An automated framework for 3D serous pigment epithelium detachment segmentation in SD-OCT images.
Ontology highlight
ABSTRACT:
SUBMITTER: Sun Z
PROVIDER: S-EPMC4761989 | biostudies-other | 2016 Feb
REPOSITORIES: biostudies-other
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Project description:To automatically identify which spectral-domain optical coherence tomography (SD-OCT) scans will provide reliable automated layer segmentations for more accurate layer thickness analyses in population studies.Six hundred ninety macular SD-OCT image volumes (6.0 × 6.0 × 2.3 mm(3)) were obtained from one eyes of 690 subjects (74.6 ± 9.7 [mean ± SD] years, 37.8% of males) randomly selected from the population-based Rotterdam Study. The dataset consisted of 420 OCT volumes with successful automated retinal nerve fiber layer (RNFL) segmentations obtained from our previously reported graph-based segmentation method and 270 volumes with failed segmentations. To evaluate the reliability of the layer segmentations, we have developed a new metric, segmentability index SI, which is obtained from a random forest regressor based on 12 features using OCT voxel intensities, edge-based costs, and on-surface costs. The SI was compared with well-known quality indices, quality index (QI), and maximum tissue contrast index (mTCI), using receiver operating characteristic (ROC) analysis.The 95% confidence interval (CI) and the area under the curve (AUC) for the QI are 0.621 to 0.805 with AUC 0.713, for the mTCI 0.673 to 0.838 with AUC 0.756, and for the SI 0.784 to 0.920 with AUC 0.852. The SI AUC is significantly larger than either the QI or mTCI AUC (P < 0.01).The segmentability index SI is well suited to identify SD-OCT scans for which successful automated intraretinal layer segmentations can be expected.Interpreting the quantification of SD-OCT images requires the underlying segmentation to be reliable, but standard SD-OCT quality metrics do not predict which segmentations are reliable and which are not. The segmentability index SI presented in this study does allow reliable segmentations to be identified, which is important for more accurate layer thickness analyses in research and population studies.
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Project description:The purpose of this study was to evaluate focal damage in the retinal pigment epithelium (RPE) layer in serous retinal pigment epithelium detachment (PED) with multi-contrast optical coherence tomography (OCT), which is capable of simultaneous measurement of OCT angiography, polarization-sensitive OCT and standard OCT images. We evaluated 37 eyes with age-related macular degeneration that had serous PED. Focal RPE damage was indicated by hyper-transmission beneath the RPE-Bruch's membrane band in standard OCT images. Distribution of RPE melanin was calculated using the dataset from multi-contrast OCT. Twenty-four points with hyper-transmission were detected in 21 of the 37 eyes. Standard OCT images failed to show disruption of the RPE-Bruch's membrane band at 5 of the 24 hyper-transmission points. Conversely, multi-contrast OCT images clearly showed melanin defects in the RPE-Bruch's membrane band at all points. Areas of melanin defects with disruption of the RPE-Bruch's membrane band were significantly larger than those without disruption. The volume of intraretinal hyper-reflective foci was significantly larger in eyes with hyper-transmission than that in eyes without hyper-transmission. Multi-contrast OCT is more sensitive than standard OCT for displaying changes at the RPE-Bruch's membrane band when there are small areas of RPE damage.
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Project description:The purpose of this study was to quantitatively evaluate retinal pigment epithelium (RPE) changes in serous pigment epithelial detachment (PED) among patients with age-related macular degeneration by means of prototype multi-contrast optical coherence tomography (OCT), which is capable of simultaneous collection of OCT angiography, polarization-sensitive OCT, and standard OCT images. We evaluated 26 eyes of 21 patients with serous PED. RPE-melanin OCT images were calculated from the multi-contrast OCT dataset and compared with near-infrared autofluorescence images. An active RPE lesion was defined as an area of thickened RPE-melanin (≥ 70 μm; RPE70) on RPE-melanin OCT. Each PED area was divided into peak and slope regions. RPE70 area ratios were compared with the maximum PED height, PED area, PED volume, and slope area ratio (area of slope region/area of whole PED). RPE-melanin OCT images were consistent with near-infrared autofluorescence images. The RPE70 area ratio in the slope region was significantly negatively correlated with the slope area ratio. Development of active RPE lesions in the slope region was correlated with the PED configuration. Multi-contrast OCT is useful for objective evaluation of changes in the RPE in patients with age-related macular degeneration.
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Project description:MotivationMorphological analyses with flatmount fluorescent images are essential to retinal pigment epithelial (RPE) aging studies and thus require accurate RPE cell segmentation. Although rapid technology advances in deep learning semantic segmentation have achieved great success in many biomedical research, the performance of these supervised learning methods for RPE cell segmentation is still limited by inadequate training data with high-quality annotations.ResultsTo address this problem, we develop a Self-Supervised Semantic Segmentation (S4) method that utilizes a self-supervised learning strategy to train a semantic segmentation network with an encoder-decoder architecture. We employ a reconstruction and a pairwise representation loss to make the encoder extract structural information, while we create a morphology loss to produce the segmentation map. In addition, we develop a novel image augmentation algorithm (AugCut) to produce multiple views for self-supervised learning and enhance the network training performance. To validate the efficacy of our method, we applied our developed S4 method for RPE cell segmentation to a large set of flatmount fluorescent microscopy images, we compare our developed method for RPE cell segmentation with other state-of-the-art deep learning approaches. Compared with other state-of-the-art deep learning approaches, our method demonstrates better performance in both qualitative and quantitative evaluations, suggesting its promising potential to support large-scale cell morphological analyses in RPE aging investigations.Availability and implementationThe codes and the documentation are available at: https://github.com/jkonglab/S4_RPE.
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Project description:Pigment epithelial detachment (PED) is common in eyes with central serous chorioretinopathy (CSC), and choroidal neovascularisation (CNV), which is almost always associated with PED, is found in a higher proportion than previously expected. Using en-face optical coherence tomography, this retrospective study aimed to investigate the PED location in relation to various geometric landmarks including the foveal centre (FC), greatest choroidal thickness (GCT) point and optic disc centre. In a total of 98 eyes, the distance from the FC to PED centroid was correlated with the ratio of GCT to subfoveal choroidal thickness (r = 0.278, P = 0.006) and the distance from the FC to GCT point (r = 0.371, P < 0.001). Eyes with CNV had a shorter distance between the PED centroid and FC (700 ± 439 μm) than those without (1191 ± 964 μm, P = 0.001). Analysis of covariance showed that the distance from the FC to the PED centroid was significantly correlated with the distance from the FC to the GCT point (P = 0.009) and the PED group with and without CNV (P = 0.020). This result suggests that the development of complicated PED with CNV can be related to both choroidal vascular abnormalities and retinal pigment epithelial insufficiency.
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Project description:PurposeTo investigate the thickness of retinal layers and association with final visual acuity using spectral-domain optical coherence tomography (SD-OCT) in macular area of macula-off rhegmatogenous retinal detachment (RRD) patients after a successful macular re-attachment.MethodsIn retrospective study, a total 24 eyes with macula-off RRD were enrolled. All patients underwent vitrectomy to repair RRD. Outer plexiform layer (OPL), outer nuclear layer (ONL), photoreceptor layer (PR), retinal pigment epithelium (RPE) thicknesses were measured by the Spectralis (Heidelberg Engineering, Heidelberg, Germany) SD-OCT with automated segmentation software. The relationship between the thicknesses of each retinal layer and postoperative logarithm of the minimum angle of resolution scale (LogMAR) visual acuity was analyzed.ResultsOPL and RPE thicknesses were not significantly different between the retinal detachment eyes and fellow eyes (P = 0.839, 0.999, respectively). The ONL and photoreceptor thickness were significantly thinner in the retinal detachment eyes (P <0.001 and 0.001, respectively). In the univariate regression analysis, preoperative best corrected visual acuity (BCVA), ONL thickness and photoreceptor thickness showed association with the postoperative BCVA (P = 0.003, <0.001 and 0.024, respectively). In final multiple linear regression model, ONL thickness was the only variable significantly associated with postoperative BCVA (P = 0.044).ConclusionsSegmented ONL and photoreceptor thickness of retinal detachment eyes were significantly thinner than fellow eyes. Segmental analysis of the retinal layer in macular region may provide valuable information for evaluation RRD. And ONL thickness can be used as a potential biomarker to predict visual outcome after RRD repair.
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