<p xmlns:xsi="http://www.w3.org/2001/XMLSchema-instance" class="first" dir="auto"
id="d6042465e99">Cerebrovascular imaging is a common examination. Its accurate cerebrovascular
segmentation
become an important auxiliary method for the diagnosis and treatment of cerebrovascular
diseases, which has received extensive attention from researchers. Deep learning is
a heuristic method that encourages researchers to derive answers from the images by
driving datasets. With the continuous development of datasets and deep learning theory,
it has achieved important success for cerebrovascular segmentation. Detailed survey
is an important reference for researchers. To comprehensively analyze the newest cerebrovascular
segmentation, we have organized and discussed researches centered on deep learning.
This survey comprehensively reviews deep learning for cerebrovascular segmentation
since 2015, it mainly includes sliding window based models, U-Net based models, other
CNNs based models, small-sample based models, semi-supervised or unsupervised models,
fusion based models, Transformer based models, and graphics based models. We organize
the structures, improvement, and important parameters of these models, as well as
analyze development trends and quantitative assessment. Finally, we have discussed
the challenges and opportunities of possible research directions, hoping that our
survey can provide researchers with convenient reference.
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