Segmentation of retinal vessels on fundus images plays a critical role in the diagnosis of
micro-vascular and ophthalmological diseases. Although being extensively studied, this task …

The structure of the retinal vascular tree can reflect many indicators of ophthalmic health
status. Retinal vessel segmentation, an important basis for the quantitative analysis of …

Automatic retinal blood vessel segmentation is a key link in the diagnosis of ophthalmic
diseases. Recent deep learning methods have achieved high accuracy in vessel …

Vessel segmentation is crucial in many medical image applications, such as detecting
coronary stenoses, retinal vessel diseases and brain aneurysms. However, achieving high …

Retinal blood vessels structure analysis is an important step in the detection of ocular
diseases such as diabetic retinopathy and retinopathy of prematurity. Accurate tracking and …

Semantic segmentation of panoramic images plays a key role in many applications, such as
security monitoring and autonomous driving. With the rapid development of deep learning …

The automatic artery/vein (A/V) classification in retinal fundus images plays a significant role
in detecting vascular abnormalities and could speed up the diagnosis of various systemic …

Automatic segmentation and centerline extraction of blood vessels from retinal fundus
images is an essential step to measure the state of retinal blood vessels and achieve the …

Background This study aims to develop a venipuncture robot to replace manual
venipuncture to ease the heavy workload, lower the risk of 2019‐nCoV infection, and boost …

Accurate and fast recognition of medical images is a key step for clinical diagnosis. With the
rapid development of convolutional neural networks in image processing, the convolutional …