Due to the lack of automated methods, to diagnose cerebrovascular disease, time-of-flight magnetic resonance angiography (TOF-MRA) is assessed visually, making it time-consuming. The commonly used encoder-decoder architectures for cerebrovascular segmentation utilize redundant features, eventually leading to the extraction of low-level features multiple times. Additionally, convolutional neural networks (CNNs) suffer from performance degradation when the batch size is small, and deeper networks experience the vanishing gradient problem. Methods: In this paper, we attempt to solve these limitations and propose the 3D cerebrovascular attention UNet method, named CV-AttentionUNet, for precise extraction of brain vessel images. We proposed a sequence of preprocessing techniques followed by deeply supervised UNet to improve the accuracy of segmentation of the brain vessels leading to a stroke. To combine the low and high semantics, we applied the attention mechanism. This mechanism focuses on relevant associations and neglects irrelevant anatomical information. Furthermore, the inclusion of deep supervision incorporates different levels of features that prove to be beneficial for network convergence. Results: We demonstrate the efficiency of the proposed method by cross-validating with an unlabeled dataset, which was further labeled by us. We believe that the novelty of this algorithm lies in its ability to perform well on both labeled and unlabeled data with image processing-based enhancement. The results indicate that our method performed better than the existing state-of-the-art methods on the TubeTK dataset. Conclusion: The proposed method will help in accurate segmentation of cerebrovascular structure leading to stroke

翻译：由于缺乏自动化方法，诊断脑血管疾病时，时间飞跃法磁共振血管成像（TOF-MRA）通常依赖视觉评估，导致耗时较长。常用的编码器-解码器架构在脑血管分割中会利用冗余特征，最终多次提取低级特征。此外，当批次尺寸较小时，卷积神经网络（CNN）会出现性能下降，而更深层网络则面临梯度消失问题。方法：本文尝试解决这些局限性，提出了一种名为CV-AttentionUNet的三维脑血管注意力UNet方法，用于精确提取脑部血管图像。我们提出了一系列预处理技术，随后采用深度监督UNet来提高导致中风的脑部血管的分割精度。为了融合低层和高层语义，应用了注意力机制。该机制聚焦于相关关联，忽略无关的解剖信息。此外，深度监督的引入整合了不同层次的特征，这有利于网络收敛。结果：我们通过交叉验证一个未标记数据集（后由我们进行标记）证明了所提方法的有效性。我们认为该算法的新颖性在于其能够通过基于图像处理的增强，在标记和未标记数据上均表现良好。结果表明，我们的方法在TubeTK数据集上的性能优于现有的最先进方法。结论：所提方法将有助于导致中风的脑血管结构的精确分割。