DAVS-NET: Dense Aggregation Vessel Segmentation Network for retinal vasculature detection in fundus images.

Mohsin Raza,Khuram Naveed,Amir Afaq,Nema Salem,Hussain Ahmad Madni,Mui-Zzud- Din,Mohammad A U Khan,Awais Akram,Le Hoang Son

doi:10.1371/journal.pone.0261698

Abstract

In this era, deep learning-based medical image analysis has become a reliable source in assisting medical practitioners for various retinal disease diagnosis like hypertension, diabetic retinopathy (DR), arteriosclerosis glaucoma, and macular edema etc. Among these retinal diseases, DR can lead to vision detachment in diabetic patients which cause swelling of these retinal blood vessels or even can create new vessels. This creation or the new vessels and swelling can be analyzed as biomarker for screening and analysis of DR. Deep learning-based semantic segmentation of these vessels can be an effective tool to detect changes in retinal vasculature for diagnostic purposes. This segmentation task becomes challenging because of the low-quality retinal images with different image acquisition conditions, and intensity variations. Existing retinal blood vessels segmentation methods require a large number of trainable parameters for training of their networks. This paper introduces a novel Dense Aggregation Vessel Segmentation Network (DAVS-Net), which can achieve high segmentation performance with only a few trainable parameters. For faster convergence, this network uses an encoder-decoder framework in which edge information is transferred from the first layers of the encoder to the last layer of the decoder. Performance of the proposed network is evaluated on publicly available retinal blood vessels datasets of DRIVE, CHASE_DB1, and STARE. Proposed method achieved state-of-the-art segmentation accuracy using a few number of trainable parameters.

Highlights

Detection of potential blindness diseases is vital to treat their progression and avoid vision loss, for instance, Aging based Mocular Degeneration (AMD), Diabetic Retinopathy (DR) and Hypertension Retinopathy (HR) [1]
Semantic segmentation is well suited for retinal vessel segmentation since detection of tiniest of vessels is vital for analysis and diagnosis of retinal disease
The Deep Neural Networks (DNNs) used for segmentation are not local enough in their operation and as a consequence, they do not classify each pixel for detection of a vessel leading to loss of minor and tiny vessels

Summary

Introduction

Detection of potential blindness diseases is vital to treat their progression and avoid vision loss, for instance, Aging based Mocular Degeneration (AMD), Diabetic Retinopathy (DR) and Hypertension Retinopathy (HR) [1]. DAVS-NET: Dense Aggregation Vessel Segmentation Network and Glaucoma is useful for availing cost effective remedies. Semantic segmentation is regarded as a fundamental application in computer vision where pixel-wise classification is performed for all the pixels present in the image. This approach is able differentiate between pixels belonging to objects and those belonging to the background leading to the detection of tiniest objects. Deep networks for vessel detection use many convolutional and pooling layers which cause vanishing gradient problems. This loss of spatial information degrades the overall performance of pixel-wise classification. Res-Nets caused the feature transfer impedance problem that was later covered by Dense-Net [36] through deep feature concatenation

Methods

Results

Conclusion