Two-dimensional segmentation of the retinal vascular network from optical coherence tomography

作者：Rodrigues Pedro^*; Guimaraes Pedro; Santos Torcato; Simao Silvia; Miranda Telmo; Serranho Pedro; Bernardes Rui

来源：Journal of Biomedical Optics, 2013, 18(12): 126011.

DOI：10.1117/1.JBO.18.12.126011

• segmentation
• support vector machines
• optical coherence tomography
• retina
• vascular network
• supervised learning

The automatic segmentation of the retinal vascular network from ocular fundus images has been performed by several research groups. Although different approaches have been proposed for traditional imaging modalities, only a few have addressed this problem for optical coherence tomography (OCT). Furthermore, these approaches were focused on the optic nerve head region. Compared to color fundus photography and fluorescein angiography, two-dimensional ocular fundus reference images computed from three-dimensional OCT data present additional problems related to system lateral resolution, image contrast, and noise. Specifically, the combination of system lateral resolution and vessel diameter in the macular region renders the process particularly complex, which might partly explain the focus on the optic disc region. In this report, we describe a set of features computed from standard OCT data of the human macula that are used by a supervised-learning process (support vector machines) to automatically segment the vascular network. For a set of macular OCT scans of healthy subjects and diabetic patients, the proposed method achieves 98% accuracy, 99% specificity, and 83% sensitivity. This method was also tested on OCT data of the optic nerve head region achieving similar results.

• 出版日期2013-12