A2V: A Semi-Supervised Domain Adaptation Framework for Brain Vessel Segmentation via Two-Phase Training Angiography-to-Venography Translation
arxiv(2023)
摘要
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
cerebrovascular image segmentation across different modalities.
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