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.

翻译：我们提出了一种面向不同图像模态的脑血管分割半监督域适应框架。尽管现有多种脑血管成像技术，但现有最优方法主要聚焦于单一模态，这可能导致显著的分布偏移，从而影响跨模态的泛化能力。本框架利用标注的血管造影图像和少量标注的静脉造影图像，通过解耦且语义丰富的潜在空间表征异构数据，实现从源域到目标域的图像级适应，同时完成图像到图像翻译与语义分割。此外，我们降低了典型循环架构的复杂度，并最小化对抗训练的使用，从而构建了一个高效、直观且训练稳定的模型。我们在磁共振血管造影和静脉造影图像上评估了该方法。在源域达到最优性能的同时，本方法在目标域获得的Dice系数仅降低8.9%，凸显了其在跨模态鲁棒性脑血管图像分割方面的潜力。