# Unsupervised Domain Adaptation with Variational Approximation for Cardiac Segmentation

arXiv: https://arxiv.org/abs/2106.08752 · HTML: https://mlsanity.com/paper/2106.08752/

## Key facts

- Cited by 28 papers in the map (data through 14 Oct 2026).
- Builds on 22 of its 49 references that are in the map.
- First posted on arXiv 16 Jun 2021.

## Builds on (most cited first, 22 of 22)

- [Adam: A Method for Stochastic Optimization](https://mlsanity.com/paper/1412.6980/) (2014)
- [U-Net: Convolutional Networks for Biomedical Image Segmentation](https://mlsanity.com/paper/1505.04597/) (2015)
- [Auto-Encoding Variational Bayes](https://mlsanity.com/paper/1312.6114/) (2013)
- [Domain-Adversarial Training of Neural Networks](https://mlsanity.com/paper/1505.07818/) (2015)
- [Learning Transferable Features with Deep Adaptation Networks](https://mlsanity.com/paper/1502.02791/) (2015)
- [Deep CORAL: Correlation Alignment for Deep Domain Adaptation](https://mlsanity.com/paper/1607.01719/) (2016)
- [Multimodal Unsupervised Image-to-Image Translation](https://mlsanity.com/paper/1804.04732/) (2018)
- [Wasserstein GAN](https://mlsanity.com/paper/1701.07875/) (2017)
- [Deep Transfer Learning with Joint Adaptation Networks](https://mlsanity.com/paper/1605.06636/) (2016)
- [Deep Domain Confusion: Maximizing for Domain Invariance](https://mlsanity.com/paper/1412.3474/) (2014)
- [Return of Frustratingly Easy Domain Adaptation](https://mlsanity.com/paper/1511.05547/) (2015)
- [A DIRT-T Approach to Unsupervised Domain Adaptation](https://mlsanity.com/paper/1802.08735/) (2018)
- [Variational Mixture-of-Experts Autoencoders for Multi-Modal Deep Generative Models](https://mlsanity.com/paper/1911.03393/) (2019)
- [Unsupervised Bidirectional Cross-Modality Adaptation via Deeply Synergistic Image and Feature Alignment for Medical Image Segmentation](https://mlsanity.com/paper/2002.02255/) (2020)
- [Multivariate mixture model for myocardium segmentation combining multi-source images](https://mlsanity.com/paper/1612.08820/) (2016)
- [Unsupervised domain adaptation in brain lesion segmentation with adversarial networks](https://mlsanity.com/paper/1612.08894/) (2016)
- [Evaluation of Algorithms for Multi-Modality Whole Heart Segmentation: An Open-Access Grand Challenge](https://mlsanity.com/paper/1902.07880/) (2019)
- [Translating and Segmenting Multimodal Medical Volumes with Cycle- and Shape-Consistency Generative Adversarial Network](https://mlsanity.com/paper/1802.09655/) (2018)
- [An Uncertain Future: Forecasting from Static Images using Variational Autoencoders](https://mlsanity.com/paper/1606.07873/) (2016)
- [Data Efficient Unsupervised Domain Adaptation for Cross-Modality Image Segmentation](https://mlsanity.com/paper/1907.02766/) (2019)
- [Domain Adaptation on Graphs by Learning Aligned Graph Bases](https://mlsanity.com/paper/1803.05288/) (2018)
- [Deep Domain Adaptation by Geodesic Distance Minimization](https://mlsanity.com/paper/1707.09842/) (2017)

## Built on by (most cited first, 28 of 28)

- [Unsupervised Medical Image Translation with Adversarial Diffusion Models](https://mlsanity.com/paper/2207.08208/) (2022)
- [Medical Image Analysis on Left Atrial LGE MRI for Atrial Fibrillation Studies: A Review](https://mlsanity.com/paper/2106.09862/) (2021)
- [Deep learning for unsupervised domain adaptation in medical imaging: Recent advancements and future perspectives](https://mlsanity.com/paper/2308.01265/) (2023)
- [One Model to Unite Them All: Personalized Federated Learning of Multi-Contrast MRI Synthesis](https://mlsanity.com/paper/2207.06509/) (2022)
- [GraphEcho: Graph-Driven Unsupervised Domain Adaptation for Echocardiogram Video Segmentation](https://mlsanity.com/paper/2309.11145/) (2023)
- [UPL-SFDA: Uncertainty-aware Pseudo Label Guided Source-Free Domain Adaptation for Medical Image Segmentation](https://mlsanity.com/paper/2309.10244/) (2023)
- [Multi-Modality Cardiac Image Computing: A Survey](https://mlsanity.com/paper/2208.12881/) (2022)
- [PASS:Test-Time Prompting to Adapt Styles and Semantic Shapes in Medical Image Segmentation](https://mlsanity.com/paper/2410.01573/) (2024)
- [Unsupervised Domain Adaptation for Cardiac Segmentation: Towards Structure Mutual Information Maximization](https://mlsanity.com/paper/2204.09334/) (2022)
- [Steady Progress Beats Stagnation: Mutual Aid of Foundation and Conventional Models in Mixed Domain Semi-Supervised Medical Image Segmentation](https://mlsanity.com/paper/2503.16997/) (2025)
- [3D Vessel Segmentation with Limited Guidance of 2D Structure-agnostic Vessel Annotations](https://mlsanity.com/paper/2302.03299/) (2023)
- [Medical Image Segmentation with Domain Adaptation: A Survey](https://mlsanity.com/paper/2311.01702/) (2023)
- [AS-IntroVAE: Adversarial Similarity Distance Makes Robust IntroVAE](https://mlsanity.com/paper/2206.13903/) (2022)
- [Revisiting Adaptive Cellular Recognition Under Domain Shifts: A Contextual Correspondence View](https://mlsanity.com/paper/2407.12870/) (2024)
- [DRL-STNet: Unsupervised Domain Adaptation for Cross-modality Medical Image Segmentation via Disentangled Representation Learning](https://mlsanity.com/paper/2409.18340/) (2024)
- [Tissue Segmentation of Thick-Slice Fetal Brain MR Scans with Guidance from High-Quality Isotropic Volumes](https://mlsanity.com/paper/2308.06762/) (2023)
- [Can Diffusion Models Bridge the Domain Gap in Cardiac MR Imaging?](https://mlsanity.com/paper/2508.06327/) (2025)
- [Semantic-guided Disentangled Representation for Unsupervised Cross-modality Medical Image Segmentation](https://mlsanity.com/paper/2203.14025/) (2022)
- [Adapting Foundation Models for Few-Shot Medical Image Segmentation: Actively and Sequentially](https://mlsanity.com/paper/2502.01000/) (2025)
- [Unified and Semantically Grounded Domain Adaptation for Medical Image Segmentation](https://mlsanity.com/paper/2508.08660/) (2025)
- [Multi-Domain Brain Vessel Segmentation Through Feature Disentanglement](https://mlsanity.com/paper/2510.00665/) (2025)
- [A3-TTA: Adaptive Anchor Alignment Test-Time Adaptation for Image Segmentation](https://mlsanity.com/paper/2602.03292/) (2026)
- [Multi-Modality Cardiac Image Analysis with Deep Learning](https://mlsanity.com/paper/2111.04736/) (2021)
- [Contrastive Image Synthesis and Self-supervised Feature Adaptation for Cross-Modality Biomedical Image Segmentation](https://mlsanity.com/paper/2207.13240/) (2022)
- [A Survey on Causal Representation Learning and Future Work for Medical Image Analysis](https://mlsanity.com/paper/2210.16034/) (2022)
- [Image-level supervision and self-training for transformer-based cross-modality tumor segmentation](https://mlsanity.com/paper/2309.09246/) (2023)
- [Double Banking on Knowledge: Customized Modulation and Prototypes for Multi-Modality Semi-supervised Medical Image Segmentation](https://mlsanity.com/paper/2410.17565/) (2024)
- [Gaze-Assisted Human-Centric Domain Adaptation for Cardiac Ultrasound Image Segmentation](https://mlsanity.com/paper/2502.03781/) (2025)

## Abstract (arXiv)

Unsupervised domain adaptation is useful in medical image segmentation. Particularly, when ground truths of the target images are not available, domain adaptation can train a target-specific model by utilizing the existing labeled images from other modalities. Most of the reported works mapped images of both the source and target domains into a common latent feature space, and then reduced their discrepancy either implicitly with adversarial training or explicitly by directly minimizing a discrepancy metric. In this work, we propose a new framework, where the latent features of both domains are driven towards a common and parameterized variational form, whose conditional distribution given the image is Gaussian. This is achieved by two networks based on variational auto-encoders (VAEs) and a regularization for this variational approximation. Both of the VAEs, each for one domain, contain a segmentation module, where the source segmentation is trained in a supervised manner, while the target one is trained unsupervisedly. We validated the proposed domain adaptation method using two cardiac segmentation tasks, i.e., the cross-modality (CT and MR) whole heart segmentation and the cross-sequence cardiac MR segmentation. Results show that the proposed method achieved better accuracies compared to two state-of-the-art approaches and demonstrated good potential for cardiac segmentation. Furthermore, the proposed explicit regularization was shown to be effective and efficient in narrowing down the distribution gap between domains, which is useful for unsupervised domain adaptation. Our code and data has been released via https://zmiclab.github.io/projects.html.