# PSP: Harnessing Position and Shape Priors for Cross-Domain Few-Shot Medical Image Segmentation

arXiv: https://arxiv.org/abs/2606.28799 · HTML: https://mlsanity.com/paper/2606.28799/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 13 of its 26 references that are in the map.
- First posted on arXiv 27 Jun 2026.

## Builds on (most cited first, 13 of 13)

- [Microsoft COCO: Common Objects in Context](https://mlsanity.com/paper/1405.0312/) (2014)
- [PANet: Few-Shot Image Semantic Segmentation with Prototype Alignment](https://mlsanity.com/paper/1908.06391/) (2019)
- [CHAOS Challenge -- Combined (CT-MR) Healthy Abdominal Organ Segmentation](https://mlsanity.com/paper/2001.06535/) (2020)
- [Anomaly Detection-Inspired Few-Shot Medical Image Segmentation Through Self-Supervision With Supervoxels](https://mlsanity.com/paper/2203.02048/) (2022)
- [Shape-Aware Organ Segmentation by Predicting Signed Distance Maps](https://mlsanity.com/paper/1912.03849/) (2019)
- [Cardiac Segmentation on Late Gadolinium Enhancement MRI: A Benchmark Study from Multi-Sequence Cardiac MR Segmentation Challenge](https://mlsanity.com/paper/2006.12434/) (2020)
- [Cross-Domain Few-Shot Segmentation via Iterative Support-Query Correspondence Mining](https://mlsanity.com/paper/2401.08407/) (2024)
- [Few-Shot Medical Image Segmentation via a Region-enhanced Prototypical Transformer](https://mlsanity.com/paper/2309.04825/) (2023)
- [Few Shot Medical Image Segmentation with Cross Attention Transformer](https://mlsanity.com/paper/2303.13867/) (2023)
- [FAMNet: Frequency-aware Matching Network for Cross-domain Few-shot Medical Image Segmentation](https://mlsanity.com/paper/2412.09319/) (2024)
- [RobustEMD: Domain Robust Matching for Cross-domain Few-shot Medical Image Segmentation](https://mlsanity.com/paper/2410.01110/) (2024)
- [FCSN: Global Context Aware Segmentation by Learning the Fourier Coefficients of Objects in Medical Images](https://mlsanity.com/paper/2207.14477/) (2022)
- [Concentrate on Weakness: Mining Hard Prototypes for Few-Shot Medical Image Segmentation](https://mlsanity.com/paper/2505.21897/) (2025)

## Abstract (arXiv)

Few-Shot Medical Image Segmentation (FSMIS) offers a powerful solution to data scarcity but struggles to generalize across different imaging modalities. This performance collapse stems primarily from the drastic texture discrepancies between domains, which mislead models trained on source-specific intensity distributions. While existing methods attempt to align frequency or local texture features, they often fail to decouple semantic structure from domain-specific appearance. To address this, we identify a critical invariance: despite distinct imaging physics, the position and geometric shape of organs remain robustly consistent across modalities. Therefore, we propose a novel framework that harnesses Position and Shape Priors (PSP) for cross-domain FSMIS. Specifically, PSP first introduces a Position Coordinate Embedding (PCE) module to inject relative spatial coordinates for rapid organ localization. Subsequently, a Shape Prototype Modulation (SPM) module constructs domain-invariant structural prototypes via explicit shape priors, effectively filtering out texture noise. Furthermore, the Hybrid-Prototype Prediction (HPP) module adaptively calibrates the support prototype to the query feature distribution, mitigating feature misalignment. Extensive experiments on two public medical imaging datasets demonstrate that PSP significantly outperforms state-of-the-art methods.