# Probabilistic Multilayer Regularization Network for Unsupervised 3D Brain Image Registration

arXiv: https://arxiv.org/abs/1907.01922 · HTML: https://mlsanity.com/paper/1907.01922/

## Key facts

- Cited by 13 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 3 Jul 2019.

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

- [A survey on deep learning in medical image registration: new technologies, uncertainty, evaluation metrics, and beyond](https://mlsanity.com/paper/2307.15615/) (2023)
- [Cross-conditioned Diffusion Model for Medical Image to Image Translation](https://mlsanity.com/paper/2409.08500/) (2024)
- [Enhancing Medical Image Registration via Appearance Adjustment Networks](https://mlsanity.com/paper/2103.05213/) (2021)
- [Semantic similarity metrics for learned image registration](https://mlsanity.com/paper/2104.10051/) (2021)
- [PC-SwinMorph: Patch Representation for Unsupervised Medical Image Registration and Segmentation](https://mlsanity.com/paper/2203.05684/) (2022)
- [Medical image registration using unsupervised deep neural network: A scoping literature review](https://mlsanity.com/paper/2208.01825/) (2022)
- [Cascaded Feature Warping Network for Unsupervised Medical Image Registration](https://mlsanity.com/paper/2103.08213/) (2021)
- [Uncertainty quantification in non-rigid image registration via stochastic gradient Markov chain Monte Carlo](https://mlsanity.com/paper/2110.13289/) (2021)
- [Simultaneous Semantic and Instance Segmentation for Colon Nuclei Identification and Counting](https://mlsanity.com/paper/2203.00157/) (2022)
- [ABN: Anti-Blur Neural Networks for Multi-Stage Deformable Image Registration](https://mlsanity.com/paper/2212.03277/) (2022)
- [Learning Homeomorphic Image Registration via Conformal-Invariant Hyperelastic Regularisation](https://mlsanity.com/paper/2303.08113/) (2023)
- [Spot the Difference: Detection of Topological Changes via Geometric Alignment](https://mlsanity.com/paper/2106.08233/) (2021)
- [Optimised ProPainter for Video Diminished Reality Inpainting](https://mlsanity.com/paper/2406.02287/) (2024)

## Abstract (arXiv)

Brain image registration transforms a pair of images into one system with the matched imaging contents, which is of essential importance for brain image analysis. This paper presents a novel framework for unsupervised 3D brain image registration by capturing the feature-level transformation relationships between the unaligned image and reference image. To achieve this, we develop a feature-level probabilistic model to provide the direct regularization to the hidden layers of two deep convolutional neural networks, which are constructed from two input images. This model design is developed into multiple layers of these two networks to capture the transformation relationships at different levels. We employ two common benchmark datasets for 3D brain image registration and perform various experiments to evaluate our method. Experimental results show that our method clearly outperforms state-of-the-art methods on both benchmark datasets by a large margin.