Automated layer segmentation of macular OCT images using dual-scale gradient information

Abstract

A novel automated boundary segmentation algorithm is proposed for fast and reliable quantification of nine intra-retinal boundaries in optical coherence tomography (OCT) images. The algorithm employs a two-step segmentation schema based on gradient information in dual scales, utilizing local and complementary global gradient information simultaneously. A shortest path search is applied to optimize the edge selection. The segmentation algorithm was validated with independent manual segmentation and a reproducibility study. It demonstrates high accuracy and reproducibility in segmenting normal 3D OCT volumes. The execution time is about 16 seconds per volume (480×512×128 voxels). The algorithm shows potential for quantifying images from diseased retinas as well.

Multiple-object geometric deformable model for segmentation of macular OCT

“…In terms of specific boundaries, again with the caveat of different data and noting that layer definitions seem to vary somewhat by group and scanner used, we compare for the ILM and BrM. [20,21,33]. The data used in our study is of a very high quality for OCT data, with a mean SNR of 30.6 dB, which might be part of the reason for the improved accuracy of our results.…”

“…These eight layers (and their associated nine boundaries) are the maximal set that are typically segmented from OCT of the macular retina. There has been a large body of work on macular retinal OCT layer segmentation [19][20][21][22][23][24][25][26][27][28][29][30][31][32][33][34][35]. A diverse array of approaches have been investigated including methods based on active contours [19,20] [31], registration [34], and level sets [35].…”

A Review of Algorithms for Segmentation of Retinal Image Data Using Optical Coherence Tomography

“…see Kocaoglu et al, 2007& Ruggeri et al, 2007. Recently, more intensity variation based approaches have also been presented (see Table 1 for details) (Fabritius et al, 2009;Tumlinson et al, 2009;Koprowski et al, 2009 ;Lu et al, 2010 andYang et al, 2010) Among them, it is worthy to mention that Fabritius et al incorporated 3D intensity information to improve the intensity based segmentation and segmented the ILM and RPE directly from the OCT data without massive pre-processing in a very faster manner. (Fabritius et al, 2009).…”

“…(Fabritius et al, 2009). Likewise, Yang et al presented a fast, efficient algorithm that simultaneously utilized both local and global gradient information (Yang et al, 2010). This approach skillfully used an A-scan reduction technique to reduce the execution time to 16 seconds per volume (480x512x128 voxels) without remarkably degrading the accuracy or reproducibility of the results.…”

Retinal Macular Edema Detection Using Optical Coherence Tomography Images

“…An automated method was presented to segment and quantify the cystoid volume for abnormal retina with macular hole which resulted in a good accuracy rate of 99.7% [6]. A high accuracy and reproducibility were demonstrated when a dual-scale gradient information was used for layer segmentation of macular OCT images [7]. A fully automated assessment of Macular Edema from OCT images using Discriminant Analysis classifier has also been reported [8].…”