# Geometry-aware Prototype Learning for Cross-domain Few-shot Medical Image Segmentation

arXiv: https://arxiv.org/abs/2605.10885 · HTML: https://mlsanity.com/paper/2605.10885/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 25 of its 42 references that are in the map.
- First posted on arXiv 11 May 2026.

## Builds on (most cited first, 25 of 25)

- [Deep Residual Learning for Image Recognition](https://mlsanity.com/paper/1512.03385/) (2015)
- [U-Net: Convolutional Networks for Biomedical Image Segmentation](https://mlsanity.com/paper/1505.04597/) (2015)
- [Microsoft COCO: Common Objects in Context](https://mlsanity.com/paper/1405.0312/) (2014)
- [Skin Lesion Analysis Toward Melanoma Detection 2018: A Challenge Hosted by the International Skin Imaging Collaboration (ISIC)](https://mlsanity.com/paper/1902.03368/) (2019)
- [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)
- [Multivariate mixture model for myocardium segmentation combining multi-source images](https://mlsanity.com/paper/1612.08820/) (2016)
- [Self-Supervision with Superpixels: Training Few-shot Medical Image Segmentation without Annotation](https://mlsanity.com/paper/2007.09886/) (2020)
- [Recurrent Mask Refinement for Few-Shot Medical Image Segmentation](https://mlsanity.com/paper/2108.00622/) (2021)
- [Deep Distance Transform for Tubular Structure Segmentation in CT Scans](https://mlsanity.com/paper/1912.03383/) (2019)
- [Anomaly Detection-Inspired Few-Shot Medical Image Segmentation Through Self-Supervision With Supervoxels](https://mlsanity.com/paper/2203.02048/) (2022)
- [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)
- [Q-Net: Query-Informed Few-Shot Medical Image Segmentation](https://mlsanity.com/paper/2208.11451/) (2022)
- [Few Shot Medical Image Segmentation with Cross Attention Transformer](https://mlsanity.com/paper/2303.13867/) (2023)
- [MI-SegNet: Mutual Information-Based US Segmentation for Unseen Domain Generalization](https://mlsanity.com/paper/2303.12649/) (2023)
- [Lightweight Frequency Masker for Cross-Domain Few-Shot Semantic Segmentation](https://mlsanity.com/paper/2410.22135/) (2024)
- [Prototypical few-shot segmentation for cross-institution male pelvic structures with spatial registration](https://mlsanity.com/paper/2209.05160/) (2022)
- [FAMNet: Frequency-aware Matching Network for Cross-domain Few-shot Medical Image Segmentation](https://mlsanity.com/paper/2412.09319/) (2024)
- [Prototype Correlation Matching and Class-Relation Reasoning for Few-Shot Medical Image Segmentation](https://mlsanity.com/paper/2406.05054/) (2024)
- [Partition-A-Medical-Image: Extracting Multiple Representative Sub-regions for Few-shot Medical Image Segmentation](https://mlsanity.com/paper/2309.11172/) (2023)
- [MAUP: Training-free Multi-center Adaptive Uncertainty-aware Prompting for Cross-domain Few-shot Medical Image Segmentation](https://mlsanity.com/paper/2508.03511/) (2025)
- [RobustEMD: Domain Robust Matching for Cross-domain Few-shot Medical Image Segmentation](https://mlsanity.com/paper/2410.01110/) (2024)
- [Contrastive Graph Modeling for Cross-Domain Few-Shot Medical Image Segmentation](https://mlsanity.com/paper/2512.21683/) (2025)

## Abstract (arXiv)

Cross-domain few-shot medical image segmentation (CD-FSMIS) requires a model to generalise simultaneously to novel anatomical categories and unseen imaging domains from only a handful of annotated examples. Existing prototypical approaches inevitably entangle anatomical structure with domain-specific appearance variations, and thus lack a stable reference for reliable matching under domain shift. We observe that the geometric structure of human anatomy constitutes a reliable, domain-transferable prior that has been overlooked. Building on this insight, we propose GeoProto, a geometry-aware CD-FSMIS framework that enriches prototypical matching with explicit structural priors. The core component, Geometry-Aware Prototype Enrichment (GAPE), augments each local appearance prototype with a learned geometric offset encoding its ordinal position within the organ's interior topology. This offset is derived from an auxiliary Ordinal Shape Branch (OSB) trained under an ordinally consistent objective that enforces monotonic variation of geometric embeddings across interior strata, requiring no annotation beyond standard segmentation masks. Extensive experiments across seven datasets spanning three evaluation settings (cross-modality, cross-sequence, and cross-context) demonstrate that GeoProto achieves state-of-the-art performance.