Corneal layer thickness in keratoconus using optical coherence tomography.

CLINICAL RELEVANCE: Accurate thickness measurement of corneal layers using anterior segment OCT can be used to improve visual outcomes. Understanding its applications is essential for optometric practices to enhance eye care procedures. BACKGROUND: To evaluate the thicknesses of different corneal layers for identifying keratoconus (KCN) and subclinical keratoconus (SKCN) using spectral-domain optical coherence tomography (SD-OCT). METHODS: This prospective study analyzed 60 eyes with KCN, 48 eyes with SKCN, and 53 normal eyes. The central corneal thickness (CCT) and thicknesses of the epithelium, Bowman, stroma, and Descemet-endothelium layers were measured using SD-OCT. One way analysis of variance and the area under the curve (AUC) were used to evaluate the parameters. The Delong method was used to compare AUCs. RESULTS: In KCN, CCT and thicknesses of epithelium, Bowman, stroma, and Descemet-endothelium layers were 495.5 ± 41.7, 52.6 ± 6.4,11.5 ± 1.4, 415.5 ± 38.9, and 12.3 ± 1.7 µm, respectively. These thickness values were respectively 524.5 ± 33.3, 56.8 ± 6.8, 11.5 ± 1.6, 439.8 ± 30.6, and 12.4 ± 1.7 µm in SKCN and 563.8 ± 37.9, 57.7 ± 6.9, 12.2 ± 1.6, 469.5 ± 33.7, and 12.8 ± 2.1µm in normal group. Total cornea and stroma in KCN and SKCN, and epithelium in KCN were significantly thinner compared to the normal group (P < 0.001). The highest AUC values were observed for CCT in KCN (AUC 0.90) and SKCN (AUC 0.782). The diagnostic accuracy was significantly higher for stromal thickness in KCN (sensitivity 81.7%, specificity 73.6%, AUC 0.871) and SKCN (sensitivity 80.0%, specificity 56.6%, AUC 0.751) than other individual corneal layers (Delong, P < 0.001) . CONCLUSION: CCT can accurately distinguish keratoconus from normal eyes. However, central corneal stromal thinning was the most sensitive diagnostic index for early detection of SKCN. Developing standardized stromal maps may be helpful for detecting SKCN.

Multi-scale convolutional neural network for automated AMD classification using retinal OCT images.

BACKGROUND AND OBJECTIVE: Age-related macular degeneration (AMD) is the most common cause of blindness in developed countries, especially in people over 60 years of age. The workload of specialists and the healthcare system in this field has increased in recent years mainly due to three reasons: 1) increased use of retinal optical coherence tomography (OCT) imaging technique, 2) prevalence of population aging worldwide, and 3) chronic nature of AMD. Recent advancements in the field of deep learning have provided a unique opportunity for the development of fully automated diagnosis frameworks. Considering the presence of AMD-related retinal pathologies in varying sizes in OCT images, our objective was to propose a multi-scale convolutional neural network (CNN) that can capture inter-scale variations and improve performance using a feature fusion strategy across convolutional blocks. METHODS: Our proposed method introduces a multi-scale CNN based on the feature pyramid network (FPN) structure. This method is used for the reliable diagnosis of normal and two common clinical characteristics of dry and wet AMD, namely drusen and choroidal neovascularization (CNV). The proposed method is evaluated on the national dataset gathered at Hospital (NEH) for this study, consisting of 12649 retinal OCT images from 441 patients, and the UCSD public dataset, consisting of 108312 OCT images from 4686 patients. RESULTS: Experimental results show the superior performance of our proposed multi-scale structure over several well-known OCT classification frameworks. This feature combination strategy has proved to be effective on all tested backbone models, with improvements ranging from 0.4% to 3.3%. In addition, gradual learning has proved to be effective in improving performance in two consecutive stages. In the first stage, the performance was boosted from 87.2%±2.5% to 92.0%±1.6% using pre-trained ImageNet weights. In the second stage, another performance boost from 92.0%±1.6% to 93.4%±1.4% was observed as a result of fine-tuning the previous model on the UCSD dataset. Lastly, generating heatmaps provided additional proof for the effectiveness of our multi-scale structure, enabling the detection of retinal pathologies appearing in different sizes. CONCLUSION: The promising quantitative results of the proposed architecture, along with qualitative evaluations through generating heatmaps, prove the suitability of the proposed method to be used as a screening tool in healthcare centers assisting ophthalmologists in making better diagnostic decisions.

Topical Interferon Alpha 2b in the Treatment of Refractory Diabetic Macular Edema.

PURPOSE: To report the efficacy of topical interferon alpha 2b in the treatment of refractory diabetic macular edema. METHODS: In this retrospective interventional case series, five eyes of three individuals with diabetic macular edema resistant to multiple intravitreal injections of anti-vascular endothelial growth factor drugs and macular photocoagulation were included. RESULTS: All studied eyes had undergone multiple intravitreal injections including bevacizumab, combination of bevacizumab and triamcinolone and aflibercept, and macular laser photocoagulation before being included in this study. Two intravitreal ranibizumab injections had also been performed in both eyes of one patient. Two eyes had undergone pars plana vitrectomy, one for diabetic macular edema and the other for rhegmatogenous retinal detachment. After a discussion regarding the experimental topical interferon alpha 2b treatment, all patients agreed to start interferon alpha 2b drops four times a day. One month after the treatment, optical coherence tomography demonstrated a significant improvement in macular structure and thickness which was stable or improved at the three-month follow-up visit. Visual acuity in all eyes was stable or improved throughout the three-month follow-up period. Conjunctival injection and follicular conjunctivitis were the side effects of topical interferon alpha 2b and were treated with lubrication and steroids. CONCLUSION: This case series demonstrated the potential efficacy of interferon alpha 2b in the treatment of refractory diabetic macular edema. It might be an option in patients with contraindications for intravitreal injections.

Three-dimensional curvelet-based dictionary learning for speckle noise removal of optical coherence tomography.

Optical coherence tomography (OCT) is a recently emerging non-invasive diagnostic tool useful in several medical applications such as ophthalmology, cardiology, gastroenterology and dermatology. One of the major problems with OCT pertains to its low contrast due to the presence of multiplicative speckle noise, which limits the signal-to-noise ratio (SNR) and obscures low-intensity and small features. In this paper, we recommend a new method using the 3D curvelet based K-times singular value decomposition (K-SVD) algorithm for speckle noise reduction and contrast enhancement of the intra-retinal layers of 3D Spectral-Domain OCT (3D-SDOCT) images. In order to benefit from the near-optimum properties of curvelet transform (such as good directional selectivity) on top of dictionary learning, we propose a new plan in dictionary learning by using the curvelet atoms as the initial dictionary. For this reason, the curvelet transform of the noisy image is taken and then the noisy coefficients matrix in each scale, rotation and spatial coordinates is passed through the K-SVD denoising algorithm with predefined 3D initial dictionary that is adaptively selected from thresholded coefficients in the same subband of the image. During the denoising of curvelet coefficients, we can also modify them for the purpose of contrast enhancement of intra-retinal layers. We demonstrate the ability of our proposed algorithm in the speckle noise reduction of 17 publicly available 3D OCT data sets, each of which contains 100 B-scans of size 512×1000 with and without neovascular age-related macular degeneration (AMD) images acquired using SDOCT, Bioptigen imaging systems. Experimental results show that an improvement from 1.27 to 7.81 in contrast to noise ratio (CNR), and from 38.09 to 1983.07 in equivalent number of looks (ENL) is achieved, which would outperform existing state-of-the-art OCT despeckling methods.

Convolutional Mixture of Experts Model: A Comparative Study on Automatic Macular Diagnosis in Retinal Optical Coherence Tomography Imaging.

BACKGROUND: Macular disorders, such as diabetic macular edema (DME) and age-related macular degeneration (AMD) are among the major ocular diseases. Having one of these diseases can lead to vision impairments or even permanent blindness in a not-so-long time span. So, the early diagnosis of these diseases are the main goals for researchers in the field. METHODS: This study is designed in order to present a comparative analysis on the recent convolutional mixture of experts (CMoE) models for distinguishing normal macular OCT from DME and AMD. For this purpose, we considered three recent CMoE models called Mixture ensemble of convolutional neural networks (ME-CNN), Multi-scale Convolutional Mixture of Experts (MCME), and Wavelet-based Convolutional Mixture of Experts (WCME) models. For this research study, the models were evaluated on a database of three different macular OCT sets. Two first OCT sets were acquired by Heidelberg imaging systems consisting of 148 and 45 subjects respectively and set3 was constituted of 384 Bioptigen OCT acquisitions. To provide better performance insight into the CMoE ensembles, we extensively analyzed the models based on the 5-fold cross-validation method and various classification measures such as precision and average area under the ROC curve (AUC). RESULTS: Experimental evaluations showed that the MCME and WCME outperformed the ME-CNN model and presented overall precisions of 98.14% and 96.06% for aligned OCTs respectively. For non-aligned retinal OCTs, these values were 93.95% and 95.56%. CONCLUSION: Based on the comparative analysis, although the MCME model outperformed the other CMoE models in the analysis of aligned retinal OCTs, the WCME offers a robust model for diagnosis of non-aligned retinal OCTs. This allows having a fast and robust computer-aided system in macular OCT imaging which does not rely on the routine computerized processes such as denoising, segmentation of retinal layers, and also retinal layers alignment.

Multipoint assessment of demarcation line depth after standard and accelerated cross-linking in central and inferior keratoconus.

PURPOSE: To determine the changes in the depth of the demarcation line in the central to peripheral cornea following accelerated compared to standard corneal cross-linking (CXL). METHODS: In this prospective, non-randomized study, 60 eyes with progressive keratoconus underwent accelerated or standard CXL (30 in each group). Anterior segment optical coherence tomography (AS-OCT) was done one month later by two independent masked examiners to measure the depth of the demarcation line in the central cornea and on peripheral rings. RESULTS: The inter-examiner agreement (intra-class correlation coefficient) was >0.75 for all measured points, and average measurements were used in the analysis. The depth of the visualized demarcation line in the center was 223.4 ± 67.4 µm and 354.9 ± 79.0 µm in the accelerated and standard groups, respectively (P < 0.001). The depth significantly decreased from the center to the 7 mm ring in both groups (all P < 0.05). This change was 7.7-26.1% and 2.2%-11.1% in the accelerated and standard groups, respectively. In the accelerated group, the demarcation line was deeper in the central cone sub-group compared to the inferior cone sub-group, but in the standard group, the demarcation line was deeper in the inferior cone sub-group (all P < 0.05). Cases with an inferior cone showed greater inter-group differences in all studied points. CONCLUSIONS: The depth of the demarcation line with accelerated CXL is less than the standard protocol and decreases from the center towards the periphery. Demarcation lines are more homogenized with standard CXL. In cases with an inferior cone, demarcation line depth varies throughout the cornea.

Macular OCT Classification Using a Multi-Scale Convolutional Neural Network Ensemble.

Computer-aided diagnosis (CAD) of retinal pathologies is a current active area in medical image analysis. Due to the increasing use of retinal optical coherence tomography (OCT) imaging technique, a CAD system in retinal OCT is essential to assist ophthalmologist in the early detection of ocular diseases and treatment monitoring. This paper presents a novel CAD system based on a multi-scale convolutional mixture of expert (MCME) ensemble model to identify normal retina, and two common types of macular pathologies, namely, dry age-related macular degeneration, and diabetic macular edema. The proposed MCME modular model is a data-driven neural structure, which employs a new cost function for discriminative and fast learning of image features by applying convolutional neural networks on multiple-scale sub-images. MCME maximizes the likelihood function of the training data set and ground truth by considering a mixture model, which tries also to model the joint interaction between individual experts by using a correlated multivariate component for each expert module instead of only modeling the marginal distributions by independent Gaussian components. Two different macular OCT data sets from Heidelberg devices were considered for the evaluation of the method, i.e., a local data set of OCT images of 148 subjects and a public data set of 45 OCT acquisitions. For comparison purpose, we performed a wide range of classification measures to compare the results with the best configurations of the MCME method. With the MCME model of four scale-dependent experts, the precision rate of 98.86%, and the area under the receiver operating characteristic curve (AUC) of 0.9985 were obtained on average.

Automatic diagnosis of abnormal macula in retinal optical coherence tomography images using wavelet-based convolutional neural network features and random forests classifier.

The present research intends to propose a fully automatic algorithm for the classification of three-dimensional (3-D) optical coherence tomography (OCT) scans of patients suffering from abnormal macula from normal candidates. The method proposed does not require any denoising, segmentation, retinal alignment processes to assess the intraretinal layers, as well as abnormalities or lesion structures. To classify abnormal cases from the control group, a two-stage scheme was utilized, which consists of automatic subsystems for adaptive feature learning and diagnostic scoring. In the first stage, a wavelet-based convolutional neural network (CNN) model was introduced and exploited to generate B-scan representative CNN codes in the spatial-frequency domain, and the cumulative features of 3-D volumes were extracted. In the second stage, the presence of abnormalities in 3-D OCTs was scored over the extracted features. Two different retinal SD-OCT datasets are used for evaluation of the algorithm based on the unbiased fivefold cross-validation (CV) approach. The first set constitutes 3-D OCT images of 30 normal subjects and 30 diabetic macular edema (DME) patients captured from the Topcon device. The second publicly available set consists of 45 subjects with a distribution of 15 patients in age-related macular degeneration, DME, and normal classes from the Heidelberg device. With the application of the algorithm on overall OCT volumes and 10 repetitions of the fivefold CV, the proposed scheme obtained an average precision of 99.33% on dataset1 as a two-class classification problem and 98.67% on dataset2 as a three-class classification task.

Speckle Noise Reduction in Optical Coherence Tomography Using Two-dimensional Curvelet-based Dictionary Learning.

The process of interpretation of high-speed optical coherence tomography (OCT) images is restricted due to the large speckle noise. To address this problem, this paper proposes a new method using two-dimensional (2D) curvelet-based K-SVD algorithm for speckle noise reduction and contrast enhancement of intra-retinal layers of 2D spectral-domain OCT images. For this purpose, we take curvelet transform of the noisy image. In the next step, noisy sub-bands of different scales and rotations are separately thresholded with an adaptive data-driven thresholding method, then, each thresholded sub-band is denoised based on K-SVD dictionary learning with a variable size initial dictionary dependent on the size of curvelet coefficients' matrix in each sub-band. We also modify each coefficient matrix to enhance intra-retinal layers, with noise suppression at the same time. We demonstrate the ability of the proposed algorithm in speckle noise reduction of 100 publically available OCT B-scans with and without non-neovascular age-related macular degeneration (AMD), and improvement of contrast-to-noise ratio from 1.27 to 5.12 and mean-to-standard deviation ratio from 3.20 to 14.41 are obtained.

Three-dimensional Segmentation of Retinal Cysts from Spectral-domain Optical Coherence Tomography Images by the Use of Three-dimensional Curvelet Based K-SVD.

This paper presents a new three-dimensional curvelet transform based dictionary learning for automatic segmentation of intraretinal cysts, most relevant prognostic biomarker in neovascular age-related macular degeneration, from 3D spectral-domain optical coherence tomography (SD-OCT) images. In particular, we focus on the Spectralis SD-OCT (Heidelberg Engineering, Heidelberg, Germany) system, and show the applicability of our algorithm in the segmentation of these features. For this purpose, we use recursive Gaussian filter and approximate the corrupted pixels from its surrounding, then in order to enhance the cystoid dark space regions and future noise suppression we introduce a new scheme in dictionary learning and take curvelet transform of filtered image then denoise and modify each noisy coefficients matrix in each scale with predefined initial 3D sparse dictionary. Dark pixels between retinal pigment epithelium and nerve fiber layer that were extracted with graph theory are considered as cystoid spaces. The average dice coefficient for the segmentation of cystoid regions in whole 3D volume and with-in central 3 mm diameter on the MICCAI 2015 OPTIMA Cyst Segmentation Challenge dataset were found to be 0.65 and 0.77, respectively.

Obtaining Thickness Maps of Corneal Layers Using the Optimal Algorithm for Intracorneal Layer Segmentation.

Optical Coherence Tomography (OCT) is one of the most informative methodologies in ophthalmology and provides cross sectional images from anterior and posterior segments of the eye. Corneal diseases can be diagnosed by these images and corneal thickness maps can also assist in the treatment and diagnosis. The need for automatic segmentation of cross sectional images is inevitable since manual segmentation is time consuming and imprecise. In this paper, segmentation methods such as Gaussian Mixture Model (GMM), Graph Cut, and Level Set are used for automatic segmentation of three clinically important corneal layer boundaries on OCT images. Using the segmentation of the boundaries in three-dimensional corneal data, we obtained thickness maps of the layers which are created by these borders. Mean and standard deviation of the thickness values for normal subjects in epithelial, stromal, and whole cornea are calculated in central, superior, inferior, nasal, and temporal zones (centered on the center of pupil). To evaluate our approach, the automatic boundary results are compared with the boundaries segmented manually by two corneal specialists. The quantitative results show that GMM method segments the desired boundaries with the best accuracy.

Impact of whole exome sequencing among Iranian patients with autosomal recessive retinitis pigmentosa.

BACKGROUND: Non-syndromic autosomal recessive Retinitis Pigmentosa (arRP) is a highly heterogeneous genetic visual disorder with a large number of causative genes. We aimed to determine the power of Whole Exome Sequencing (WES) in the identification of the genes responsible for non-syndromic arRP among Iranian patients. METHODS: We used WES, followed by the Sanger sequencing to identify the underlying gene mutations causing non-syndromic arRP. RESULTS: Our study revealed disease-causing mutations in known arRP genes for 10 of the 13 families studied (76.9%). These mutations included two-frameshift insertion/deletion in CRB1 and ABCA4, one splicing mutation in PDE6B, four missense mutations in RP1, CRB1, PANK2 and IFT140, as well as three stop codon mutations in RDH12, PRCD, and C2orf71. Three remaining families harbored no mutation in previously known RP genes. Of the 10 diseases causing mutations identified among the investigated Iranian patients with non-syndromic arRP, eight variants had not been reported previously. We confirmed segregation of all 10 mutations with disease phenotypes in our studied population. CONCLUSION: This study supports the genetic heterogeneity of non-syndromic arRP in Iranian patients, and provides an opportunity to show the effectiveness of WES in the identification of pathogenic mutations among patients with non-syndromic arRP born to consanguineous parents.

Thickness mapping of eleven retinal layers segmented using the diffusion maps method in normal eyes.

This study was conducted to determine the thickness map of eleven retinal layers in normal subjects by spectral domain optical coherence tomography (SD-OCT) and evaluate their association with sex and age. Mean regional retinal thickness of 11 retinal layers was obtained by automatic three-dimensional diffusion map based method in 112 normal eyes of 76 Iranian subjects. We applied our previously reported 3D intraretinal fast layer segmentation which does not require edge-based image information but rather relies on regional image texture. The thickness maps are compared among 9 macular sectors within 3 concentric circles as defined by ETDRS. The thickness map of central foveal area in layers 1, 3, and 4 displayed the minimum thickness. Maximum thickness was observed in nasal to the fovea of layer 1 and in a circular pattern in the parafoveal retinal area of layers 2, 3, and 4 and in central foveal area of layer 6. Temporal and inferior quadrants of the total retinal thickness and most of other quadrants of layer 1 were significantly greater in the men than in the women. Surrounding eight sectors of total retinal thickness and a limited number of sectors in layers 1 and 4 significantly correlated with age.

An Automatic Algorithm for Segmentation of the Boundaries of Corneal Layers in Optical Coherence Tomography Images using Gaussian Mixture Model.

Diagnosis of corneal diseases is possible by measuring and evaluation of corneal thickness in different layers. Thus, the need for precise segmentation of corneal layer boundaries is inevitable. Obviously, manual segmentation is time-consuming and imprecise. In this paper, the Gaussian mixture model (GMM) is used for automatic segmentation of three clinically important corneal boundaries on optical coherence tomography (OCT) images. For this purpose, we apply the GMM method in two consequent steps. In the first step, the GMM is applied on the original image to localize the first and the last boundaries. In the next step, gradient response of a contrast enhanced version of the image is fed into another GMM algorithm to obtain a more clear result around the second boundary. Finally, the first boundary is traced toward down to localize the exact location of the second boundary. We tested the performance of the algorithm on images taken from a Heidelberg OCT imaging system. To evaluate our approach, the automatic boundary results are compared with the boundaries that have been segmented manually by two corneal specialists. The quantitative results show that the proposed method segments the desired boundaries with a great accuracy. Unsigned mean errors between the results of the proposed method and the manual segmentation are 0.332, 0.421, and 0.795 for detection of epithelium, Bowman, and endothelium boundaries, respectively. Unsigned mean errors of the inter-observer between two corneal specialists have also a comparable unsigned value of 0.330, 0.398, and 0.534, respectively.

Agreement of two different spectral domain optical coherence tomography instruments for retinal nerve fiber layer measurements.

PURPOSE: To determine the agreement between Spectralis and Cirrus spectral domain optical coherence tomography (SD-OCT) measurements of peripapillary retinal nerve fiber layer (RNFL) thickness. METHODS: Suspected or confirmed cases of glaucoma who met the inclusion criteria underwent peripapillary RNFL thickness measurement using both the Spectralis and Cirrus on the same day within a few minutes. RESULTS: Measurements were performed on 103 eyes of 103 patients with mean age of 50.4±17.7 years. Mean RNFL thickness was 89.22±15.87 versus 84.54±13.68 µm using Spectralis and Cirrus, respectively. The difference between measurements and the average of paired measurements with the two devices showed a significant linear relationship. Bland-Altman plots demonstrated that Spectralis thickness values were systematically larger than that of Cirrus. CONCLUSION: Spectralis OCT generates higher peripapillary RNFL thickness readings as compared to Cirrus OCT; this should be kept in mind when values obtained with different instruments are compared during follow-up.

Segmentation of choroidal boundary in enhanced depth imaging OCTs using a multiresolution texture based modeling in graph cuts.

The introduction of enhanced depth imaging optical coherence tomography (EDI-OCT) has provided the advantage of in vivo cross-sectional imaging of the choroid, similar to the retina, with standard commercially available spectral domain (SD) OCT machines. A texture-based algorithm is introduced in this paper for fully automatic segmentation of choroidal images obtained from an EDI system of Heidelberg 3D OCT Spectralis. Dynamic programming is utilized to determine the location of the retinal pigment epithelium (RPE). Bruch's membrane (BM) (the blood-retina barrier which separates the RPE cells of the retina from the choroid) can be segmented by searching for the pixels with the biggest gradient value below the RPE. Furthermore, a novel method is proposed to segment the choroid-sclera interface (CSI), which employs the wavelet based features to construct a Gaussian mixture model (GMM). The model is then used in a graph cut for segmentation of the choroidal boundary. The proposed algorithm is tested on 100 EDI OCTs and is compared with manual segmentation. The results showed an unsigned error of 2.48 ± 0.32 pixels for BM extraction and 9.79 ± 3.29 pixels for choroid detection. It implies significant improvement of the proposed method over other approaches like k-means and graph cut methods.

An accurate multimodal 3-D vessel segmentation method based on brightness variations on OCT layers and curvelet domain fundus image analysis.

This paper proposes a multimodal approach for vessel segmentation of macular optical coherence tomography (OCT) slices along with the fundus image. The method is comprised of two separate stages; the first step is 2-D segmentation of blood vessels in curvelet domain, enhanced by taking advantage of vessel information in crossing OCT slices (named feedback procedure), and improved by suppressing the false positives around the optic nerve head. The proposed method for vessel localization of OCT slices is also enhanced utilizing the fact that retinal nerve fiber layer becomes thicker in the presence of the blood vessels. The second stage of this method is axial localization of the vessels in OCT slices and 3-D reconstruction of the blood vessels. Twenty-four macular spectral 3-D OCT scans of 16 normal subjects were acquired using a Heidelberg HRA OCT scanner. Each dataset consisted of a scanning laser ophthalmoscopy (SLO) image and limited number of OCT scans with size of 496 × 512 (namely, for a data with 19 selected OCT slices, the whole data size was 496 × 512 × 19). The method is developed with least complicated algorithms and the results show considerable improvement in accuracy of vessel segmentation over similar methods to produce a local accuracy of 0.9632 in area of SLO, covered with OCT slices, and the overall accuracy of 0.9467 in the whole SLO image. The results are also demonstrative of a direct relation between the overall accuracy and percentage of SLO coverage by OCT slices.

Posttraumatic endophthalmitis: report No. 2.

PURPOSE: To study predisposing factors of endophthalmitis in patients with open-globe injury. METHODS: All 2,340 patients with open-globe injury admitted to our center over 5 years were studied retrospectively, and patients with endophthalmitis were compared with other patients. RESULTS: The number of endophthalmitis cases among patients with open-globe injury was 117 eyes, accounting for an incidence of 5.1%. The risk of endophthalmitis was significantly higher among male patients with pure corneal injuries (6.8%), intraocular foreign bodies (9.3%), traumatic lens rupture (7.1%), trauma resulting from needles (22.2%), and shorter lacerations. The existence of hyphema or iris prolapse was associated with lower rates of endophthalmitis. The mean gap between injury time and operation time was longer in the endophthalmitis group (2.1 ± 1.8 vs. 1.1 ± 1.3 days). Patients with endophthalmitis had significantly lower ocular trauma scores compared with other patients. CONCLUSION: Posttraumatic endophthalmitis is more likely among patients with open-globe injury that are needle related and among those who have intraocular foreign bodies, traumatic lens rupture, smaller wounds, or wounds exclusively in the cornea. Lower ocular trauma scores in this group of patients with penetrating trauma indicated a greater risk of poor visual prognosis. Therefore, it is suggested that necessary measures and immediate treatment be undertaken in high-risk patients.

Choroidal mapping; a novel approach for evaluating choroidal thickness and volume.

There are a limited number of non-invasive imaging techniques available for assessing the choroid, a structure that may be affected by a variety of retinal disorders or become primarily involved in conditions such as polypoidal choroidal vasculopathy and choroidal tumors. The introduction of enhanced depth imaging optical coherence tomography (EDI-OCT) has provided the advantage of in vivo cross-sectional imaging of the choroid, similar to the retina, with standard commercially available spectral-domain OCT machines. In this article, we review this imaging technique and introduce choroidal mapping as a novel approach for obtaining accurate topographic and volumetric information on the choroid in normal and diseased states.

Optical coherence tomographic findings in highly myopic eyes.

Optical coherence tomography (OCT) has enhanced our understanding of changes in different ocular layers when axial myopia progresses and the globe is stretched. These findings consist of dehiscence of retinal layers known as retinoschisis, paravascular inner retinal cleavage, cysts and lamellar holes, peripapillary intrachoroidal cavitation, tractional internal limiting membrane detachment, macular holes (lamellar and full thickness), posterior retinal detachment, and choroidal neovascular membranes. In this review, recent observations regarding retinal changes in highly myopic eyes explored by OCT are described to highlight structural findings that cannot be diagnosed by simple ophthalmoscopy.