Adaptive Semi-Supervised Segmentation of Brain Vessels with Ambiguous
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Fengming Lin Yan Xia Nishant Ravikumar Qiongyao Liu Michael MacRaild Alejandro F Frangi
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.