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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 forcerebrovascular segmentation utilize redundant features, eventually leading tothe extraction of low-level features multiple times. Additionally,convolutional neural networks (CNNs) suffer from performance degradation whenthe batch size is small, and deeper networks experience the vanishing gradientproblem. Methods: In this paper, we attempt to solve these limitations andpropose the 3D cerebrovascular attention UNet method, named CV-AttentionUNet,for precise extraction of brain vessel images. We proposed a sequence ofpreprocessing techniques followed by deeply supervised UNet to improve theaccuracy of segmentation of the brain vessels leading to a stroke. To combinethe low and high semantics, we applied the attention mechanism. This mechanismfocuses on relevant associations and neglects irrelevant anatomicalinformation. Furthermore, the inclusion of deep supervision incorporatesdifferent levels of features that prove to be beneficial for networkconvergence. Results: We demonstrate the efficiency of the proposed method bycross-validating with an unlabeled dataset, which was further labeled by us. Webelieve that the novelty of this algorithm lies in its ability to perform wellon both labeled and unlabeled data with image processing-based enhancement. Theresults indicate that our method performed better than the existingstate-of-the-art methods on the TubeTK dataset. Conclusion: The proposed methodwill help in accurate segmentation of cerebrovascular structure leading tostroke

CV-Attention UNet: Attention-based UNet for 3D Cerebrovascular Segmentation of Enhanced TOF-MRA Images