Feasibility of level-set analysis of enface OCT retinal images in diabetic retinopathy

Association between Visual Acuity and Retinal Layer Metrics in Diabetics with and without Macular Edema

Purpose

Diabetes is known to cause alterations in retinal microvasculature and tissue that progressively lead to visual impairment. Optical coherence tomography (OCT) is useful for assessment of total retinal thickening due to diabetic macular edema (DME). In the current study, we determined associations between visual acuity (VA) and retinal layer thickness, reflectance, and interface disruption derived from enface OCT images in subjects with and without DME.

Materials and Methods

Best corrected VA was measured and high-density OCT volume scans were acquired in 149 diabetic subjects. A previously established image segmentation method identified retinal layer interfaces and locations of visually indiscernible (disrupted) interfaces. Enface thickness maps and reflectance images of the nerve fiber layer (NFL), combined ganglion cell and inner plexiform layer (GCLIPL), inner nuclear layer (INL), outer plexiform layer (OPL), outer nuclear layer (ONL), photoreceptor outer segment layer (OSL), and retinal pigment epithelium (RPE) were generated in the central macular subfield. The associations among VA and retinal layer metrics were determined by multivariate linear regressions after adjusting for covariates (age, sex, race, HbA1c, diabetes type, and duration) and correcting for multiple comparisons.

Results

In DME subjects, increased GCLIPL and OPL thickness and decreased OSL thickness were associated with reduced VA. Furthermore, increased NFL reflectance and decreased OSL reflectance were associated with reduced VA. Additionally, increased areas of INL and ONL interface disruptions were associated with reduced VA. In subjects without DME, increased INL thickness was associated with reduced VA, whereas in subjects without DME but with previous antivascular endothelium growth factor treatment, thickening of OPL was associated with reduced VA.

Conclusions

Alterations in retinal layer thickness and reflectance metrics derived from enface OCT images were associated with reduced VA with and without presence of DME, suggestive of their potential for monitoring development, progression, and treatment of DME.

Assessment of Global and Local Alterations in Retinal Layer Thickness in Ins2 (Akita) Diabetic Mice by Spectral Domain Optical Coherence Tomography

PURPOSE/AIM

The Ins2 (Akita) mouse is a spontaneous diabetic mouse model with a heterozygous mutation in the insulin 2 gene that results in sustained hyperglycemia. The purpose of the study was to assess global and local retinal layer thickness alterations in Akita mice by analysis of spectral domain optical coherence tomography (SD-OCT) images.

MATERIALS AND METHODS

SD-OCT imaging was performed in Akita and wild-type mice at 12 and 24 weeks of age. Inner retinal thickness (IRT), outer retinal thickness (ORT), total retinal thickness (TRT), and photoreceptor outer segment length (OSL) were measured. Mean global thickness values were compared between Akita and wild-type mice. Local thickness variations in Akita mice were assessed based on normative values in wild-type mice.

RESULTS

Akita mice had higher blood glucose levels and lower body weights (p < 0.001). On average, IRT, ORT, and TRT were approximately 2% lower in Akita mice than in wild-type mice (p ≤ 0.02). In Akita mice, the percent difference between retinal areas with thickness below and above normative values for IRT, ORT, and TRT was 22%, 32%, and 38%, respectively.

CONCLUSIONS

These findings support the use of the Akita mouse model to study the retinal neurodegenerative effects of hyperglycemia.

Alterations in Retinal Layer Thickness and Reflectance at Different Stages of Diabetic Retinopathy by En Face Optical Coherence Tomography

Purpose

This article reports a method for en face optical coherence tomography (OCT) imaging and quantitative assessment of alterations in both thickness and reflectance of individual retinal layers at different stages of diabetic retinopathy (DR).

Methods

High-density OCT raster volume scans were acquired in 29 diabetic subjects divided into no DR (NDR) or non-proliferative DR (NPDR) groups and 22 control subjects (CNTL). A customized image segmentation method identified eight retinal layer interfaces and generated en face thickness maps and reflectance images for nerve fiber layer (NFL), ganglion cell and inner plexiform layers (GCLIPL), inner nuclear layer (INL), outer plexiform layer (OPL), outer nuclear layer (ONL), photoreceptor outer segment layer (OSL), and retinal pigment epithelium (RPE). Mean thickness and intensity values were calculated in nine macular subfields for each retinal layer.

Results

En face thickness maps and reflectance images of retinal layers in CNTL subjects corresponded to normal retinal anatomy. Total retinal thickness correlated negatively with age in nasal subfields (R ≤−0.31; P ≤ 0.03, N = 51). In NDR subjects, NFL and OPL thickness were decreased (P = 0.05), and ONL thickness was increased (P = 0.04) compared to CNTL. In NPDR subjects, GCLIPL thickness was increased in perifoveal subfields (P< 0.05) and INL intensity was higher in all macular subfields (P = 0.04) compared to CNTL.

Conclusions

Depth and spatially resolved retinal thickness and reflectance measurements are potential biomarkers for assessment and monitoring of DR.

Automatic Subretinal Fluid Segmentation of Retinal SD-OCT Images With Neurosensory Retinal Detachment Guided by Enface Fundus Imaging

OBJECTIVE

Accurate segmentation of neurosensory retinal detachment (NRD) associated subretinal fluid in spectral domain optical coherence tomography (SD-OCT) is vital for the assessment of central serous chorioretinopathy (CSC). A novel two-stage segmentation algorithm was proposed, guided by Enface fundus imaging.

METHODS

In the first stage, Enface fundus image was segmented using thickness map prior to detecting the fluid-associated abnormalities with diffuse boundaries. In the second stage, the locations of the abnormalities were used to restrict the spatial extent of the fluid region, and a fuzzy level set method with a spatial smoothness constraint was applied to subretinal fluid segmentation in the SD-OCT scans.

RESULTS

Experimental results from 31 retinal SD-OCT volumes with CSC demonstrate that our method can achieve a true positive volume fraction (TPVF), false positive volume fraction (FPVF), and positive predicative value (PPV) of 94.3%, 0.97%, and 93.6%, respectively, for NRD regions. Our approach can also discriminate NRD-associated subretinal fluid from subretinal pigment epithelium fluid associated with pigment epithelial detachment with a TPVF, FPVF, and PPV of 93.8%, 0.40%, and 90.5%, respectively.

CONCLUSION

We report a fully automatic method for the segmentation of subretinal fluid.

SIGNIFICANCE

Our method shows the potential to improve clinical therapy for CSC.

Implicit kernel sparse shape representation: a sparse-neighbors-based objection segmentation framework

This paper introduces a new implicit-kernel-sparse-shape-representation-based object segmentation framework. Given an input object whose shape is similar to some of the elements in the training set, the proposed model can automatically find a cluster of implicit kernel sparse neighbors to approximately represent the input shape and guide the segmentation. A distance-constrained probabilistic definition together with a dualization energy term is developed to connect high-level shape representation and low-level image information. We theoretically prove that our model not only derives from two projected convex sets but is also equivalent to a sparse-reconstruction-error-based representation in the Hilbert space. Finally, a "wake-sleep"-based segmentation framework is applied to drive the evolutionary curve to recover the original shape of the object. We test our model on two public datasets. Numerical experiments on both synthetic images and real applications show the superior capabilities of the proposed framework.

Automated Internal Classification of Beadless Chinese ZhuJi Freshwater Pearls based on Optical Coherence Tomography Images

Optical coherence tomography (OCT) has been applied to inspect the internal defect of beadless Chinese ZhuJi fleshwater pearls. A novel fully automated algorithm is proposed to classify between normal and defective sub-layer in nacre layer. Our algorithm utilizes the graph segmentation approach to estimate the up and down boundaries of defect sub-layers from flattened and cropped image, and also proposes the strategy for edge and weight construction in segmentation process. The vertical gradients of boundary pixels are used to make grading decision. The algorithm is tested by typical pearl samples, and achieves 100% classification accuracy. The experiment result shows the feasibility and adaptability of the proposed approach, and proves that the OCT technique combined with proposed algorithm is a potential tool for fast and non-destructive diagnosis of internal structure of beadless pearl.

Automated volumetric segmentation of retinal fluid on optical coherence tomography

We propose a novel automated volumetric segmentation method to detect and quantify retinal fluid on optical coherence tomography (OCT). The fuzzy level set method was introduced for identifying the boundaries of fluid filled regions on B-scans (x and y-axes) and C-scans (z-axis). The boundaries identified from three types of scans were combined to generate a comprehensive volumetric segmentation of retinal fluid. Then, artefactual fluid regions were removed using morphological characteristics and by identifying vascular shadowing with OCT angiography obtained from the same scan. The accuracy of retinal fluid detection and quantification was evaluated on 10 eyes with diabetic macular edema. Automated segmentation had good agreement with manual segmentation qualitatively and quantitatively. The fluid map can be integrated with OCT angiogram for intuitive clinical evaluation.

Enface Thickness Mapping and Reflectance Imaging of Retinal Layers in Diabetic Retinopathy

Purpose

To present a method for image segmentation and generation of enface thickness maps and reflectance images of retinal layers in healthy and diabetic retinopathy (DR) subjects.

Methods

High density spectral domain optical coherence tomography (SDOCT) images were acquired in 10 healthy and 4 DR subjects. Customized image analysis software identified 5 retinal cell layer interfaces and generated thickness maps and reflectance images of the total retina (TR), inner retina (IR), outer retina (OR), and the inner segment ellipsoid (ISe) band. Thickness maps in DR subjects were compared to those of healthy subjects by generating deviation maps which displayed retinal locations with thickness below, within, and above the normal 95% confidence interval.

Results

In healthy subjects, TR and IR thickness maps displayed the foveal depression and increased thickness in the parafoveal region. OR and ISe thickness maps showed increased thickness at the fovea, consistent with normal retinal anatomy. In DR subjects, thickening and thinning in localized regions were demonstrated on TR, IR, OR, and ISe thickness maps, corresponding to retinal edema and atrophy, respectively. TR and OR reflectance images showed reduced reflectivity in regions of increased thickness. Hard exudates appeared as hyper-reflective spots in IR reflectance images and casted shadows on the deeper OR and ISe reflectance images. The ISe reflectance image clearly showed the presence of focal laser scars.

Conclusions

Enface thickness mapping and reflectance imaging of retinal layers is a potentially useful method for quantifying the spatial and axial extent of pathologies due to DR.

[Optical coherence tomography for differentiation of parathyroid gland tissue]

BACKGROUND

Optical coherence tomography (OCT) is a high-resolution imaging technique that allows the identification of microarchitectural features in real-time.

OBJECTIVE

Can OCT be used to differentiate parathyroid tissue from other cervical tissue entities?

MATERIAL AND METHODS

All investigations were carried out during cervical operations. Initially, ex vivo images were analyzed to define morphological imaging criteria for each tissue entity. These criteria were used to evaluate a first series of ex vivo images. In a second phase the practicability of the technique was investigated in vivo and in the third phase backscattering intensity measurements were analyzed employing linear discriminant analysis (LDA).

RESULTS

In the ex vivo series parathyroid tissue could be differentiated from other tissue entities with a sensitivity and specificity of 84  % and 94  %, respectively. Parathyroid tissue was correctly identified in the in vivo series in only 69.2 %. The analysis of backscattering intensity profiles employing LDA reliably distinguished between the different tissue types.

CONCLUSION

The OCT images displayed typical characteristics for each tissue entity. Due to technical problems in handling the probe the in vivo OCT images were of much poorer quality. Backscattering intensity measurements illustrated that OCT images provide an individual profile for each tissue entity independent of the defined morphological assessment criteria. The results show that OCT is fundamentally suitable for intraoperative differentiation of tissues.