FlowVM-Net: Enhanced Vessel Segmentation in X-Ray Coronary Angiography Using Temporal Information Fusion

Precise segmentation of continuous vessels in X-ray coronary angiography (XCA) image sequences is pivotal for improving the diagnosis and treatment of coronary artery disease. However, motion artifacts and shadowing in XCA images significantly complicate the segmentation of accurate vessel segmentation. To address these challenges, we propose FlowVM-Net, a dynamic information-enhanced encoder-decoder architecture. The model incorporates an optical flow generation module to create temporal information across image sequences. Additionally, we introduce a wavelet dilated convolution visual state space model block based on VMamba as a fundamental component of the encoder-decoder structure. An attention-based optical flow feature fusion module is designed to effectively integrate sequential spatial features and temporal information. Furthermore, a composite loss function, including boundary difference over union loss, is employed to enhance the accuracy of vessel edge and thin vessel segmentation. We evaluate FlowVM-Net on a dataset of 542 samples, achieving a DSC of 85.17%, a sensitivity of 85.15%, and a quality score of 90.49% on the test set. The proposed network effectively preserves vessel continuity, accurately segments thin vessels, and demonstrates the potential of leveraging dynamic context from XCA sequences for improved coronary artery segmentation. The code for this project is available at: https://github.com/wgyhhhh/FlowVM-Net .