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

• URL: http://arxiv.org/abs/2309.03477v1
• Date: Thu, 7 Sep 2023 04:44:38 GMT
• Title: TSI-Net: A Timing Sequence Image Segmentation Network for Intracranial Artery Segmentation in Digital Subtraction Angiography
• Authors: Lemeng Wang, Wentao Liu, Weijin Xu, Haoyuan Li, Huihua Yang, Feng Gao
• Abstract summary: We propose a timing sequence image segmentation network with U-shape, called TSI-Net. It incorporates a bi-directional ConvGRU module (BCM) in the encoder, which can input variable-length DSA sequences. The method performs significantly better than state-of-the-art networks in recent years.
• Score: 14.584220472118188
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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.

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