# Closing the Gap between Deep and Conventional Image Registration using Probabilistic Dense Displacement Networks

arXiv: https://arxiv.org/abs/1907.10931 · HTML: https://mlsanity.com/paper/1907.10931/

## Key facts

- Cited by 40 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 25 Jul 2019.

## Built on by (most cited first, 30 of 40)

- [Learn2Reg: comprehensive multi-task medical image registration challenge, dataset and evaluation in the era of deep learning](https://mlsanity.com/paper/2112.04489/) (2021)
- [Recurrent Mask Refinement for Few-Shot Medical Image Segmentation](https://mlsanity.com/paper/2108.00622/) (2021)
- [A survey on deep learning in medical image registration: new technologies, uncertainty, evaluation metrics, and beyond](https://mlsanity.com/paper/2307.15615/) (2023)
- [AFTer-UNet: Axial Fusion Transformer UNet for Medical Image Segmentation](https://mlsanity.com/paper/2110.10403/) (2021)
- [Fast 3D registration with accurate optimisation and little learning for Learn2Reg 2021](https://mlsanity.com/paper/2112.03053/) (2021)
- [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)
- [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)
- [SAMConvex: Fast Discrete Optimization for CT Registration using Self-supervised Anatomical Embedding and Correlation Pyramid](https://mlsanity.com/paper/2307.09727/) (2023)
- [Deep learning based geometric registration for medical images: How accurate can we get without visual features?](https://mlsanity.com/paper/2103.00885/) (2021)
- [Residual Aligner Network](https://mlsanity.com/paper/2203.04290/) (2022)
- [Vector Field Attention for Deformable Image Registration](https://mlsanity.com/paper/2407.10209/) (2024)
- [Automatic Segmentation of Organs-at-Risk from Head-and-Neck CT using Separable Convolutional Neural Network with Hard-Region-Weighted Loss](https://mlsanity.com/paper/2102.01897/) (2021)
- [Robust Image Registration with Absent Correspondences in Pre-operative and Follow-up Brain MRI Scans of Diffuse Glioma Patients](https://mlsanity.com/paper/2210.11045/) (2022)
- [A Deep-Discrete Learning Framework for Spherical Surface Registration](https://mlsanity.com/paper/2203.12999/) (2022)
- [Recursive Deformable Image Registration Network with Mutual Attention](https://mlsanity.com/paper/2206.01863/) (2022)
- [Unsupervised 3D registration through optimization-guided cyclical self-training](https://mlsanity.com/paper/2306.16997/) (2023)
- [MICS : Multi-steps, Inverse Consistency and Symmetric deep learning registration network](https://mlsanity.com/paper/2111.12123/) (2021)
- [Driving Points Prediction For Abdominal Probabilistic Registration](https://mlsanity.com/paper/2208.03232/) (2022)
- [Disentangling Progress in Medical Image Registration: Beyond Trend-Driven Architectures towards Domain-Specific Strategies](https://mlsanity.com/paper/2512.01913/) (2025)
- [Automated liver tissues delineation techniques: A systematic survey on machine learning current trends and future orientations](https://mlsanity.com/paper/2103.06384/) (2021)
- [Uncertainty quantification in non-rigid image registration via stochastic gradient Markov chain Monte Carlo](https://mlsanity.com/paper/2110.13289/) (2021)
- [Unsupervised Deformable Image Registration with Structural Nonparametric Smoothing](https://mlsanity.com/paper/2506.10813/) (2025)
- [Learning Homeomorphic Image Registration via Conformal-Invariant Hyperelastic Regularisation](https://mlsanity.com/paper/2303.08113/) (2023)
- [VoxelOpt: Voxel-Adaptive Message Passing for Discrete Optimization in Deformable Abdominal CT Registration](https://mlsanity.com/paper/2506.19975/) (2025)
- [A multi-organ point cloud registration algorithm for abdominal CT registration](https://mlsanity.com/paper/2203.08041/) (2022)
- [Recurrence With Correlation Network for Medical Image Registration](https://mlsanity.com/paper/2302.02283/) (2023)
- [Multi-Contrast Computed Tomography Atlas of Healthy Pancreas](https://mlsanity.com/paper/2306.01853/) (2023)
- [QUIZ: An Arbitrary Volumetric Point Matching Method for Medical Image Registration](https://mlsanity.com/paper/2310.00296/) (2023)

## Abstract (arXiv)

Nonlinear image registration continues to be a fundamentally important tool in medical image analysis. Diagnostic tasks, image-guided surgery and radiotherapy as well as motion analysis all rely heavily on accurate intra-patient alignment. Furthermore, inter-patient registration enables atlas-based segmentation or landmark localisation and shape analysis. When labelled scans are scarce and anatomical differences large, conventional registration has often remained superior to deep learning methods that have so far mainly dealt with relatively small or low-complexity deformations. We address this shortcoming by leveraging ideas from probabilistic dense displacement optimisation that has excelled in many registration tasks with large deformations. We propose to design a network with approximate min-convolutions and mean field inference for differentiable displacement regularisation within a discrete weakly-supervised registration setting. By employing these meaningful and theoretically proven constraints, our learnable registration algorithm contains very few trainable weights (primarily for feature extraction) and is easier to train with few labelled scans. It is very fast in training and inference and achieves state-of-the-art accuracies for the challenging inter-patient registration of abdominal CT outperforming previous deep learning approaches by 15% Dice overlap.