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

TL;DR

TSI-Net is a novel segmentation network that leverages sequence information in DSA images to improve intracranial artery segmentation, aiding cerebrovascular disease diagnosis and treatment.

Contribution

Introduces TSI-Net with a bi-directional ConvGRU module and a sensitive detail branch for enhanced sequence-based artery segmentation.

Findings

Outperforms state-of-the-art networks on DIAS dataset

Achieves 0.797 sensitivity, 3% higher than previous methods

Effectively captures complete artery information across DSA sequences

Abstract

Cerebrovascular disease is one of the major diseases facing the world today. Automatic segmentation of intracranial artery (IA) in digital subtraction angiography (DSA) sequences is an important step in the diagnosis of vascular related diseases and in guiding neurointerventional procedures. While, a single image can only show part of the IA within the contrast medium according to the imaging principle of DSA technology. Therefore, 2D DSA segmentation methods are unable to capture the complete IA information and treatment of cerebrovascular diseases. We propose A timing sequence image segmentation network with U-shape, called TSI-Net, which incorporates a bi-directional ConvGRU module (BCM) in the encoder. The network incorporates a bi-directional ConvGRU module (BCM) in the encoder, which can input variable-length DSA sequences, retain past and future information, segment them into 2D images. In addition, we introduce a sensitive detail branch (SDB) at the end for supervising fine vessels. Experimented on the DSA sequence dataset DIAS, the method performs significantly better than state-of-the-art networks in recent years. In particular, it achieves a Sen evaluation metric of 0.797, which is a 3% improvement compared to other methods.