# CNN-based Lung CT Registration with Multiple Anatomical Constraints

arXiv: https://arxiv.org/abs/2011.14372 · HTML: https://mlsanity.com/paper/2011.14372/

## Key facts

- Cited by 26 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 29 Nov 2020.

## Built on by (most cited first, 26 of 26)

- [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)
- [textttGradICON: Approximate Diffeomorphisms via Gradient Inverse Consistency](https://mlsanity.com/paper/2206.05897/) (2022)
- [Affine Medical Image Registration with Coarse-to-Fine Vision Transformer](https://mlsanity.com/paper/2203.15216/) (2022)
- [Voxelmorph++ Going beyond the cranial vault with keypoint supervision and multi-channel instance optimisation](https://mlsanity.com/paper/2203.00046/) (2022)
- [Spatially Covariant Image Registration with Text Prompts](https://mlsanity.com/paper/2311.15607/) (2023)
- [Robust deformable image registration using cycle-consistent implicit representations](https://mlsanity.com/paper/2310.01934/) (2023)
- [Comparison of Consecutive and Re-stained Sections for Image Registration in Histopathology](https://mlsanity.com/paper/2106.13150/) (2021)
- [Unsupervised Echocardiography Registration through Patch-based MLPs and Transformers](https://mlsanity.com/paper/2211.11687/) (2022)
- [ORRN: An ODE-based Recursive Registration Network for Deformable Respiratory Motion Estimation with Lung 4DCT Images](https://mlsanity.com/paper/2305.14673/) (2023)
- [Learning Homeomorphic Image Registration via Conformal-Invariant Hyperelastic Regularisation](https://mlsanity.com/paper/2303.08113/) (2023)
- [MomentaMorph: Unsupervised Spatial-Temporal Registration with Momenta, Shooting, and Correction](https://mlsanity.com/paper/2308.02949/) (2023)
- [Multi-Objective Learning for Deformable Image Registration](https://mlsanity.com/paper/2402.16658/) (2024)
- [Gaussian Primitives for Deformable Image Registration](https://mlsanity.com/paper/2406.03394/) (2024)
- [Toward Universal Medical Image Registration via Sharpness-Aware Meta-Continual Learning](https://mlsanity.com/paper/2406.17575/) (2024)
- [ST-NeRP: Spatial-Temporal Neural Representation Learning with Prior Embedding for Patient-specific Imaging Study](https://mlsanity.com/paper/2410.19283/) (2024)
- [GuidedMorph: Two-Stage Deformable Registration for Breast MRI](https://mlsanity.com/paper/2505.13414/) (2025)
- [TotalRegistrator: Towards a Lightweight Foundation Model for CT Image Registration](https://mlsanity.com/paper/2508.04450/) (2025)
- [DARE: A Deformable Adaptive Regularization Estimator for Learning-Based Medical Image Registration](https://mlsanity.com/paper/2510.19353/) (2025)
- [A Bi-variant Variational Model for Diffeomorphic Image Registration with Relaxed Jacobian Determinant Constraints](https://mlsanity.com/paper/2308.02393/) (2023)
- [CARL: A Framework for Equivariant Image Registration](https://mlsanity.com/paper/2405.16738/) (2024)
- [Divide to Conquer: A Field Decomposition Approach for Multi-Organ Whole-Body CT Image Registration](https://mlsanity.com/paper/2503.22281/) (2025)
- [cIDIR: Conditioned Implicit Neural Representation for Regularized Deformable Image Registration](https://mlsanity.com/paper/2507.12953/) (2025)
- [Multimodal registration of FISH and nanoSIMS images using convolutional neural network models](https://mlsanity.com/paper/2201.05545/) (2022)
- [Uncertainty-Aware Test-Time Adaptation for Inverse Consistent Diffeomorphic Lung Image Registration](https://mlsanity.com/paper/2411.07567/) (2024)
- [Ideal Registration? Segmentation is All You Need](https://mlsanity.com/paper/2509.15784/) (2025)

## Abstract (arXiv)

Deep-learning-based registration methods emerged as a fast alternative to conventional registration methods. However, these methods often still cannot achieve the same performance as conventional registration methods because they are either limited to small deformation or they fail to handle a superposition of large and small deformations without producing implausible deformation fields with foldings inside. In this paper, we identify important strategies of conventional registration methods for lung registration and successfully developed the deep-learning counterpart. We employ a Gaussian-pyramid-based multilevel framework that can solve the image registration optimization in a coarse-to-fine fashion. Furthermore, we prevent foldings of the deformation field and restrict the determinant of the Jacobian to physiologically meaningful values by combining a volume change penalty with a curvature regularizer in the loss function. Keypoint correspondences are integrated to focus on the alignment of smaller structures. We perform an extensive evaluation to assess the accuracy, the robustness, the plausibility of the estimated deformation fields, and the transferability of our registration approach. We show that it achieves state-of-the-art results on the COPDGene dataset compared to conventional registration method with much shorter execution time. In our experiments on the DIRLab exhale to inhale lung registration, we demonstrate substantial improvements (TRE below 1.2 mm) over other deep learning methods. Our algorithm is publicly available at https://grand-challenge.org/algorithms/deep-learning-based-ct-lung-registration/.