Sine-Net: A fully convolutional deep learning architecture for retinal blood vessel segmentation

• Published in: Engineering science and technology, an international journal an international journal, 2021-04, Vol.24 (2), p.271-283
• Main Authors: Atli, İbrahim, Gedik, Osman Serdar
• Format: Article
• Language: English
• Subjects: Blood vessel segmentation; Deep learning; Fully convolutional neural network; Sine-Net
• ISSN: 2215-0986; 2215-0986
• DOI: 10.1016/j.jestch.2020.07.008

Segmentation of blood vessels becomes an initial critical step in medical imaging because it is a key item for the diagnosis of many diseases in different fields including ophthalmology, neurosurgery, oncology, cardiology and laryngology. An automated tool for vessel segmentation can assist clinicians and contribute to patient treatment scheduling. However, it is still a challenging problem due to various conditions existing in images such as pathology, noise and poor contrast. Deep learning architectures typically achieve the-state-of-the-art performances in machine vision applications due to high contextual feature generations. This paper introduces a deep learning architecture for fully automated blood vessel segmentation. We propose a novel model, called Sine-Net, that first applies up-sampling and then down-sampling for catching thin and thick vessel features, respectively. We also include residuals to carry more contextual information to the deeper levels of the architecture. Deep networks may perform better if inputs are appropriately pre-processed. Thus, we conduct tests on our network with and without pre-processing applied to input images. We present experimental validations on retinal images of 3 publicly available databases (STARE, CHASE_DB1 and DRIVE) and compare results in terms of sensitivity, specificity, accuracy and area under curve metrics. Our results demonstrate that Sine-Net outperforms the methods proposed in the literature in some of the metrics. In addition, the method has the potential to be used in clinical applications thanks to its decent execution time, high accuracy and robustness.