Retinal blood vessel segmentation using a deep learning method based on modified U-NET model

Abstract

<div>Automatic retinal blood vessel segmentation is very crucial to ophthalmology. It plays a vital role in the early detection of several retinal diseases such as Diabetic Retinopathy, hypertension, etc. In recent times, deep learning based methods have attained great success in automatic segmentation of retinal blood vessels from images. In this paper, a U-NET based architecture is proposed to segment the retinal blood vessels from fundus images of the eye. Furthermore, 3 pre-processing algorithms are also proposed to enhance the performance of the system. The proposed architecture has provided significant results. On the basis of experimental evaluation on the publicly available DRIVE data set, it has been observed that the average accuracy (Acc) is .9577, sensitivity (Se) is .7436, specificity (Sp) is .9838 and F1-score is .7931. The proposed system outperforms all recent state of art approaches mentioned in the literature.</div>

Segmentación morfológica y clasificación de niveles para la retinopatía diabética e hipertensiva mediante imágenes oftálmicas y redes convolucionales

“…Table 3 describes the key characteristics of this disease and their classes: (22). In contrast, NA et al employs a modified version of the popular U-NET architecture along with proposed pre-processing algorithms in order to segment the input images; the model works on extracted patches of images obtained after the preprocessing stage (23). Table 4 shows the different sources of information, including the total amount, division into training and validation sets, format, and file type associated with each set, along with their corresponding labeling.…”