TSI-Net：一种用于数字减影血管造影颅内动脉分割的时序图像分割网络
TSI-Net: A Timing Sequence Image Segmentation Network for Intracranial Artery Segmentation in Digital Subtraction Angiography
AI总结：
提出TSI-Net网络，通过在编码器引入双向ConvGRU模块处理可变长度DSA序列，并加入敏感细节分支监督细小血管，在DIAS数据集上Sen指标达0.797，显著提升颅内动脉分割性能。
摘要：
脑血管疾病是当今世界面临的主要疾病之一。在数字减影血管造影（DSA）序列中自动分割颅内动脉（IA）是诊断血管相关疾病和指导神经介入手术的重要步骤。然而，根据DSA技术的成像原理，单张图像只能显示造影剂内部分的IA。因此，2D DSA分割方法无法捕获完整的IA信息和脑血管疾病的治疗。我们提出了一种U型的时序图像分割网络，称为TSI-Net，它在编码器中引入了双向ConvGRU模块（BCM）。该网络可以输入可变长度的DSA序列，保留过去和未来的信息，并将其分割为2D图像。此外，我们在末端引入了敏感细节分支（SDB）用于监督细小血管。在DSA序列数据集DIAS上进行实验，该方法的性能显著优于近年来的最先进网络。特别地，它达到了0.797的Sen评估指标，与其他方法相比提升了3%。
英文摘要：
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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