Yang Botao, Li Chunming, Fezzi Simone, Fan Zehao, Wei Runguo, Chen Yankai, Tavella Domenico, Ribichini Flavio L, Zhang Su, Sharif Faisal, Tu Shengxian
School of Biomedical Engineering, Shanghai Jiao Tong University, HuaShan Road, Shanghai, 200030, China.
Department of Medicine, University of Verona, Viale dell'Università, Verona, 37135, Italy.
Int J Comput Assist Radiol Surg. 2025 Aug 2. doi: 10.1007/s11548-025-03486-y.
Accurate segmentation of renal arteries from X-ray angiography videos is crucial for evaluating renal sympathetic denervation (RDN) procedures but remains challenging due to dynamic changes in contrast concentration and vessel morphology across frames. The purpose of this study is to propose TCA-Net, a deep learning model that improves segmentation consistency by leveraging local and global contextual information in angiography videos.
Our approach utilizes a novel deep learning framework that incorporates two key modules: a local temporal window vessel enhancement module and a global vessel refinement module (GVR). The local module fuses multi-scale temporal-spatial features to improve the semantic representation of vessels in the current frame, while the GVR module integrates decoupled attention strategies (video-level and object-level attention) and gating mechanisms to refine global vessel information and eliminate redundancy. To further improve segmentation consistency, a temporal perception consistency loss function is introduced during training.
We evaluated our model using 195 renal artery angiography sequences for development and tested it on an external dataset from 44 patients. The results demonstrate that TCA-Net achieves an F1-score of 0.8678 for segmenting renal arteries, outperforming existing state-of-the-art segmentation methods.
We present TCA-Net, a deep learning-based model that significantly improves segmentation consistency for renal artery angiography videos. By effectively leveraging both local and global temporal contextual information, TCA-Net outperforms current methods and provides a reliable tool for assessing RDN procedures.
从X射线血管造影视频中准确分割肾动脉对于评估肾交感神经去支配术（RDN）至关重要，但由于各帧间造影剂浓度和血管形态的动态变化，这一任务仍具有挑战性。本研究的目的是提出TCA-Net，这是一种深度学习模型，通过利用血管造影视频中的局部和全局上下文信息来提高分割的一致性。
我们的方法采用了一种新颖的深度学习框架，该框架包含两个关键模块：局部时间窗口血管增强模块和全局血管细化模块（GVR）。局部模块融合多尺度时空特征以改善当前帧中血管的语义表示，而GVR模块集成了解耦注意力策略（视频级和对象级注意力）和门控机制，以细化全局血管信息并消除冗余。为了进一步提高分割的一致性，在训练过程中引入了时间感知一致性损失函数。
我们使用195个肾动脉血管造影序列进行模型开发评估，并在来自44名患者的外部数据集上进行测试。结果表明，TCA-Net在分割肾动脉时的F1分数达到0.8678，优于现有的最先进分割方法。
我们提出了TCA-Net，这是一种基于深度学习的模型，可显著提高肾动脉血管造影视频的分割一致性。通过有效利用局部和全局时间上下文信息，TCA-Net优于当前方法，并为评估RDN手术提供了可靠的工具。
