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

2023-09-07 · Lemeng Wang, Wentao Liu, Weijin Xu, Haoyuan Li, Huihua Yang, Feng Gao

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.

📄 PDF Abstract BibTeX arXiv:2309.03477

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Tasks

Image Segmentation Segmentation Semantic Segmentation

DIAS: A Dataset and Benchmark for Intracranial Artery Segmentation in DSA sequences

2023-06-21 · Wentao Liu, Tong Tian, Lemeng Wang, Weijin Xu 외

The automated segmentation of Intracranial Arteries (IA) in Digital Subtraction Angiography (DSA) plays a crucial role in the quantification of vascular morphology, significantly contributing to computer-assisted stroke …

Dimensionality Reduction Image Segmentation Segmentation Semantic Segmentation+1

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Cerebrovascular research heavily relies on quantitative analysis of intracranial arteries from time-of-flight magnetic resonance angiography, yet existing processing pipelines remain limited by inconsistent artery labeli…

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Automated segmentation of intracranial arteries on magnetic resonance angiography (MRA) allows for quantification of cerebrovascular features, which provides tools for understanding aging and pathophysiological adaptatio…

Binary Classification General Classification Segmentation

Deep Feature Fusion via Graph Convolutional Network for Intracranial Artery Labeling

2022-05-22 · Yaxin Zhu, Peisheng Qian, Ziyuan Zhao, Zeng Zeng

Intracranial arteries are critical blood vessels that supply the brain with oxygenated blood. Intracranial artery labels provide valuable guidance and navigation to numerous clinical applications and disease diagnoses. V…

Decoder

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2025-07-01 · Jisoo Kim, Chu-Hsuan Lin, Alberto Ceballos-Arroyo, Ping Liu 외 arxiv

Introduction: Deep learning (DL) models can help detect intracranial aneurysms on CTA, but high false positive (FP) rates remain a barrier to clinical translation, despite improvement in model architectures and strategie…