DSNet: A Spatio-Temporal Consistency Network for Cerebrovascular Segmentation in Digital Subtraction Angiography Sequences

Digital Subtraction Angiography (DSA) sequences serve as the foremost diagnostic standard for cerebrovascular diseases (CVDs). Accurate cerebrovascular segmentation in DSA sequences assists clinicians in analyzing pathological changes and pinpointing lesions. However, existing methods commonly utilize a single frame extracted from DSA sequences for cerebrovascular segmentation, disregarding the inherent temporal information within these sequences. This rich temporal information has the potential to achieve better segmentation coherence while reducing the interference caused by artifacts. Therefore, in this paper, we propose a spatio-temporal consistency network for cerebrovascular segmentation in DSA sequences, named DSNet, which fully exploits the information of DSA sequences. Specifically, our DSNet comprises a dual-branch encoder and a dual-branch decoder. The encoder consists of a temporal encoding branch (TEB) and a spatial encoding branch (SEB). The TEB is designed to capture dynamic vessel flow information and the SEB is utilized to extract static vessel structure information. To effectively capture the correlations among sequential frames, a dynamic frame reweighting module is designed to adjust the weights of the frames. In bottleneck, we exploit a spatio-temporal feature alignment (STFA) module to fuse the features from the encoder to achieve a more comprehensive vascular representation. Moreover, DSNet employs unsupervised loss for consistency regularization between the dual output from the decoder during training. Experimental results demonstrate that DSNet outperforms existing methods, achieving a Dice score of 89.34% for cerebrovascular segmentation.