# Image Segmentation with Adaptive Spatial Priors from Joint Registration

arXiv: https://arxiv.org/abs/2203.15548 · HTML: https://mlsanity.com/paper/2203.15548/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- Builds on 0 of its 46 references that are in the map.
- First posted on arXiv 29 Mar 2022.

## Built on by (most cited first, 5 of 5)

- [Re-initialization-free Level Set Method via Molecular Beam Epitaxy Equation Regularization for Image Segmentation](https://mlsanity.com/paper/2310.08861/) (2023)
- [Interpretable Small Training Set Image Segmentation Network Originated from Multi-Grid Variational Model](https://mlsanity.com/paper/2306.14097/) (2023)
- [Difference of Anisotropic and Isotropic TV for Segmentation under Blur and Poisson Noise](https://mlsanity.com/paper/2301.03393/) (2023)
- [A Geometric Flow Approach for Segmentation of Images with Inhomongeneous Intensity and Missing Boundaries](https://mlsanity.com/paper/2309.10935/) (2023)
- [Dual-Scale Volume Priors with Wasserstein-Based Consistency for Semi-Supervised Medical Image Segmentation](https://mlsanity.com/paper/2509.04273/) (2025)

## Abstract (arXiv)

Image segmentation is a crucial but challenging task that has many applications. In medical imaging for instance, intensity inhomogeneity and noise are common. In thigh muscle images, different muscles are closed packed together and there are often no clear boundaries between them. Intensity based segmentation models cannot separate one muscle from another. To solve such problems, in this work we present a segmentation model with adaptive spatial priors from joint registration. This model combines segmentation and registration in a unified framework to leverage their positive mutual influence. The segmentation is based on a modified Gaussian mixture model (GMM), which integrates intensity inhomogeneity and spacial smoothness. The registration plays the role of providing a shape prior. We adopt a modified sum of squared difference (SSD) fidelity term and Tikhonov regularity term for registration, and also utilize Gaussian pyramid and parametric method for robustness. The connection between segmentation and registration is guaranteed by the cross entropy metric that aims to make the segmentation map (from segmentation) and deformed atlas (from registration) as similar as possible. This joint framework is implemented within a constraint optimization framework, which leads to an efficient algorithm. We evaluate our proposed model on synthetic and thigh muscle MR images. Numerical results show the improvement as compared to segmentation and registration performed separately and other joint models.