeomorphic Image Registration via Conformal-Invariant Hyperelastic Regularisation](https://mlsanity.com/paper/2303.08113/) (2023)
- [TotalRegistrator: Towards a Lightweight Foundation Model for CT Image Registration](https://mlsanity.com/paper/2508.04450/) (2025)
- [Stationary Velocity Fields on Matrix Groups for Deformable Image Registration](https://mlsanity.com/paper/2410.10997/) (2024)
- [CardioMorphNet: Cardiac Motion Prediction Using a Shape-Guided Bayesian Recurrent Deep Network](https://mlsanity.com/paper/2508.20734/) (2025)
- [Deep Convolutional Neural Network for Non-rigid Image Registration](https://mlsanity.com/paper/2104.12034/) (2021)
- [CDIDN: A Registration Model with High Deformation Impedance Capability for Long-Term Tracking of Pulmonary Lesion Dynamics](https://mlsanity.com/paper/2305.11024/) (2023)
- [MorphSeek: Fine-grained Latent Representation-Level Policy Optimization for Deformable Image Registration](https://mlsanity.com/paper/2511.17392/) (2025)

## Abstract (arXiv)

We propose a supervised nonrigid image registration method, trained using artificial displacement vector fields (DVF), for which we propose and compare three network architectures. The artificial DVFs allow training in a fully supervised and voxel-wise dense manner, but without the cost usually associated with the creation of densely labeled data. We propose a scheme to artificially generate DVFs, and for chest CT registration augment these with simulated respiratory motion. The proposed architectures are embedded in a multi-stage approach, to increase the capture range of the proposed networks in order to more accurately predict larger displacements. The proposed method, RegNet, is evaluated on multiple databases of chest CT scans and achieved a target registration error of 2.32 ± 5.33 mm and 1.86 ± 2.12 mm on SPREAD and DIR-Lab-4DCT studies, respectively. The average inference time of RegNet with two stages is about 2.2 s.