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.

翻译：从X射线冠状动脉造影序列中分割主要冠状动脉对于冠状动脉疾病的诊断至关重要。然而，由于边界模糊、辐射对比度不一致、运动模式复杂以及缺乏用于训练的标注图像等问题，该任务具有挑战性。尽管半监督学习可以减轻标注负担，但传统方法在处理复杂的时间动态和不可靠的不确定性量化方面存在困难。为解决这些挑战，我们提出了基于SAM3的师生框架，结合运动感知一致性和渐进式置信度正则化，这是一种用于X射线血管造影视频的半监督血管分割方法。首先，我们的方法利用SAM3独特的可提示概念分割设计，并创新性地构建了一个基于SAM3的师生框架，以最大化教师模型和学生模型的性能潜力。其次，我们通过整合血管掩码变形技术和运动一致性损失来建模复杂的血管动态，从而增强分割效果。针对由边界模糊和对比度极低引起的教师预测不可靠问题，我们进一步提出了渐进式置信感知一致性正则化，以降低不可靠输出的风险。在来自不同机构的三个XCA序列数据集上进行的大量实验表明，SMART在显著减少标注需求的同时实现了最先进的性能，这使其在标注数据稀缺的实际临床应用中具有特别重要的价值。我们的代码可在以下网址获取：https://github.com/qimingfan10/SMART。