# A2V: A Semi-Supervised Domain Adaptation Framework for Brain Vessel Segmentation via Two-Phase Training Angiography-to-Venography Translation

arXiv: https://arxiv.org/abs/2309.06075 · HTML: https://mlsanity.com/paper/2309.06075/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- Builds on 21 of its 34 references that are in the map.
- First posted on arXiv 12 Sep 2023.

## Builds on (most cited first, 21 of 21)

- [The Unreasonable Effectiveness of Deep Features as a Perceptual Metric](https://mlsanity.com/paper/1801.03924/) (2018)
- [Analyzing and Improving the Image Quality of StyleGAN](https://mlsanity.com/paper/1912.04958/) (2019)
- [Encoding in Style: a StyleGAN Encoder for Image-to-Image Translation](https://mlsanity.com/paper/2008.00951/) (2020)
- [Learning to Adapt Structured Output Space for Semantic Segmentation](https://mlsanity.com/paper/1802.10349/) (2018)
- [Domain Adaptation for Medical Image Analysis: A Survey](https://mlsanity.com/paper/2102.09508/) (2021)
- [clDice -- A Novel Topology-Preserving Loss Function for Tubular Structure Segmentation](https://mlsanity.com/paper/2003.07311/) (2020)
- [DatasetGAN: Efficient Labeled Data Factory with Minimal Human Effort](https://mlsanity.com/paper/2104.06490/) (2021)
- [SynthSeg: Segmentation of brain MRI scans of any contrast and resolution without retraining](https://mlsanity.com/paper/2107.09559/) (2021)
- [Automated brain extraction of multi-sequence MRI using artificial neural networks](https://mlsanity.com/paper/1901.11341/) (2019)
- [SynthStrip: Skull-Stripping for Any Brain Image](https://mlsanity.com/paper/2203.09974/) (2022)
- [Unsupervised domain adaptation in brain lesion segmentation with adversarial networks](https://mlsanity.com/paper/1612.08894/) (2016)
- [Synergistic Image and Feature Adaptation: Towards Cross-Modality Domain Adaptation for Medical Image Segmentation](https://mlsanity.com/paper/1901.08211/) (2019)
- [Transfer Learning for Domain Adaptation in MRI: Application in Brain Lesion Segmentation](https://mlsanity.com/paper/1702.07841/) (2017)
- [TransGaGa: Geometry-Aware Unsupervised Image-to-Image Translation](https://mlsanity.com/paper/1904.09571/) (2019)
- [Boundary-weighted Domain Adaptive Neural Network for Prostate MR Image Segmentation](https://mlsanity.com/paper/1902.08128/) (2019)
- [ACT: Semi-supervised Domain-adaptive Medical Image Segmentation with Asymmetric Co-training](https://mlsanity.com/paper/2206.02288/) (2022)
- [Contrastive Semi-supervised Learning for Domain Adaptive Segmentation Across Similar Anatomical Structures](https://mlsanity.com/paper/2208.08605/) (2022)
- [Unsupervised Image-to-Image Translation with Generative Prior](https://mlsanity.com/paper/2204.03641/) (2022)
- [Vessel-CAPTCHA: an efficient learning framework for vessel annotation and segmentation](https://mlsanity.com/paper/2101.09321/) (2021)
- [A New Bidirectional Unsupervised Domain Adaptation Segmentation Framework](https://mlsanity.com/paper/2108.07979/) (2021)
- [Unsupervised Domain Adaptation for Cross-Modality Retinal Vessel Segmentation via Disentangling Representation Style Transfer and Collaborative Consistency Learning](https://mlsanity.com/paper/2201.04812/) (2022)

## Built on by (most cited first, 4 of 4)

- [The TopCoW Challenge -- Topology-Aware Circle of Willis Segmentation for CT and MR Angiography](https://mlsanity.com/paper/2312.17670/) (2023)
- [Disentangling representations of retinal images with generative models](https://mlsanity.com/paper/2402.19186/) (2024)
- [Multi-Domain Brain Vessel Segmentation Through Feature Disentanglement](https://mlsanity.com/paper/2510.00665/) (2025)
- [MID: Mask-Image Distributional Divergence for Evaluating Medical Image Segmentation](https://mlsanity.com/paper/2610.17354/) (2026)

## Abstract (arXiv)

We present a semi-supervised domain adaptation framework for brain vessel segmentation from different image modalities. Existing state-of-the-art methods focus on a single modality, despite the wide range of available cerebrovascular imaging techniques. This can lead to significant distribution shifts that negatively impact the generalization across modalities. By relying on annotated angiographies and a limited number of annotated venographies, our framework accomplishes image-to-image translation and semantic segmentation, leveraging a disentangled and semantically rich latent space to represent heterogeneous data and perform image-level adaptation from source to target domains. Moreover, we reduce the typical complexity of cycle-based architectures and minimize the use of adversarial training, which allows us to build an efficient and intuitive model with stable training. We evaluate our method on magnetic resonance angiographies and venographies. While achieving state-of-the-art performance in the source domain, our method attains a Dice score coefficient in the target domain that is only 8.9% lower, highlighting its promising potential for robust cerebrovascular image segmentation across different modalities.