Unsupervised Domain Adaptation for Brain Vessel Segmentation through
  Transwarp Contrastive Learning
Fengming Lin Yan Xia Michael MacRaild Yash Deo Haoran Dou Qiongyao Liu Kun Wu Nishant Ravikumar Alejandro F. Frangi
Unsupervised domain adaptation (UDA) aims to align the labelled source
distribution with the unlabelled target distribution to obtain domain-invariant
predictive models. Since cross-modality medical data exhibit significant intra
and inter-domain shifts and most are unlabelled, UDA is more important while
challenging in medical image analysis. This paper proposes a simple yet potent
contrastive learning framework for UDA to narrow the inter-domain gap between
labelled source and unlabelled target distribution. Our method is validated on
cerebral vessel datasets. Experimental results show that our approach can learn
latent features from labelled 3DRA modality data and improve vessel
segmentation performance in unlabelled MRA modality data.