# POLAFFINI: Efficient feature-based polyaffine initialization for improved non-linear image registration

arXiv: https://arxiv.org/abs/2407.03922 · HTML: https://mlsanity.com/paper/2407.03922/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- Builds on 4 of its 28 references that are in the map.
- First posted on arXiv 4 Jul 2024.

## Builds on (most cited first, 4 of 4)

- [VoxelMorph: A Learning Framework for Deformable Medical Image Registration](https://mlsanity.com/paper/1809.05231/) (2018)
- [Unsupervised Learning of Probabilistic Diffeomorphic Registration for Images and Surfaces](https://mlsanity.com/paper/1903.03545/) (2019)
- [Large Deformation Diffeomorphic Image Registration with Laplacian Pyramid Networks](https://mlsanity.com/paper/2006.16148/) (2020)
- [FastSurfer -- A fast and accurate deep learning based neuroimaging pipeline](https://mlsanity.com/paper/1910.03866/) (2019)

## Built on by (most cited first, 4 of 4)

- [Adaptive Segmentation-Based Initialization for Steered Mixture of Experts Image Regression](https://mlsanity.com/paper/2409.10101/) (2024)
- [Polaffini: A feature-based approach for robust affine and polyaffine image registration](https://mlsanity.com/paper/2602.17337/) (2026)
- [NimbleReg: A light-weight deep-learning framework for diffeomorphic image registration](https://mlsanity.com/paper/2503.07768/) (2025)
- [Aligning Fetal Anatomy with Kinematic Tree Log-Euclidean PolyRigid Transforms](https://mlsanity.com/paper/2603.02371/) (2026)

## Abstract (arXiv)

This paper presents an efficient feature-based approach to initialize non-linear image registration. Today, nonlinear image registration is dominated by methods relying on intensity-based similarity measures. A good estimate of the initial transformation is essential, both for traditional iterative algorithms and for recent one-shot deep learning (DL)-based alternatives. The established approach to estimate this starting point is to perform affine registration, but this may be insufficient due to its parsimonious, global, and non-bending nature. We propose an improved initialization method that takes advantage of recent advances in DL-based segmentation techniques able to instantly estimate fine-grained regional delineations with state-of-the-art accuracies. Those segmentations are used to produce local, anatomically grounded, feature-based affine matchings using iteration-free closed-form expressions. Estimated local affine transformations are then fused, with the log-Euclidean polyaffine framework, into an overall dense diffeomorphic transformation. We show that, compared to its affine counterpart, the proposed initialization leads to significantly better alignment for both traditional and DL-based non-linear registration algorithms. The proposed approach is also more robust and significantly faster than commonly used affine registration algorithms such as FSL FLIRT.