Objective: When using single-frame images for coronary vessel segmentation, the small size and complex structure of the vessels often lead to over-segmentation and mis-segmentation. Additionally, limited information from low-quality images result in disrupting the vascular topology. To address this, we introduce temporal information from coronary angiography sequences to aid in segmentation and improve accuracy.

Methods: We establish a dataset SqCS specialized for coronary angiography sequence segmentation and propose a time series-based coronary angiography segmentation network TSNet. Specifically, our proposed TSNet is a multi-input single-output end-to-end U-shaped network that utilizes multiple encoders for simultaneous extraction of spatial features from input sequence frames. It incorporates an edge enhancement method for segmented frames and employs the Temporal and Spatial Attention Unit (TSAU) for refined extraction of temporal and spatial information and fusion of multi-frame features. Our code is publicly available at https://github.com/huigao-II/TSNet.

Results: We validated TSNet on our SqCS dataset, achieving a Dice score of 0.8966, Acc of 0.9906, IoU of 0.8127, clDice of 0.9354, VCA of 1.9027, BIOU of 0.3565 and VCA of 1.9072.

Conclusion: Our method enhances pixel-wise accuracy while addressing vessel discontinuities in low-contrast regions common in single-frame segmentation. It preserves vascular topology and significantly improves edge accuracy.

Significance: Our SqCS dataset provides a foundation for sequence-based coronary angiography vessel segmentation research. The segmentation model trained using our method lays the groundwork for accurate clinical diagnosis and treatment decisions in coronary artery disease.

Keywords: Coronary angiography vessel segmentation; Edge enhancement; Temporal information extraction; Topological structure.

Copyright © 2025 Elsevier Ltd. All rights reserved.

目标： 在使用单帧图像进行冠状血管分割时，血管的小尺寸和复杂结构常常导致过度分割和误分割。此外，低质量图像提供的有限信息会导致破坏血管拓扑结构。为了解决这些问题，我们引入了来自冠状动脉造影序列的时序信息来辅助分割并提高准确性。

方法： 我们建立了一个专门用于冠状动脉造影序列分割的数据集SqCS，并提出了一个基于时间序列的冠状动脉造影分割网络TSNet。具体来说，我们的TSNet是一个多输入单输出端到端U形网络，它利用多个编码器同时提取输入序列帧的空间特征。该方法包括边缘增强方法用于分割后的帧，并采用时空注意力单元（TSAU）进行时间与空间信息的精细提取以及多帧特征融合。我们的代码可在https://github.com/huigao-II/TSNet公开获取。

结果： 我们在我们的SqCS数据集上验证了TSNet，取得了Dice分数为0.8966、准确率（Acc）为0.9906、交并比（IoU）为0.8127、clDice为0.9354、血管对比度评估（VCA）为1.9027和边界交并比（BIOU）为0.3565。

结论： 我们的方法提高了像素级的准确性，同时解决了单帧分割中常见的低对比度区域血管不连续的问题。它保留了血管拓扑结构，并显著提升了边缘准确率。

意义： 我们提供的SqCS数据集为基于序列的冠状动脉造影血管分割研究奠定了基础。使用我们的方法训练的分割模型为冠状动脉疾病的准确临床诊断和治疗决策提供了依据。

关键词： 冠状动脉造影血管分割；边缘增强；时序信息提取；拓扑结构。