Robust multi-view approaches for retinal layer segmentation in glaucoma patients via transfer learning

Abstract

Background: Glaucoma is the leading global cause of irreversible blindness. Glaucoma patients experience
a progressive deterioration of the retinal nervous tissues that begins with a loss of peripheral vision. An early
diagnosis is essential in order to prevent blindness. Ophthalmologists measure the deterioration caused by
this disease by assessing the retinal layers in different regions of the eye, using different optical coherence
tomography (OCT) scanning patterns to extract images, generating different views from multiple parts of
the retina. These images are used to measure the thickness of the retinal layers in different regions.
Methods: We present two approaches for the multi-region segmentation of the retinal layers in OCT
images of glaucoma patients. These approaches can extract the relevant anatomical structures for glaucoma
assessment from three different OCT scan patterns: circumpapillary circle scans, macular cube scans and
optic disc (OD) radial scans. By employing transfer learning to take advantage of the visual patterns present
in a related domain, these approaches use state-of-the-art segmentation modules to achieve a robust, fully
automatic segmentation of the retinal layers. The first approach exploits inter-view similarities by using a
single module to segment all of the scan patterns, considering them as a single domain. The second approach
uses view-specific modules for the segmentation of each scan pattern, automatically detecting the suitable
module to analyse each image.
Results: The proposed approaches produced satisfactory results with the first approach achieving a dice
coefficient of 0.85±0.06 and the second one 0.87±0.08 for all segmented layers. The first approach produced
the best results for the radial scans. Concurrently, the view-specific second approach achieved the best results
for the better represented circle and cube scan patterns.
Conclusions: To the extent of our knowledge, this is the first proposal in the literature for the multi-view
segmentation of the retinal layers of glaucoma patients, demonstrating the applicability of machine learningbased systems for aiding in the diagnosis of this relevant pathology.