TSI-NET: A Timing Sequence Image Segmentation Network for Intracranial Artery Segmentation in Digital Subtraction Angiography

Automatic segmentation of intracranial arteries (IA) in digital subtraction angiography (DSA) sequences is an important step in the diagnosis of vascular-related diseases. While a single image can only show part of the IA within the contrast medium, according to the imaging principle of DSA technology, we propose a U-shaped temporal image segmentation network, called TSI-Net, which incorporates a bi-directional ConvGRU module (BCM) in the encoder, which can input variable-length DSA sequences, retain past and future information, and segment them into 2D images. In addition, we introduced Sensitive Detail Branching (SDB) to supervise fine vessel segmentation. Experimenting on the DSA sequence dataset DIAS, the method has performed significantly better than state-of-the-art networks in recent years. In particular, it achieves a sensitivity evaluation metric of 0.797, which is a 3% improvement compared to other methods.