# Linear and Deformable Image Registration with 3D Convolutional Neural Networks

arXiv: https://arxiv.org/abs/1809.06226 · HTML: https://mlsanity.com/paper/1809.06226/

## Key facts

- Cited by 8 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 13 Sep 2018.

## Built on by (most cited first, 8 of 8)

- [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)
- [Cross-Modality Image Registration using a Training-Time Privileged Third Modality](https://mlsanity.com/paper/2207.12901/) (2022)
- [TEDS-Net: Enforcing Diffeomorphisms in Spatial Transformers to Guarantee Topology Preservation in Segmentations](https://mlsanity.com/paper/2107.13542/) (2021)
- [Medical image registration using unsupervised deep neural network: A scoping literature review](https://mlsanity.com/paper/2208.01825/) (2022)
- [MICS : Multi-steps, Inverse Consistency and Symmetric deep learning registration network](https://mlsanity.com/paper/2111.12123/) (2021)
- [Semi-weakly-supervised neural network training for medical image registration](https://mlsanity.com/paper/2402.10728/) (2024)
- [Exploring Deep Registration Latent Spaces](https://mlsanity.com/paper/2107.11238/) (2021)
- [Deformable image registration with deep network priors: a study on longitudinal PET images](https://mlsanity.com/paper/2111.11873/) (2021)

## Abstract (arXiv)

Image registration and in particular deformable registration methods are pillars of medical imaging. Inspired by the recent advances in deep learning, we propose in this paper, a novel convolutional neural network architecture that couples linear and deformable registration within a unified architecture endowed with near real-time performance. Our framework is modular with respect to the global transformation component, as well as with respect to the similarity function while it guarantees smooth displacement fields. We evaluate the performance of our network on the challenging problem of MRI lung registration, and demonstrate superior performance with respect to state of the art elastic registration methods. The proposed deformation (between inspiration & expiration) was considered within a clinically relevant task of interstitial lung disease (ILD) classification and showed promising results.