Temporal consistency-aware network for renal artery segmentation in X-ray angiography.
Authors
Affiliations (5)
Affiliations (5)
- 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.
- School of Biomedical Engineering, Shanghai Jiao Tong University, HuaShan Road, Shanghai, 200030, China. [email protected].
- Department of Cardiology, University Hospital Galway, Old Dublin Road, Galway, H91 D79N, Ireland.
- School of Biomedical Engineering, Shanghai Jiao Tong University, HuaShan Road, Shanghai, 200030, China. [email protected].
Abstract
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.