# PnP-AdaNet: Plug-and-Play Adversarial Domain Adaptation Network with a Benchmark at Cross-modality Cardiac Segmentation

arXiv: https://arxiv.org/abs/1812.07907 · HTML: https://mlsanity.com/paper/1812.07907/

## Key facts

- Cited by 12 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 19 Dec 2018.

## Built on by (most cited first, 12 of 12)

- [Self-Attentive Spatial Adaptive Normalization for Cross-Modality Domain Adaptation](https://mlsanity.com/paper/2103.03781/) (2021)
- [Consistent Posterior Distributions under Vessel-Mixing: A Regularization for Cross-Domain Retinal Artery/Vein Classification](https://mlsanity.com/paper/2103.09097/) (2021)
- [Self-Supervised 2D/3D Registration for X-Ray to CT Image Fusion](https://mlsanity.com/paper/2210.07611/) (2022)
- [A New Bidirectional Unsupervised Domain Adaptation Segmentation Framework](https://mlsanity.com/paper/2108.07979/) (2021)
- [Reducing Domain Gap in Frequency and Spatial domain for Cross-modality Domain Adaptation on Medical Image Segmentation](https://mlsanity.com/paper/2211.15235/) (2022)
- [Mind The Gap: Alleviating Local Imbalance for Unsupervised Cross-Modality Medical Image Segmentation](https://mlsanity.com/paper/2205.11888/) (2022)
- [MSCDA: Multi-level Semantic-guided Contrast Improves Unsupervised Domain Adaptation for Breast MRI Segmentation in Small Datasets](https://mlsanity.com/paper/2301.02554/) (2023)
- [Domain adaptation based self-correction model for COVID-19 infection segmentation in CT images](https://mlsanity.com/paper/2104.09699/) (2021)
- [Optimal Latent Vector Alignment for Unsupervised Domain Adaptation in Medical Image Segmentation](https://mlsanity.com/paper/2106.08188/) (2021)
- [Multi-organ segmentation: a progressive exploration of learning paradigms under scarce annotation](https://mlsanity.com/paper/2302.03296/) (2023)
- [Plug-and-play Shape Refinement Framework for Multi-site and Lifespan Brain Skull Stripping](https://mlsanity.com/paper/2203.04299/) (2022)
- [Diverse Teaching and Label Propagation for Generic Semi-Supervised Medical Image Segmentation](https://mlsanity.com/paper/2508.08549/) (2025)

## Abstract (arXiv)

Deep convolutional networks have demonstrated the state-of-the-art performance on various medical image computing tasks. Leveraging images from different modalities for the same analysis task holds clinical benefits. However, the generalization capability of deep models on test data with different distributions remain as a major challenge. In this paper, we propose the PnPAdaNet (plug-and-play adversarial domain adaptation network) for adapting segmentation networks between different modalities of medical images, e.g., MRI and CT. We propose to tackle the significant domain shift by aligning the feature spaces of source and target domains in an unsupervised manner. Specifically, a domain adaptation module flexibly replaces the early encoder layers of the source network, and the higher layers are shared between domains. With adversarial learning, we build two discriminators whose inputs are respectively multi-level features and predicted segmentation masks. We have validated our domain adaptation method on cardiac structure segmentation in unpaired MRI and CT. The experimental results with comprehensive ablation studies demonstrate the excellent efficacy of our proposed PnP-AdaNet. Moreover, we introduce a novel benchmark on the cardiac dataset for the task of unsupervised cross-modality domain adaptation. We will make our code and database publicly available, aiming to promote future studies on this challenging yet important research topic in medical imaging.