A deep neural network for vessel segmentation of Scanning Laser Ophthalmoscopy images

Authors: Meyer, M.I., Costa, P., Galdran, A., Mendonça, A.M. and Campilho, A.

Journal: Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

Volume: 10317 LNCS

Pages: 507-515

eISSN: 1611-3349

ISBN: 9783319598758

ISSN: 0302-9743

DOI: 10.1007/978-3-319-59876-5_56

Abstract:

Retinal vessel segmentation is a fundamental and well-studied problem in the retinal image analysis field. The standard images in this context are color photographs acquired with standard fundus cameras. Several vessel segmentation techniques have been proposed in the literature that perform successfully on this class of images. However, for other retinal imaging modalities, blood vessel extraction has not been thoroughly explored. In this paper, we propose a vessel segmentation technique for Scanning Laser Opthalmoscopy (SLO) retinal images. Our method adapts a Deep Neural Network (DNN) architecture initially devised for segmentation of biological images (U-Net), to perform the task of vessel segmentation. The model was trained on a recent public dataset of SLO images. Results show that our approach efficiently segments the vessel network, achieving a performance that outperforms the current state-of-the-art on this particular class of images.

Source: Scopus