U-net is an image segmentation technique developed primarily for image segmentation
tasks. These traits provide U-net with a high utility within the medical imaging community …

This paper presents a comprehensive review of retinal blood vessel segmentation based on
deep learning. The geometric characteristics of retinal vessels reflect the health status of …

Retinal blood vessel segmentation in fundus images plays an important role in the early
diagnosis and treatment of retinal diseases. In recent years, the segmentation methods …

Medical image segmentation enables doctors to observe lesion regions better and make
accurate diagnostic decisions. Single-branch models such as U-Net have achieved great …

Accurate segmentation of retinal vessels from fundus images is fundamental for the
diagnosis of numerous diseases of eye, and an automated vessel segmentation method can …

Retinal fundus vessels contain rich geometric features, including both thick and thin vessels,
which is particularly important for accurate clinical diagnosis of cardiovascular diseases …

Retinal fundus images contain plenty of morphological information, so it is particularly
important to realize precise segmentation of the retinal vessels for clinical diagnosis. With …

U-net is an image segmentation technique developed primarily for medical image analysis
that can precisely segment images using a scarce amount of training data. These traits …

In biomedical image analysis, information about the location and appearance of tumors and
lesions is indispensable to aid doctors in treating and identifying the severity of diseases …

摘要病灶精确分割对患者病情评估和治疗方案制定有重要意义, 由于医学图像中病灶与周围组织
的对比度低, 同一疾病病灶边缘和形状存在很大差异, 从而增加了分割难度. U-Net …