Geng Chen, Lu Yucheng, Xue Peiyang, Dai Bin, Bao Yifang, Bai Dunhui, Li Yuxin, Dai Yakang
Suzhou Institute of Biomedical Engineering and Technology, Chinese Academy of Sciences, 88 Keling Road, Suzhou 215163, China; Jinan Guoke Medical Technology Development Co. Ltd., Jinan 250101, China; Key Laboratory of Biomedical Imaging Science and System, Chinese Academy of Sciences, China; State Key Laboratory of Biomedical Imaging Science and System, China.
Department of Radiology, Huashan Hospital, Fudan University, 12 Wulumuqi Middle Road, Shanghai 200040, China; Institute of Functional and Molecular Medical Imaging, Fudan University, Shanghai 200040, China.
Comput Methods Programs Biomed. 2025 Sep;269:108906. doi: 10.1016/j.cmpb.2025.108906. Epub 2025 Jun 10.
Cerebral aneurysms are a type of cerebrovascular disease that poses a severe threat to life and health. Early screening using Time-of-Flight Magnetic Resonance Angiography (TOF-MRA) can effectively reduce the risk of rupture. Despite the importance of early detection, manual image screening remains a laborious and inefficient process. The current thrust of research in computer-aided detection (CAD) methods is to refine neural networks to improve diagnostic accuracy. In our preliminary work, we discovered that utilizing arterial contour as external knowledge guidance can substantially enhance the detection capabilities of existing networks, thus providing a new perspective for optimizing aneurysm detection techniques.
In this paper, we introduce an innovative approach to building a cerebral aneurysm detection model that employs artery fragment as external guidance data. We propose a hypothesis regarding the optimal distribution pattern of knowledge-guided data based on the brain artery volume of interest (VOI), and based on this, we have developed an end-to-end fully automatic and data-adaptive artery fragment generation method tailored for both training and testing data. Utilizing a multicenter dataset, we tested the performance enhancement capabilities of this method for two commonly used vascular networks, SE-3D UNet and VNet. Furthermore, we conducted a comparative analysis with other guidance methods using the best-performing model to elucidate the mechanisms behind the improved guidance efficacy of our approach.
This study amassed a total of 500 cases of 3.0T TOF-MRA data from 13 devices across 6 hospitals, with 400 cases designated as the training set and 100 cases as the test set, while data from one device was exclusively used for testing. The proposed method showed significant improvements for both SE-3D UNet and VNet. Specifically, SE 3D UNET saw a 13.89 % increase in sensitivity while maintaining a false positives per case (FPs/case) of 0.63. For VNet, the FPs/case was reduced by 20 %, with a slight improvement in sensitivity. Compared to other guidance methods, our approach achieved optimal levels in various metrics and exhibited stronger robustness on unfamiliar datasets.
This study presents an artery fragment-guided approach that enhances the detection of cerebral aneurysms in TOF-MRA imaging. It not only outperforms our previous work but also excels when compared to alternative guidance methods. This approach offers a compelling knowledge-guided strategy for cerebral aneurysm detection.
脑动脉瘤是一种对生命和健康构成严重威胁的脑血管疾病。使用时间飞跃磁共振血管造影（TOF-MRA）进行早期筛查可有效降低破裂风险。尽管早期检测很重要，但手动图像筛查仍然是一项费力且低效的过程。当前计算机辅助检测（CAD）方法的研究重点是改进神经网络以提高诊断准确性。在我们的初步工作中，我们发现利用动脉轮廓作为外部知识指导可以显著增强现有网络的检测能力，从而为优化动脉瘤检测技术提供了新的视角。
在本文中，我们介绍了一种创新方法来构建以动脉片段作为外部指导数据的脑动脉瘤检测模型。我们基于感兴趣的脑动脉体积（VOI）提出了关于知识指导数据的最佳分布模式的假设，并在此基础上开发了一种针对训练和测试数据量身定制的端到端全自动且数据自适应的动脉片段生成方法。利用多中心数据集，我们测试了该方法对两种常用血管网络SE-3D UNet和VNet的性能增强能力。此外，我们使用性能最佳的模型与其他指导方法进行了对比分析，以阐明我们方法提高指导效果背后的机制。
本研究共收集了来自6家医院13台设备的500例3.0T TOF-MRA数据，其中400例作为训练集，100例作为测试集，而来自一台设备的数据专门用于测试。所提出的方法对SE-3D UNet和VNet均有显著改进。具体而言，SE 3D UNET的灵敏度提高了13.89%，同时每例假阳性（FPs/病例）保持在0.63。对于VNet，每例假阳性减少了20%，灵敏度略有提高。与其他指导方法相比，我们的方法在各项指标上均达到了最优水平，并且在不熟悉的数据集中表现出更强的鲁棒性。
本研究提出了一种动脉片段引导方法，可增强TOF-MRA成像中脑动脉瘤的检测。它不仅优于我们之前的工作，而且与其他指导方法相比也表现出色。这种方法为脑动脉瘤检测提供了一种引人注目的知识引导策略。
