# Estimation of Large Motion in Lung CT by Integrating Regularized Keypoint Correspondences into Dense Deformable Registration

arXiv: https://arxiv.org/abs/1807.00467 · HTML: https://mlsanity.com/paper/1807.00467/

## Key facts

- Cited by 23 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 2 Jul 2018.

## Built on by (most cited first, 23 of 23)

- [Learn2Reg: comprehensive multi-task medical image registration challenge, dataset and evaluation in the era of deep learning](https://mlsanity.com/paper/2112.04489/) (2021)
- [A survey on deep learning in medical image registration: new technologies, uncertainty, evaluation metrics, and beyond](https://mlsanity.com/paper/2307.15615/) (2023)
- [Accurate Point Cloud Registration with Robust Optimal Transport](https://mlsanity.com/paper/2111.00648/) (2021)
- [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)
- [Global Multi-modal 2D/3D Registration via Local Descriptors Learning](https://mlsanity.com/paper/2205.03439/) (2022)
- [Robust deformable image registration using cycle-consistent implicit representations](https://mlsanity.com/paper/2310.01934/) (2023)
- [Deep learning based geometric registration for medical images: How accurate can we get without visual features?](https://mlsanity.com/paper/2103.00885/) (2021)
- [Keypoint-Augmented Self-Supervised Learning for Medical Image Segmentation with Limited Annotation](https://mlsanity.com/paper/2310.01680/) (2023)
- [Recursive Refinement Network for Deformable Lung Registration between Exhale and Inhale CT Scans](https://mlsanity.com/paper/2106.07608/) (2021)
- [IGCN: Image-to-graph Convolutional Network for 2D/3D Deformable Registration](https://mlsanity.com/paper/2111.00484/) (2021)
- [Automatic Landmarks Correspondence Detection in Medical Images with an Application to Deformable Image Registration](https://mlsanity.com/paper/2109.02722/) (2021)
- [Driving Points Prediction For Abdominal Probabilistic Registration](https://mlsanity.com/paper/2208.03232/) (2022)
- [A 3D Cross-modal Keypoint Descriptor for MR-US Matching and Registration](https://mlsanity.com/paper/2507.18551/) (2025)
- [Deformable Image Registration with Stochastically Regularized Biomechanical Equilibrium](https://mlsanity.com/paper/2312.14987/) (2023)
- [Gaussian Primitives for Deformable Image Registration](https://mlsanity.com/paper/2406.03394/) (2024)
- [Kernel-based framework to estimate deformations of pneumothorax lung using relative position of anatomical landmarks](https://mlsanity.com/paper/2102.12505/) (2021)
- [A denoised Mean Teacher for domain adaptive point cloud registration](https://mlsanity.com/paper/2306.14749/) (2023)
- [A Bi-variant Variational Model for Diffeomorphic Image Registration with Relaxed Jacobian Determinant Constraints](https://mlsanity.com/paper/2308.02393/) (2023)
- [QUIZ: An Arbitrary Volumetric Point Matching Method for Medical Image Registration](https://mlsanity.com/paper/2310.00296/) (2023)
- [HyperPredict: Estimating Hyperparameter Effects for Instance-Specific Regularization in Deformable Image Registration](https://mlsanity.com/paper/2403.02069/) (2024)
- [Neural Graphics Primitives-based Deformable Image Registration for On-the-fly Motion Extraction](https://mlsanity.com/paper/2402.05568/) (2024)
- [Unsupervised Deformable Image Registration with Local-Global Attention and Image Decomposition](https://mlsanity.com/paper/2601.14337/) (2026)
- [An Accessible Solution for Deformable Image Registration Compared with Learning-Based Approaches](https://mlsanity.com/paper/2608.02248/) (2026)

## Abstract (arXiv)

We present a novel algorithm for the registration of pulmonary CT scans. Our method is designed for large respiratory motion by integrating sparse keypoint correspondences into a dense continuous optimization framework. The detection of keypoint correspondences enables robustness against large deformations by jointly optimizing over a large number of potential discrete displacements, whereas the dense continuous registration achieves subvoxel alignment with smooth transformations. Both steps are driven by the same normalized gradient fields data term. We employ curvature regularization and a volume change control mechanism to prevent foldings of the deformation grid and restrict the determinant of the Jacobian to physiologically meaningful values. Keypoint correspondences are integrated into the dense registration by a quadratic penalty with adaptively determined weight. Using a parallel matrix-free derivative calculation scheme, a runtime of about 5 min was realized on a standard PC. The proposed algorithm ranks first in the EMPIRE10 challenge on pulmonary image registration. Moreover, it achieves an average landmark distance of 0.82 mm on the DIR-Lab COPD database, thereby improving upon the state of the art in accuracy by 15%. Our algorithm is the first to reach the inter-observer variability in landmark annotation on this dataset.