  Cardio-cerebrovascular diseases are the leading causes of mortality
worldwide, whose accurate blood vessel segmentation is significant for both
scientific research and clinical usage. However, segmenting
cardio-cerebrovascular structures from medical images is very challenging due
to the presence of thin or blurred vascular shapes, imbalanced distribution of
vessel and non-vessel pixels, and interference from imaging artifacts. These
difficulties make manual or semi-manual segmentation methods highly
time-consuming, labor-intensive, and prone to errors with interobserver
variability, where different experts may produce different segmentations from a
variety of modalities. Consequently, there is a growing interest in developing
automated algorithms. This paper provides an up-to-date survey of deep learning
techniques, for cardio-cerebrovascular segmentation. It analyzes the research
landscape, surveys recent approaches, and discusses challenges such as the
scarcity of accurately annotated data and variability. This paper also
illustrates the urgent needs for developing multi-modality label-efficient deep
learning techniques. To the best of our knowledge, this paper is the first
comprehensive survey of deep learning approaches that effectively segment
vessels in both the heart and brain. It aims to advance automated segmentation
techniques for cardio-cerebrovascular diseases, benefiting researchers and
healthcare professionals.
