Uncertainty-Aware Concept and Motion Segmentation for Semi-Supervised Angiography Videos

• URL: http://arxiv.org/abs/2603.00881v1
• Date: Sun, 01 Mar 2026 03:04:43 GMT
• Title: Uncertainty-Aware Concept and Motion Segmentation for Semi-Supervised Angiography Videos
• Authors: Yu Luo, Guangyu Wei, Yangfan Li, Jieyu He, Yueming Lyu,
• Abstract summary: We propose a SAM3-based Teacher-student framework with Motion-Aware consistency and Progressive Confidence Regularization.<n>Our method utilizes SAM3's unique promptable concept segmentation design and innovates a SAM3-based teacher-student framework to maximize the performance potential of both the teacher and the student.
• Score: 15.975499220724044
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Segmentation of the main coronary artery from X-ray coronary angiography (XCA) sequences is crucial for the diagnosis of coronary artery diseases. However, this task is challenging due to issues such as blurred boundaries, inconsistent radiation contrast, complex motion patterns, and a lack of annotated images for training. Although Semi-Supervised Learning (SSL) can alleviate the annotation burden, conventional methods struggle with complicated temporal dynamics and unreliable uncertainty quantification. To address these challenges, we propose SAM3-based Teacher-student framework with Motion-Aware consistency and Progressive Confidence Regularization (SMART), a semi-supervised vessel segmentation approach for X-ray angiography videos. First, our method utilizes SAM3's unique promptable concept segmentation design and innovates a SAM3-based teacher-student framework to maximize the performance potential of both the teacher and the student. Second, we enhance segmentation by integrating the vessel mask warping technique and motion consistency loss to model complex vessel dynamics. To address the issue of unreliable teacher predictions caused by blurred boundaries and minimal contrast, we further propose a progressive confidence-aware consistency regularization to mitigate the risk of unreliable outputs. Extensive experiments on three datasets of XCA sequences from different institutions demonstrate that SMART achieves state-of-the-art performance while requiring significantly fewer annotations, making it particularly valuable for real-world clinical applications where labeled data is scarce. Our code is available at: https://github.com/qimingfan10/SMART.

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