With the recent success of computer vision and deep learning, remarkable progress has
been achieved on automatic personal recognition using vein biometrics. However, collecting …

Cerebrovascular imaging is a common examination. Its accurate cerebrovascular
segmentation become an important auxiliary method for the diagnosis and treatment of …

Accurate segmentation of cerebral vasculature and a quantitative assessment of its
morphology is critical to various diagnostic and therapeutic purposes and is pertinent to …

Deep-learning-based methods have achieved state-of-the-art results in cerebrovascular
segmentation. However, it is costly and time-consuming to acquire labeled data because of …

Abstract Background and Objective Accurate cerebrovascular segmentation plays an
important role in the diagnosis of cerebrovascular diseases. Considering the complexity and …

Cerebrovascular segmentation from time-of-flight magnetic resonance angiography (TOF-
MRA) data is of great importance in blood supply structure analysis, diagnosis, and …

Cerebrovascular (ie, cerebral vessel) segmentation is essential for diagnosing and treating
brain diseases. Convolutional neural network models, such as U-Net, are commonly used …

Background In recent years, three-dimensional (3D) printing has been increasingly applied
to the intracranial vasculature for patient-specific surgical planning, training, education, and …

Background As magnetic resonance angiography (MRA) has been increasingly used in the
follow-up of intracranial aneurysms (IAs) as a non-invasive technique, the knowledge …

Automated blood vessel segmentation is critical for biomedical image analysis, as vessel
morphology changes are associated with numerous pathologies. Still, precise segmentation …