Segmentation of Preretinal Space in Optical Coherence Tomography Images Using Deep Neural Networks.

Agnieszka Stankiewicz,Elzbieta Marciniak,Tomasz Marciniak,Marcin Stopa,Boguslaw Obara,Adam Dabrowski

doi:10.3390/s21227521

Abstract

This paper proposes an efficient segmentation of the preretinal area between the inner limiting membrane (ILM) and posterior cortical vitreous (PCV) of the human eye in an image obtained with the use of optical coherence tomography (OCT). The research was carried out using a database of three-dimensional OCT imaging scans obtained with the Optovue RTVue XR Avanti device. Various types of neural networks (UNet, Attention UNet, ReLayNet, LFUNet) were tested for semantic segmentation, their effectiveness was assessed using the Dice coefficient and compared to the graph theory techniques. Improvement in segmentation efficiency was achieved through the use of relative distance maps. We also show that selecting a larger kernel size for convolutional layers can improve segmentation quality depending on the neural network model. In the case of PVC, we obtain the effectiveness reaching up to . The proposed solution can be widely used to diagnose vitreomacular traction changes, which is not yet available in scientific or commercial OCT imaging solutions.

Highlights

The preretinal space of the human eye and the pathologies connected with its improper changes have become of interest in recent years [1]
Our work explores the possibility of utilizing deep neural networks (DNN) for the task of preretinal space segmentation and investigates the challenges connected with the specificity of the preretinal space
We have evaluated a set of methods employed for the segmentation of the preretinal space in optical coherence tomography images of the retina

Summary

Introduction

The preretinal space of the human eye and the pathologies connected with its improper changes have become of interest in recent years [1]. The field of ophthalmology has benefited greatly due to the development of noninvasive diagnostic tools such as optical coherence tomography (OCT) [2,3]. This imaging modality uses near-infrared light reflected from the analyzed tissue to illustrate changes in the eye morphology. In the majority of the cases, the process of PVD is asymptomatic [8] It is a consequence of a naturally occurring liquefaction of the vitreous in the aging eye. This phenomenon leads to a progressive separation of posterior cortical vitreous (PCV) [9] from the retina surface, starting from the weakest points of adhesion—the perifoveal quadrants. In the final stages of the detachment, the vitreous separates from the fovea and the optic nerve head

Methods

Results

Conclusion