Comparison of deep learning-based models for retinal vessels segmentation

With the trend of social diversification and the rapid popularization of information, more and more people use mobile phones to deal with matters in life, study and work, as well as a place of entertainment in their leisure time. People use their eyes at high intensity for a long time. In addition, Low eye care attention greatly increases the chance of developing eye diseases. Such lesions are closely linked to eye diseases such as glaucoma and diabetes, and these lesions are the main cause of blindness. The retina has a complex structure, diverse shapes, and high curvature. It is time-consuming and difficult to diagnose diseases from retinal images. Doctors subjective judgments on images account for a large proportion. In order to make segmentation performance more efficient and reduce manual segmentation errors, an intelligent system that can automatically extract retinal blood vessels from fundus images has become an urgent problem. Medical picture segmentation and the merging of machine learning have gained popularity as a study area with the growth and attention of deep learning. Various models are applied to accurate segmentation of the retina. Through computer feature extraction and sample training of images, it can be directly learned from many data samples. The development of this technology saves time and cost, and at the same time meets the medical needs in remote areas. Doctors can efficiently screen and diagnose through the vascular status of the fundus to determine diabetic retinopathy, glaucoma, microaneurysms and other eye disease fundus manifestations.