# Unsupervised Domain Adaptation via Disentangled Representations: Application to Cross-Modality Liver Segmentation

arXiv: https://arxiv.org/abs/1907.13590 · HTML: https://mlsanity.com/paper/1907.13590/

## Key facts

- Cited by 50 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 31 Jul 2019.

## Built on by (most cited first, 30 of 50)

- [Domain Adaptation for Medical Image Analysis: A Survey](https://mlsanity.com/paper/2102.09508/) (2021)
- [Anatomically-Controllable Medical Image Generation with Segmentation-Guided Diffusion Models](https://mlsanity.com/paper/2402.05210/) (2024)
- [CrossMoDA 2021 challenge: Benchmark of Cross-Modality Domain Adaptation techniques for Vestibular Schwannoma and Cochlea Segmentation](https://mlsanity.com/paper/2201.02831/) (2022)
- [Learning Disentangled Representations in the Imaging Domain](https://mlsanity.com/paper/2108.12043/) (2021)
- [Bridging the Gap between Events and Frames through Unsupervised Domain Adaptation](https://mlsanity.com/paper/2109.02618/) (2021)
- [Orthogonal Jacobian Regularization for Unsupervised Disentanglement in Image Generation](https://mlsanity.com/paper/2108.07668/) (2021)
- [On-the-Fly Test-time Adaptation for Medical Image Segmentation](https://mlsanity.com/paper/2203.05574/) (2022)
- [MT-UDA: Towards Unsupervised Cross-modality Medical Image Segmentation with Limited Source Labels](https://mlsanity.com/paper/2203.12454/) (2022)
- [Deep learning for unsupervised domain adaptation in medical imaging: Recent advancements and future perspectives](https://mlsanity.com/paper/2308.01265/) (2023)
- [Source-free unsupervised domain adaptation for cross-modality abdominal multi-organ segmentation](https://mlsanity.com/paper/2111.12221/) (2021)
- [One Model to Unite Them All: Personalized Federated Learning of Multi-Contrast MRI Synthesis](https://mlsanity.com/paper/2207.06509/) (2022)
- [Fr'echet Radiomic Distance (FRD): A Versatile Metric for Comparing Medical Imaging Datasets](https://mlsanity.com/paper/2412.01496/) (2024)
- [CDDSA: Contrastive Domain Disentanglement and Style Augmentation for Generalizable Medical Image Segmentation](https://mlsanity.com/paper/2211.12081/) (2022)
- [MMOTU: A Multi-Modality Ovarian Tumor Ultrasound Image Dataset for Unsupervised Cross-Domain Semantic Segmentation](https://mlsanity.com/paper/2207.06799/) (2022)
- [Unsupervised domain adaptation for cross-modality liver segmentation via joint adversarial learning and self-learning](https://mlsanity.com/paper/2109.05664/) (2021)
- [Target and Task specific Source-Free Domain Adaptive Image Segmentation](https://mlsanity.com/paper/2203.15792/) (2022)
- [FedNorm: Modality-Based Normalization in Federated Learning for Multi-Modal Liver Segmentation](https://mlsanity.com/paper/2205.11096/) (2022)
- [Decomposition-based Unsupervised Domain Adaptation for Remote Sensing Image Semantic Segmentation](https://mlsanity.com/paper/2404.04531/) (2024)
- [CellTranspose: Few-shot Domain Adaptation for Cellular Instance Segmentation](https://mlsanity.com/paper/2212.14121/) (2022)
- [Controllable cardiac synthesis via disentangled anatomy arithmetic](https://mlsanity.com/paper/2107.01748/) (2021)
- [GAN-based disentanglement learning for chest X-ray rib suppression](https://mlsanity.com/paper/2110.09134/) (2021)
- [Normalization Perturbation: A Simple Domain Generalization Method for Real-World Domain Shifts](https://mlsanity.com/paper/2211.04393/) (2022)
- [Explainable, Domain-Adaptive, and Federated Artificial Intelligence in Medicine](https://mlsanity.com/paper/2211.09317/) (2022)
- [TRACE: Temporally Reliable Anatomically-Conditioned 3D CT Generation with Enhanced Efficiency](https://mlsanity.com/paper/2507.00802/) (2025)
- [Generalizing Nucleus Recognition Model in Multi-source Images via Pruning](https://mlsanity.com/paper/2107.02500/) (2021)
- [Reverse Engineering Breast MRIs: Predicting Acquisition Parameters Directly from Images](https://mlsanity.com/paper/2303.04911/) (2023)
- [Deep Learning for Breast MRI Style Transfer with Limited Training Data](https://mlsanity.com/paper/2301.02069/) (2023)
- [Data efficient deep learning for medical image analysis: A survey](https://mlsanity.com/paper/2310.06557/) (2023)
- [Deep Class-Specific Affinity-Guided Convolutional Network for Multimodal Unpaired Image Segmentation](https://mlsanity.com/paper/2101.01513/) (2021)
- [Navigating Distribution Shifts in Medical Image Analysis: A Survey](https://mlsanity.com/paper/2411.05824/) (2024)

## Abstract (arXiv)

A deep learning model trained on some labeled data from a certain source domain generally performs poorly on data from different target domains due to domain shifts. Unsupervised domain adaptation methods address this problem by alleviating the domain shift between the labeled source data and the unlabeled target data. In this work, we achieve cross-modality domain adaptation, i.e. between CT and MRI images, via disentangled representations. Compared to learning a one-to-one mapping as the state-of-art CycleGAN, our model recovers a many-to-many mapping between domains to capture the complex cross-domain relations. It preserves semantic feature-level information by finding a shared content space instead of a direct pixelwise style transfer. Domain adaptation is achieved in two steps. First, images from each domain are embedded into two spaces, a shared domain-invariant content space and a domain-specific style space. Next, the representation in the content space is extracted to perform a task. We validated our method on a cross-modality liver segmentation task, to train a liver segmentation model on CT images that also performs well on MRI. Our method achieved Dice Similarity Coefficient (DSC) of 0.81, outperforming a CycleGAN-based method of 0.72. Moreover, our model achieved good generalization to joint-domain learning, in which unpaired data from different modalities are jointly learned to improve the segmentation performance on each individual modality. Lastly, under a multi-modal target domain with significant diversity, our approach exhibited the potential for diverse image generation and remained effective with DSC of 0.74 on multi-phasic MRI while the CycleGAN-based method performed poorly with a DSC of only 0.52.