# Data Efficient Unsupervised Domain Adaptation for Cross-Modality Image Segmentation

arXiv: https://arxiv.org/abs/1907.02766 · HTML: https://mlsanity.com/paper/1907.02766/

## Key facts

- Cited by 33 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 5 Jul 2019.

## Built on by (most cited first, 30 of 33)

- [Causality-inspired Single-source Domain Generalization for Medical Image Segmentation](https://mlsanity.com/paper/2111.12525/) (2021)
- [Deep Unsupervised Domain Adaptation: A Review of Recent Advances and Perspectives](https://mlsanity.com/paper/2208.07422/) (2022)
- [Recurrent Mask Refinement for Few-Shot Medical Image Segmentation](https://mlsanity.com/paper/2108.00622/) (2021)
- [CrossMoDA 2021 challenge: Benchmark of Cross-Modality Domain Adaptation techniques for Vestibular Schwannoma and Cochlea Segmentation](https://mlsanity.com/paper/2201.02831/) (2022)
- [Unsupervised Domain Adaptation with Variational Approximation for Cardiac Segmentation](https://mlsanity.com/paper/2106.08752/) (2021)
- [Deep learning for unsupervised domain adaptation in medical imaging: Recent advancements and future perspectives](https://mlsanity.com/paper/2308.01265/) (2023)
- [Multi-Modality Cardiac Image Computing: A Survey](https://mlsanity.com/paper/2208.12881/) (2022)
- [MMOTU: A Multi-Modality Ovarian Tumor Ultrasound Image Dataset for Unsupervised Cross-Domain Semantic Segmentation](https://mlsanity.com/paper/2207.06799/) (2022)
- [AnyStar: Domain randomized universal star-convex 3D instance segmentation](https://mlsanity.com/paper/2307.07044/) (2023)
- [Domain-Adaptive 3D Medical Image Synthesis: An Efficient Unsupervised Approach](https://mlsanity.com/paper/2207.00844/) (2022)
- [CUTS: A Deep Learning and Topological Framework for Multigranular Unsupervised Medical Image Segmentation](https://mlsanity.com/paper/2209.11359/) (2022)
- [Unsupervised domain adaptation for clinician pose estimation and instance segmentation in the operating room](https://mlsanity.com/paper/2108.11801/) (2021)
- [Unsupervised Domain Adaptation for Cardiac Segmentation: Towards Structure Mutual Information Maximization](https://mlsanity.com/paper/2204.09334/) (2022)
- [Adversarially Robust Prototypical Few-shot Segmentation with Neural-ODEs](https://mlsanity.com/paper/2210.03429/) (2022)
- [Do Vision Foundation Models Enhance Domain Generalization in Medical Image Segmentation?](https://mlsanity.com/paper/2409.07960/) (2024)
- [Unsupervised Domain Adaptation Using Feature Disentanglement And GCNs For Medical Image Classification](https://mlsanity.com/paper/2206.13123/) (2022)
- [SC-VAE: Sparse Coding-based Variational Autoencoder with Learned ISTA](https://mlsanity.com/paper/2303.16666/) (2023)
- [Source-Free Unsupervised Domain Adaptation with Norm and Shape Constraints for Medical Image Segmentation](https://mlsanity.com/paper/2209.01300/) (2022)
- [Optimal Latent Vector Alignment for Unsupervised Domain Adaptation in Medical Image Segmentation](https://mlsanity.com/paper/2106.08188/) (2021)
- [Exemplar Learning for Medical Image Segmentation](https://mlsanity.com/paper/2204.01713/) (2022)
- [Unsupervised Adaptation of Polyp Segmentation Models via Coarse-to-Fine Self-Supervision](https://mlsanity.com/paper/2308.06665/) (2023)
- [Towards Privacy-Supporting Fall Detection via Deep Unsupervised RGB2Depth Adaptation](https://mlsanity.com/paper/2308.12049/) (2023)
- [Unsupervised Domain Adaptation Network with Category-Centric Prototype Aligner for Biomedical Image Segmentation](https://mlsanity.com/paper/2103.02220/) (2021)
- [Unsupervised Domain Adaptation with Semantic Consistency across Heterogeneous Modalities for MRI Prostate Lesion Segmentation](https://mlsanity.com/paper/2109.09736/) (2021)
- [Consecutive Knowledge Meta-Adaptation Learning for Unsupervised Medical Diagnosis](https://mlsanity.com/paper/2209.10425/) (2022)
- [Reverse Imaging for Wide-spectrum Generalization of Cardiac MRI Segmentation](https://mlsanity.com/paper/2508.21254/) (2025)
- [Multi-Modality Cardiac Image Analysis with Deep Learning](https://mlsanity.com/paper/2111.04736/) (2021)
- [Confidence-Guided Unsupervised Domain Adaptation for Cerebellum Segmentation](https://mlsanity.com/paper/2206.10357/) (2022)
- [Contrastive Image Synthesis and Self-supervised Feature Adaptation for Cross-Modality Biomedical Image Segmentation](https://mlsanity.com/paper/2207.13240/) (2022)
- [Image-level supervision and self-training for transformer-based cross-modality tumor segmentation](https://mlsanity.com/paper/2309.09246/) (2023)

## Abstract (arXiv)

Deep learning models trained on medical images from a source domain (e.g. imaging modality) often fail when deployed on images from a different target domain, despite imaging common anatomical structures. Deep unsupervised domain adaptation (UDA) aims to improve the performance of a deep neural network model on a target domain, using solely unlabelled target domain data and labelled source domain data. However, current state-of-the-art methods exhibit reduced performance when target data is scarce. In this work, we introduce a new data efficient UDA method for multi-domain medical image segmentation. The proposed method combines a novel VAE-based feature prior matching, which is data-efficient, and domain adversarial training to learn a shared domain-invariant latent space which is exploited during segmentation. Our method is evaluated on a public multi-modality cardiac image segmentation dataset by adapting from the labelled source domain (3D MRI) to the unlabelled target domain (3D CT). We show that by using only one single unlabelled 3D CT scan, the proposed architecture outperforms the state-of-the-art in the same setting. Finally, we perform ablation studies on prior matching and domain adversarial training to shed light on the theoretical grounding of the proposed method.