Livelayer: A Semi-Automatic Software Program for Segmentation of Layers and Diabetic Macular Edema in Optical Coherence Tomography Images

Abstract

Given the capacity of Optical Coherence Tomography (OCT) imaging to display symptoms of a wide variety of eye diseases and neurological disorders, the need for OCT image segmentation and the corresponding data interpretation is latterly felt more than ever before. In this paper, we wish to address this need by designing a semi-automatic software program for applying reliable segmentation of 8 different macular layers as well as outlining retinal pathologies such as diabetic macular edema. The software accommodates a novel graph-based semi-automatic method, called “Livelayer” which is designed for straightforward segmentation of retinal layers and fluids. This method is chiefly based on Dijkstra’s Shortest Path (SPF) algorithm and the Live-wire function together with some preprocessing operations on the to-be-segmented images. The software is indeed suitable for obtaining detailed segmentation of layers, exact localization of clear or unclear fluid objects and the ground truth, demanding far less endeavor in comparison to a common manual segmentation method. It is also valuable as a tool for calculating the irregularity index in deformed OCT images. The amount of time (seconds) that Livelayer required for segmentation of ILM, IPL-INL, OPL-ONL was much less than that for the manual segmentation, 5s for the ILM (minimum) and 15.57s for the OPL-ONL (maximum). The unsigned errors (pixels) between the semi-automatically labeled and gold standard data was on average 2.7, 1.9, 2.1 for ILM, IPL-INL, OPL-ONL, respectively. The Bland-Altman plots indicated perfect concordance between the Livelayer and the manual algorithm and that they could be used interchangeably. The repeatability error was around one pixel for the OPL-ONL and < 1 for the other two. The dice scores for comparing the two algorithms and for obtaining the repeatability on segmentation of fluid objects were at acceptable levels.

Automatic production of synthetic labelled OCT images using an active shape model

“…The tested segmentation method is a semiautomatic method based on live wire theory [34]. The mean signed and unsigned border positioning errors for each border with different noise levels are presented in Tables 2 and 3.…”

Synthetic OCT Data Generation to Enhance the Performance of Diagnostic Models for Neurodegenerative Diseases

“…Each OCT image stack contains 25 horizontal B-scans (each with 512 A-scans, with automatic real-time tracking in nine frames and axial resolution of 3.8 mm), scanning a macular area of 6 by 6 mm focused on the fovea. Automated segmentation of retinal layer boundaries was performed using a custom-developed graph-based method 27 with reference values presented in Kafieh et al 26 The segmentation results were quality controlled and manually corrected in case of errors by an ophthalmologist using the method in Montazerin et al 28 To account for eye laterality, 3D OCTs from left eyes are flipped and the nasal area is located on the right side of the thickness maps.…”

A Device-Independent, Shape Preserving Retinal Optical Coherence Tomography Image Alignment Method Applying TV-Unet for RPE Layer Detection

“…This subset has been used in [28]. Subset VI is studied in [29] and consists of 193 OCT images from 19 DME patients. This dataset has been acquired from the Heidelberg system version 5.1.…”