A Deep Neural Network for Vessel Segmentation of Scanning Laser Ophthalmoscopy Images

• Type: Article in International Conference Proceedings Book
• Date: 2017

Authors

Meyer, MI (Author) Other; Costa, P (Author) Other; Galdran, A (Author) Other; Ana Maria Mendonça (Author) FEUP; Aurélio Campilho (Author) FEUP

Conference proceedings International

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

Pages: 507-515

14th International Conference on Image Analysis and Recognition, ICIAR 2017

5 July 2017 through 7 July 2017

Indexing

ISI Web of Knowledge - 17 Citations

Scopus - 15 Citations

Other information

Authenticus ID: P-00M-X2K

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

Abstract (EN): 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. © Springer International Publishing AG 2017.

Language: English

Type (Professor's evaluation): Scientific

No. of pages: 9