Kuang Hulin, Hu Bin, Wan Wenfang, Liu Shulin, Yang Shuai, Liao Weihua, Yuan Li, Luo Guanghua, Qiu Wu
Hunan Provincial Key Lab on Bioinformatics, School of Computer Science and Engineering, Central South University, Changsha 410083, People's Republic of China.
Department of Radiology, Xiangya Hospital, Central South University, Changsha 410008, People's Republic of China.
Phys Med Biol. 2025 Apr 14;70(8). doi: 10.1088/1361-6560/adc8f5.
Acute ischemic stroke (AIS) patients with good collaterals tend to have better outcomes after endovascular therapy. Existing collateral scoring methods rely mainly on vessel segmentation and convolutional neural networks (CNNs), often ignoring bilateral brain differences. This study aims to develop an automated collateral scoring model incorporating bilateral-difference awareness to improve prediction accuracy.In this paper, we propose a new dual-branch hybrid network to achieve vessel-segmentation-free collateral scoring on the CT Angiography (CTA) of 255 AIS patients. Specifically, we first adopt a data preprocessing method based on maximum intensity projection. To capture the differences between the left and right sides of the brain, we propose a novel bilateral-difference awareness module (BDAM). Then we design a hybrid network that consists of a multi-scale module, a CNN branch, a transformer branch and a feature interaction enhancement module in each stage. In addition, to learn more effective features, we propose a novel local enhancement module and a novel global enhancement module (GEM) to strengthen the local features captured by the CNN branch and the global features of the transformer branch, respectively.Experiments on a private clinical dataset with CTA images of 255 AIS patients show that our proposed method achieves an accuracy of 85.49% and an intraclass correlation coefficient of 0.9284 for 3-point collateral scoring, outperforming 13 state-of-the-art methods. Besides, for the binary classification tasks (good vs. non-good collateral scoring, poor vs. non-poor collateral scoring), our proposed method also achieves the best accuracies (89.02% and 92.94%).In this paper, we propose a novel dual-branch hybrid network that incorporates distinct local and GEMs, along with a BDAM, to achieve collateral scoring without the need for vessel segmentation. Our experimental evaluation shows that our model achieves state-of-the-art performance, providing valuable support for improving the efficiency of stroke treatment.
具有良好侧支循环的急性缺血性卒中（AIS）患者在接受血管内治疗后往往有更好的预后。现有的侧支循环评分方法主要依赖于血管分割和卷积神经网络（CNN），常常忽略双侧大脑差异。本研究旨在开发一种纳入双侧差异感知的自动侧支循环评分模型，以提高预测准确性。在本文中，我们提出了一种新的双分支混合网络，用于在255例AIS患者的CT血管造影（CTA）上实现无需血管分割的侧支循环评分。具体而言，我们首先采用基于最大强度投影的数据预处理方法。为了捕捉大脑左右两侧的差异，我们提出了一种新颖的双侧差异感知模块（BDAM）。然后我们设计了一个混合网络，在每个阶段都由一个多尺度模块、一个CNN分支、一个Transformer分支和一个特征交互增强模块组成。此外，为了学习更有效的特征，我们提出了一种新颖的局部增强模块和一种新颖的全局增强模块（GEM），分别加强CNN分支捕捉的局部特征和Transformer分支的全局特征。对一个包含255例AIS患者CTA图像的私人临床数据集进行的实验表明，我们提出的方法在3分侧支循环评分中实现了85.49%的准确率和0.9284的组内相关系数，优于13种先进方法。此外，对于二元分类任务（良好与非良好侧支循环评分、较差与非较差侧支循环评分），我们提出的方法也取得了最佳准确率（89.02%和92.94%）。在本文中，我们提出了一种新颖的双分支混合网络，它结合了独特的局部和全局增强模块以及BDAM，无需血管分割即可实现侧支循环评分。我们的实验评估表明，我们的模型达到了先进水平的性能，为提高中风治疗效率提供了有价值的支持。
