Multilevel and Multiscale Deep Neural Network for Retinal Blood Vessel Segmentation

Abstract

Retinal blood vessel segmentation influences a lot of blood vessel-related disorders such as diabetic retinopathy, hypertension, cardiovascular and cerebrovascular disorders, etc. It is found that vessel segmentation using a convolutional neural network (CNN) showed increased accuracy in feature extraction and vessel segmentation compared to the classical segmentation algorithms. CNN does not need any artificial handcrafted features to train the network. In the proposed deep neural network (DNN), a better pre-processing technique and multilevel/multiscale deep supervision (DS) layers are being incorporated for proper segmentation of retinal blood vessels. From the first four layers of the VGG-16 model, multilevel/multiscale deep supervision layers are formed by convolving vessel-specific Gaussian convolutions with two different scale initializations. These layers output the activation maps that are capable to learn vessel-specific features at multiple scales, levels, and depth. Furthermore, the receptive field of these maps is increased to obtain the symmetric feature maps that provide the refined blood vessel probability map. This map is completely free from the optic disc, boundaries, and non-vessel background. The segmented results are tested on Digital Retinal Images for Vessel Extraction (DRIVE), STructured Analysis of the Retina (STARE), High-Resolution Fundus (HRF), and real-world retinal datasets to evaluate its performance. This proposed model achieves better sensitivity values of 0.8282, 0.8979 and 0.8655 in DRIVE, STARE and HRF datasets with acceptable specificity and accuracy performance metrics.

Automatic Segmentation of Retinal Capillaries in Adaptive Optics Scanning Laser Ophthalmoscope Perfusion Images Using a Convolutional Neural Network

“…The separation of the training dataset into four different classes (capillaries, large vessel, image background, image canvas) is an exciting improvement on existing CNNs that have been used for vascular segmentation. Previous CNNs developed for and applied to fundus images have not differentiated between smaller or larger vessels, 26 – 28 likely due to the fact that fundus images do not possess the resolution necessary to visualize capillaries. A more recent CNN designed to segment capillaries in OCTA images was trained on images centered on the fovea (surrounding the foveal avascular zone), 29 where smaller vasculature is more prominent.…”

“… 22 – 25 However, translation of these thresholding algorithms to grayscale AOSLO perfusion images for the purposes of automatically segmenting retinal vasculature has proven challenging, primarily due to the large variations in contrast, brightness, and background signal that can typically manifest in AOSLO perfusion images. Machine learning techniques, such as convolutional neural networks (CNNs), have been developed for fundus 26 – 28 and OCTA 29 images. However, there is a lack of development in comparable techniques for AOSLO images.…”

Efficient BFCN for Automatic Retinal Vessel Segmentation

“…In recent years, with the development of computer vision technology, many fundus blood vessel analysis methods have been proposed [ 4 – 21 ]. The computer can quickly, automatically, and accurately segment retinal blood vessels, which will greatly improve the diagnosis rate and work efficiency of doctors.…”

Improving Retinal Vessels Segmentation via Deep Learning in Salient Region

“…It is a powerful tool for image segmentation [37]. With retinal vessel segmentation, deep learning model is arranged and calculated clearly as [36,38,39]. where, z i is the pixel ith joins into networks.…”

“…In the state-of-the art life, the powerful develop of deep learning speaks up which it overcomes the limit in feature extraction. As a result, in retinal vessel segmentation is not out of that rule [36][37][38][39]. However, these solutions are complex about the parameter and must apply in global image.…”