Accurate segmentation of brain vessels is crucial for cerebrovascular disease diagnosis and treatment. However, existing methods face challenges in capturing small vessels and handling datasets that are partially or ambiguously annotated. In this paper, we propose an adaptive semi-supervised approach to address these challenges. Our approach incorporates innovative techniques including progressive semi-supervised learning, adaptative training strategy, and boundary enhancement. Experimental results on 3DRA datasets demonstrate the superiority of our method in terms of mesh-based segmentation metrics. By leveraging the partially and ambiguously labeled data, which only annotates the main vessels, our method achieves impressive segmentation performance on mislabeled fine vessels, showcasing its potential for clinical applications.

翻译：脑部血管的精确分割对于脑血管疾病的诊断和治疗至关重要。然而，现有方法在捕捉细小血管以及处理部分标注或模糊标注数据集方面面临挑战。本文提出一种自适应半监督方法来解决这些问题。该方法融合了渐进式半监督学习、自适应训练策略和边界增强等创新技术。在3DRA数据集上的实验结果表明，我们的方法在基于网格的分割指标上具有优越性。通过利用仅标注主血管的部分且模糊的标注数据，我们的方法在错误标注的细小血管上实现了令人印象深刻的分割性能，展现了其在临床应用中的潜力。