Kuang Hulin, Liu Xinyuan, Liu Jin, Liu Shulin, Yang Shuai, Liao Weihua, Qiu Wu, Luo Guanghua, Wang Jianxin
Hunan Provincial Key Lab on Bioinformatics, School of Computer Science and Engineering, Central South University, Changsha 410083, China.
Department of Radiology, Xiangya Hospital, Central South University, Changsha 410008, Hunan, China.
Med Image Anal. 2025 Apr;101:103490. doi: 10.1016/j.media.2025.103490. Epub 2025 Feb 6.
Identifying large vessel occlusion (LVO) is of significant importance for the treatment and prognosis of acute ischemic stroke (AIS) patients. CT Angiography (CTA) is commonly used in LVO identification due to its visibility of vessels and short acquisition time. It is challenging to make LVO identification methods focus on vascular regions without vessel segmentation while accurate vessel segmentation is difficult and takes more time. Meanwhile, most existing methods fail to effectively integrate clinical prior knowledge. In this work, we propose VANet, a novel LVO identification network which utilizes coarse-grained vessel feature for feature enhancement and learns asymmetry of two brain hemispheres on CTA of AIS patients. Firstly, we reconstruct 3D CTA scans into 2D based on maximum intensity projection (MIP) to reduce computational complexity and highlight vessel information. Secondly, we design a coarse-grained vessel aware module based on simple edge detection and morphological operations to acquire coarse-grained vessel feature without precise vessel segmentation. Thirdly, we design a vessel-guided feature enhancement that directs the model's attention to vessel areas in the images by utilizing coarse-grained vessel feature. Finally, inspired by the clinical knowledge that LVO can lead to asymmetry in brain, we design an asymmetry learning module utilizing deep asymmetry supervision to keep the patients' inherent asymmetry invariant and using asymmetry computing to acquire effective asymmetry features. We validate the proposed VANet on our private internal and external AIS-LVO datasets which contain 366 and 81 AIS patients, respectively. The results indicate that our proposed VANet achieves an accuracy of 94.54% and an AUC of 0.9685 on the internal dataset, outperforms 11 state-of-the-art methods (including general classification methods and LVO-specific methods). Besides, our method also achieves the best accuracy of 88.89% and AUC of 0.9111 when compared to 11 methods on the external test dataset, implying its good generalization ability. Interpretability analysis shows that the proposed VANet can effectively focus on vascular regions and learn asymmetry features.
识别大血管闭塞（LVO）对于急性缺血性卒中（AIS）患者的治疗和预后具有重要意义。CT血管造影（CTA）因其血管可视性和采集时间短而常用于LVO识别。在不进行血管分割的情况下使LVO识别方法专注于血管区域具有挑战性，而准确的血管分割困难且耗时。同时，大多数现有方法未能有效整合临床先验知识。在这项工作中，我们提出了VANet，这是一种新颖的LVO识别网络，它利用粗粒度血管特征进行特征增强，并在AIS患者的CTA上学习两个脑半球的不对称性。首先，我们基于最大强度投影（MIP）将3D CTA扫描重建为2D，以降低计算复杂度并突出血管信息。其次，我们基于简单边缘检测和形态学操作设计了一个粗粒度血管感知模块，以在不进行精确血管分割的情况下获取粗粒度血管特征。第三，我们设计了一种血管引导的特征增强方法，通过利用粗粒度血管特征将模型的注意力引导到图像中的血管区域。最后，受LVO可导致脑不对称这一临床知识的启发，我们设计了一个不对称学习模块，利用深度不对称监督来保持患者固有的不对称性不变，并使用不对称计算来获取有效的不对称特征。我们在我们的内部和外部AIS-LVO私有数据集上验证了所提出的VANet，这两个数据集分别包含366例和81例AIS患者。结果表明，我们提出的VANet在内部数据集上的准确率达到94.54%，AUC为0.9685，优于11种先进方法（包括通用分类方法和LVO特定方法）。此外，与外部测试数据集上的11种方法相比，我们的方法还实现了88.89%的最佳准确率和0.9111的AUC，这意味着它具有良好的泛化能力。可解释性分析表明，所提出的VANet可以有效地专注于血管区域并学习不对称特征。
