Frangi, Alejandro F

Lin, Fengming

Liu, Qiongyao

MacRaild, Michael

Ravikumar, Nishant

Xia, Yan

English

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

The University of Manchester - Institutional Repository

Adaptive Semi-Supervised Segmentation of Brain Vessels with Ambiguous Labels

https://pure.manchester.ac.uk/ws/files/275123265/2308.03613v1.pdf

Adaptive Semi-Supervised Segmentation of Brain Vessels with Ambiguous Labels

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The University of Manchester - Institutional Repository