  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
