OCTID: Optical coherence tomography image database

High-Definition and 3-dimensional Imaging of Macular Pathologies with High-speed Ultrahigh-Resolution Optical Coherence Tomography

In this paper we propose a technique for obtaining a high resolution (HR) image from a single low resolution (LR) image -using joint learning dictionary - on the basis of image statistic research. It suggests that with an appropriate choice of an over-complete dictionary, image patches can be well represented as a sparse linear combination. Medical imaging for clinical analysis and medical intervention is being used for creating visual representations of the interior of a body, as well as visual representation of the function of some organs or tissues (physiology). A number of medical imaging techniques are in use like MRI, CT scan, X-rays and Optical Coherence Tomography (OCT). OCT is one of the new technologies in medical imaging and one of its uses is in ophthalmology where it is being used for analysis of the choroidal thickness in the eyes in healthy and disease states such as age-related macular degeneration, central serous chorioretinopathy, diabetic retinopathy and inherited retinal dystrophies. We have proposed a technique for enhancing the OCT images which can be used for clearly identifying and analyzing the particular diseases. Our method uses dictionary learning technique for generating a high resolution image from a single input LR image. We train two joint dictionaries, one with OCT images and the second with multiple different natural images, and compare the results with previous SR technique. Proposed method for both dictionaries produces HR images which are comparatively superior in quality with the other proposed method of SR. Proposed technique is very effective for noisy OCT images and produces up-sampled and enhanced OCT images.

To investigate morphological variations in the macular area with optical coherence tomography (OCT) after vitrectomy for diabetic fibrovascular proliferation. We reviewed 108 cases using OCT 7-15 months after vitrectomy. Of these, 32 received OCT within 3 months postoperatively. Morphological variations were categorized and correlated with visual outcome. Only 24 cases (21.4%) had no obvious abnormalities. The most frequent findings were epiretinal membrane (52.8%), macular thickening (37.0%) and macular cysts (28.7%). Multivariate regression showed that diffuse macular thickening, loss of foveal depression and diffuse retinal thinning were significantly associated with poor visual acuity. Sequential OCT (< 3 and >or= 7 months) revealed that epiretinal membrane and oedema outside of fovea changed significantly between two examinations. OCT may identify diverse morphological changes in the macular area after diabetic vitrectomy for fibrovascular proliferation. Macular appearance may change over time, and certain types of morphological changes may be associated with poor visual function.

Accurate segmentation of pigment epithelial detachment (PED) in retinal optical coherence tomography (OCT) images can help doctors comprehensively analyze and diagnose chorioretinal diseases, such as age-related macular degeneration (AMD), central serous chorioretinopathy and polypoidal choroidal vasculopathy. Due to the serious uneven sizes of PED, some traditional algorithms or common deep networks do not perform well in PED segmentation. In this paper, we propose a novel attention multi-scale network (named as AM-Net) based on a U-shape network to segment PED in OCT images. Compared with the original U-Net, there are two main improvements in the proposed method: (1) Designing channel multiscale module (CMM) to replace the skip-connection layer of the U-Net, which uses channel attention mechanism to obtain multi-scale information. (2) Designing spatial multi-scale module (SMM) based on dilated convolution, which is inserted in the decoder path to make the network pay more attention on the multi-scale spatial information. We evaluated the proposed AM-Net on 240 clinically obtained OCT B-scans with 4-fold cross validation. The mean and standard deviation of Intersection over Union (IoU), Dice Similarity Coefficient (DSC), Sensitivity (Sen) and Specificity (Spe) are 72.12± 9.60%, 79.17±8.25%, 93.05±1.72% and 79.93±5.77%, respectively.

To train generative adversarial network (GAN) models to generate predictive optical coherence tomography (OCT) images of central serous chorioretinopathy (CSC) at 3months after observation using multi-modal OCT images. Four hundred forty CSC eyes of 440 patients who underwent Cirrus OCT imaging were included. Baseline OCT B-scan images through the foveal center, en face choroid, and en face ellipsoid zone were collected from each patient. The datasets were divided into training and validation (n = 390) and test (n = 50) sets. The input images for each model comprised either baseline B-scan alone or a combination of en face choroid and ellipsoid zones. Predictive post-treatment OCT B-scan images were generated using GAN models and compared with real 3-month images. Of 50 generated OCT images, there were 48, 47, and 48 acceptable images for UNIT, CycleGAN, and RegGAN, respectively. In comparison with real 3-month images, the generated images showed sensitivity, specificity, and positive predictive values (PPV) for residual fluid in the ranges of 0.762-1.000, 0.483-0.724, and 0.583-0.704; for pigment epithelial detachment (PED) of 0.917-1.000, 0.974-1.000, and 0.917-1.000; and for subretinal hyperreflective material (SHRM) of 0.667-0.778, 0.925-0.950 and 0.700-0.750, respectively. RegGAN exhibited the highest values except for sensitivity. GAN models could generate prognostic OCT images with good performance for prediction of residual fluid, PED, and SHRM presence in CSC. Implementation of the models may help predict disease activity in CSC, facilitating the establishment of a proper treatment plan.

To evaluate how information from combined coronal optical coherence tomography (OCT) and confocal laser scanning ophthalmoscopy (SLO) with integrated simultaneous indocyanine green (ICG) dye angiography can be used in the diagnosis of a variety of macular diseases. A compact chin-rest-based OCT/confocal imaging system was used to produce the OCT image and excite the fluorescence in the ICG dye. The same eye fundus area can be visualized with coronal (C-scans, en face) OCT and ICG angiography simultaneously. Fast T scanning (transverse scanning, en face) was used to build B- or C-scan OCT images along with confocal SLO views, with and without ICG filtration. The OCT, confocal SLO and ICG fluorescence images were simultaneously presented in a three-screen format. A live mixing channel overlaid the ICG sequence on the coronal OCT slices in a fourth panel for immediate comparison. Thirty eyes were imaged. The pathologic conditions studied included classic and occult neovascular membranes, vascularized RPE detachments, polypoidal choroidal vasculopathy, traumatic choroidal rupture, diabetic maculopathy, central serous retinopathy, and macular drusen. Images were evaluated with special attention toward identifying novel relationships between morphology and function revealed by the superimposition of the studies. Simultaneous visualization of an en face (coronal, C-scan) OCT image and of an ICG angiogram, displayed side by side and superimposed, permits more precise correlations between late fluorescence accumulation with structures deep to the retinal surface at the retina-choroid interface. The multiplanar scanning also permits immediate B-scan OCT cross-sectional views of regions of abnormal fluorescence. The paper demonstrates the synergy between the two types of studies, functional and anatomic, in providing a more complete view of the pathologic condition.

Optical Coherence Tomography (OCT) is now a method of choice to asses conditions such as macular holes, diabetic macular edema or central serous chorioretinopathy 1. OCT measurements of retinal thickness have a good reproducibility as demonstrated in repeated examinations. The main application of OCT in inflammatory eye diseases is for the analysis of cystoid macular edema 2;3. Ocular toxoplasmosis is the most frequent cause of posterior segment inflammation. 4. Twenty-five consecutive patients seen in a single referral center between November 2003 and August 2004 were studied. Presumed ocular toxoplasmosis was diagnosed based on the clinical appearance of retinochoroiditis and a positive serology. Lesions were presumed to be active in the presence of whitish edematous areas.5 Fluorescein angiography was performed in active cases. Lesions were presumed to be scarred when pigmented and/or atrophic. Areas of retinochoroiditis were further analyzed by the Zeiss OCT3 imager (Humphrey-Zeiss, San Leandro, California, USA). Two perpendicular scans centered on the lesion were performed. The length of scans was 3 or 6 mm, according to the size of the lesion. The vitreoretinal interface was analyzed and measures of retinal thickness were performed. Fourteen men and 11 women were included. The mean age was 25.5 ± 9.9 years. Fifteen patients (60%) had had a previous episode of retinochoroiditis. Active lesions were observed in 16 subjects and scarred lesions in 9 subjects. Active areas appeared as hyperreflective images, which were localized in the retina in 15 cases and in the choroid in 1 case. Partial posterior vitreous detachment with a localized vitritis contiguous to the site of active retinochoroiditis, was observed in 13 cases. Sub-retinal fluid was detected by OCT in 3 cases, but was present on fluorescein angiograms only in 2 of these cases (Figure ​(Figure1).1). Scarred areas were characterized by retinal atrophy. The mean retinal thickness at the center of active lesions was 353 ± 98 µm (range 184 - 614 µm), compared to 135 ± 95 µm (range 0 - 338 µm) for scarred lesions (p<0.001). Figure 1 Optical Coherence Tomography in ocular toxoplasmosis OCT is safe, non invasive and devoided of any side effects, therefore follow-up measurements are unlimited, while fluorescein angiography is invasive and it carries some risks and side effects in certain subjects. Active toxoplasmic lesions have shown 3 main OCT characteritics: A highly reflective intraretinal area corresponding with the area of retinitis. A posterior hyaloid thickened and detached over the lesion and contained irregular hyperreflective formation. A shadow effect of the underlying choroidal tissue. Reliable measurements of retinal thickness by OCT may be impossible in the presence of severe vitritis. Fluorescein angiograms remain very useful for the assessment of vasculitis and blood-retinal barrier breakdowns, whereas OCT detects sub-retinal fluid with a higher sensitivity. We have shown that OCT imaging can distinguish between active and scarred toxoplasmic lesions. OCT provides quantitative measurements of retinal thickness, which could be useful in future prospective studies, to guide therapeutic decisions and to monitor the efficacy of treatments.

To compare choroidal vascular characteristics of age-related macular degeneration (AMD), polypoidal choroidal vasculopathy (PCV), and central serous chorioretinopathy (CSC) by qualitative and quantitative analyses using swept-source en face optical coherence tomographic (OCT) images. Eyes with non-neovascular AMD (n = 32), neovascular AMD (n = 30), thick and thin choroid PCV (n = 33 and 27), and CSC (n = 34) were enrolled. Subfoveal choroidal thickness (SFCT) and the presence and patterns of pachyvessels were assessed. En face images of the large choroidal vessel layer were converted to binary images for the analysis of vascular density. Pachyvessels were identified in 8 (25%), 14 (46%), 28 (85%), 26 (96%), and 34 (100%) non-neovascular AMD, neovascular AMD, thin choroid PCV, thick choroid PCV, and CSC eyes, respectively (P < 0.001). The pattern of pachyvessels was focal in non-neovascular AMD (100%), neovascular AMD (79%), and thin choroid PCV (89%) while the pattern was mostly diffuse in CSC (88%) and thick choroid PCV (81%). The mean choroidal vascular density in a 6 × 6mm2 macular area of each group was 45.3%, 46.9%, 47.0%, 52.5%, and 54.8%, respectively (P < 0.001). Post hoc analysis revealed significantly higher vascular density in CSC compared with other types (all P < 0.001) except PCV with thick choroid (P = 0.066). Similarities in vascular density of the large choroidal vessel layer and pachyvessel pattern were between CSC and thick choroid PCV and between AMD and thin choroid PCV, suggesting common pathophysiology involving choroidal changes in these eyes.

Optical Coherence Tomography (OCT) is a widely used non-invasive imaging modality that enables detailed visual-ization of retinal microstructures and supports early diagnosis of retinal diseases. However, manual interpretation of OCT scans is time-consuming, subjective, and highly dependent on clinical ex-pertise, which limits scalability in real-world healthcare settings. Although deep learning-based approaches have demonstratedstrong performance in automated retinal disease classification, most existing systems lack interpretability, clinical workflow integration, and automated diagnostic reporting. This paper presents a comprehensive Smart OCT-based Reti-nal Disease Diagnosis System that integrates ensemble deep learn-ing with AI-driven intelligent agents to enhance clinical usability.An ensemble model combining EfficientNetB0 and VGG-16 is employed to perform eight-class retinal disease classification,including Age-related Macular Degeneration (AMD), Choroidal Neovascularization (CNV), Central Serous Retinopathy (CSR),Diabetic Macular Edema (DME), Diabetic Retinopathy (DR),Drusen, Macular Hole (MH), and Normal retina. Beyond classi-fication, the system incorporates LangGraph-based AI agents to automatically generate diagnostic reports, provide visual explana-tions using Grad-CAM, and produce patient-friendly summaries.The proposed framework achieves 95.6% classification accu-racy while addressing key limitations of existing OCT-based di-agnostic systems through improved interpretability, automation,and scalability. Extensive experimental validationdemonstratesthe system’s capability to assist ophthalmologists in making fasterand more accurate diagnostic decisions. Index Terms—Optical Coherence Tomography, Retinal DiseaseClassification, Deep Learning, EfficientNet, VGG-16, Ensem-ble Learning, Explainable AI, LangGraph, Medical Imaging,Computer-Aided Diagnosis

Age-related macular degeneration (ARMD) is a degenerative eye disease among people over the age of 50. This disorder affects the retina and macula of the eye. The macula is the central part of the retina. The function of the macular region is to give a sharp and clear vision. ARMD is mainly identified by the presence of yellow deposits called drusen and the structural changes in the retinal pigment epithelium (RPE) layer of the retina. If ARMD is not identified at an early stage, it will lead to permanent blindness. The ARMD is mainly diagnosed using Color fundus and Optical coherence tomography (OCT) images of the retina. OCT is an imaging technique that makes use of low-coherence light to capture two-and three-dimensional images from within optical scattering media. OCT images usually are at a micrometer resolution, affected by a noise called speckle noise. If speckle noise is present in an OCT image, diagnosis of ARMD is very difficult, irrespective of whether it is manually or automatically. So, removal of speckle-noise is significant for the accurate diagnosis and early detection of ARMD. In this paper, we evaluate the performance of eight popular image noise reduction filters for its ability to remove speckle noise in terms of different performance measures on a publicly available OCT dataset. Our evaluation shows that, out of the filters selected for assessment, a fast non-local mean filter outperforms all other filters regarding all the performance measures used.

Microaneurysms (MAs) are pathognomonic signs that help clinicians to detect diabetic retinopathy (DR) in the early stages. Automatic detection of MA in retinal images is an active area of research due to its application in screening processes for DR which is one of the main reasons of blindness amongst the working-age population. The focus of these works is on the automatic detection of MAs in en face retinal images like fundus color and Fluorescein Angiography (FA). On the other hand, detection of MAs from Optical Coherence Tomography (OCT) images has 2 main advantages: first, OCT is a non-invasive imaging technique that does not require injection, therefore is safer. Secondly, because of the proven application of OCT in detection of Age-Related Macular Degeneration, Diabetic Macular Edema, and normal cases, thanks to detecting MAs in OCT, extensive information is obtained by using this imaging technique. In this research, the concentration is on the diagnosis of MAs using deep learning in the OCT images which represent in-depth structure of retinal layers. To this end, OCT B-scans should be divided into strips and MA patterns should be searched in the resulted strips. Since we need a dataset comprising OCT image strips with suitable labels and such large labelled datasets are not yet available, we have created it. For this purpose, an exact registration method is utilized to align OCT images with FA photographs. Then, with the help of corresponding FA images, OCT image strips are created from OCT B-scans in four labels, namely MA, normal, abnormal, and vessel. Once the dataset of image strips is prepared, a stacked generalization (stacking) ensemble of four fine-tuned, pre-trained convolutional neural networks is trained to classify the strips of OCT images into the mentioned classes. FA images are used once to create OCT strips for training process and they are no longer needed for subsequent steps. Once the stacking ensemble model is obtained, it will be used to classify the OCT strips in the test process. The results demonstrate that the proposed framework classifies overall OCT image strips and OCT strips containing MAs with accuracy scores of 0.982 and 0.987, respectively.

RETRACTED: Deep learning network (DL-Net) based classification and segmentation of multi-class retinal diseases using OCT scans

Feasibility of a Method for En Face Imaging of Photoreceptor Cell Integrity

To present the possibilities of a new system that combines optical coherence tomography (OCT) and confocal ophthalmoscopy, producing en face OCT images in patients with retinal diseases. A prototype OCT Ophthalmoscope (OTI, Toronto, Canada) was used to scan patients with retinal conditions. The system uses a super luminescent diode (lambda = 820 nm; Deltalambda = 20 nm) and currently scans at a rate of 2 frames per second. In each frame, the OCT Ophthalmoscope simultaneously produces a transversal OCT scan and a confocal image in the X/Y plane. Both images correspond pixel to pixel. Between January 2002 and August 2003, >800 patients with various retinal diseases were scanned with the OCT Ophthalmoscope. Illustrative cases with regularly seen macular diseases are presented, such as macular hole and central serous retinopathy. Current difficulties as well as future possibilities of this new en face OCT ophthalmoscope are discussed. By presenting normal and pathologic transversal OCT images made by a prototype OCT Ophthalmoscope, we show that it can provide information not available using conventional OCT imaging.

We propose a framework for automated detection of dry age-related macular degeneration (AMD) and diabetic macular edema (DME) from retina optical coherence tomography (OCT) images, based on sparse coding and dictionary learning. The study aims to improve the classification performance of state-of-the-art methods. First, our method presents a general approach to automatically align and crop retina regions; then it obtains global representations of images by using sparse coding and a spatial pyramid; finally, a multiclass linear support vector machine classifier is employed for classification. We apply two datasets for validating our algorithm: Duke spectral domain OCT (SD-OCT) dataset, consisting of volumetric scans acquired from 45 subjects—15 normal subjects, 15 AMD patients, and 15 DME patients; and clinical SD-OCT dataset, consisting of 678 OCT retina scans acquired from clinics in Beijing—168, 297, and 213 OCT images for AMD, DME, and normal retinas, respectively. For the former dataset, our classifier correctly identifies 100%, 100%, and 93.33% of the volumes with DME, AMD, and normal subjects, respectively, and thus performs much better than the conventional method; for the latter dataset, our classifier leads to a correct classification rate of 99.67%, 99.67%, and 100.00% for DME, AMD, and normal images, respectively.