Optical Coherence Tomography Images Research Articles

Artificial Intelligence in Anterior Chamber Evaluation: A Systematic Review and Meta-analysis.

In this meta-analysis of 6 studies and 5,269 patients, deep learning algorithms applied to AS-OCT demonstrated excellent diagnostic performance for closed-angle compared to gonioscopy, with a pooled sensitivity and specificity of 94% and 93.6%, respectively. This study aimed to review the literature and compare the accuracy of deep learning algorithms (DLA) applied to anterior segment optical coherence tomography images (AS-OCT) against gonioscopy in detecting angle-closure in patients with glaucoma. We performed a systematic review and meta-analysis evaluating DLA in AS-OCT images for the diagnosis of angle closure compared with gonioscopic evaluation. PubMed, Scopus, Embase, Lilacs, Scielo, and Cochrane Central Register of Controlled Trials were searched. The bivariate model was used to calculate pooled sensitivity and specificity. The initial search identified 214 studies, of which 6 were included for final analysis. The total study population included 5,269 patients. The combined sensitivity of the DLA compared with gonioscopy was 94.0% (95% CI 83.8%-97.9%), whereas the pooled specificity was 93.6% (95% CI 85.7%-97.3%). Sensitivity analyses removing each individual study showed a pooled sensitivity in the range of 90.1% to 95.1%. Similarly, specificity results ranged from 90.3 to 94.5% with the removal of each individual study and recalculation of pooled specificity. DLA applied to AS-OCT has excellent sensitivity and specificity in the identification of angle closure. This technology may be a valuable resource in the screening of populations without access to experienced ophthalmologists who perform gonioscopy.

Pharmacoeconomic evaluation of optical coherence tomography for imaging in cardiac interventions

Pharmacoeconomic evaluation of optical coherence tomography for imaging in cardiac interventions

Boundary-Repairing Dual-Path Network for Retinal Layer Segmentation in OCT Image with Pigment Epithelial Detachment.

Automatic retinal layer segmentation in optical coherence tomography (OCT) images is crucial for the diagnosis of ocular diseases. Currently, automatic retinal layer segmentation works well with normal OCT images. However, pigment epithelial detachment (PED) dramatically alters the retinal structure, causing blurred boundaries and partial disappearance of the Bruch's Membrane (BM), thus posing challenges to the segmentation. To tackle these problems, we propose a novel dual-path U-shaped network for simultaneous layer segmentation and boundary regression. This network first designs a feature interaction fusion (FIF) module to strengthen the boundary shape constraints in the layer path. To address the challenge posed by partial BM disappearance and boundary-blurring, we propose a layer boundary repair (LBR) module. This module aims to use contrastive loss to enhance the confidence of blurred boundary regions and refine the segmentation of layer boundaries through the re-prediction head. In addition, we introduce a novel bilateral threshold distance map (BTDM) designed for the boundary path. The BTDM serves to emphasize information within boundary regions. This map, combined with the updated probability map, culminates in topology-guaranteed segmentation results achieved through a topology correction (TC) module. We investigated the proposed network on two severely deformed datasets (i.e., OCTA-500 and Aier-PED) and one slightly deformed dataset (i.e., DUKE). The proposed method achieves an average Dice score of 94.26% on the OCTA-500 dataset, which was 1.5% higher than BAU-Net and outperformed other methods. In the DUKE and Aier-PED datasets, the proposed method achieved average Dice scores of 91.65% and 95.75%, respectively.

Assessing Choroidal Thickness in Pediatric Patients With Unilateral Strabismic Amblyopia by Using Spectral Domain-Enhanced Depth Imaging-Optical Coherence Tomography

Assessing Choroidal Thickness in Pediatric Patients With Unilateral Strabismic Amblyopia by Using Spectral Domain-Enhanced Depth Imaging-Optical Coherence Tomography

Deep learning-driven automated quality assessment of ultra-widefield optical coherence tomography angiography images for diabetic retinopathy

AbstractImage quality assessment (IQA) of fundus images constitutes a foundational step in automated disease analysis. This process is pivotal in supporting the automation of screening, diagnosis, follow-up, and related academic research for diabetic retinopathy (DR). This study introduced a deep learning-based approach for IQA of ultra-widefield optical coherence tomography angiography (UW-OCTA) images of patients with DR. Given the novelty of ultra-widefield technology, its limited prevalence, the high costs associated with equipment and operational training, and concerns regarding ethics and patient privacy, UW-OCTA datasets are notably scarce. To address this, we initially pre-train a vision transformer (ViT) model on a dataset comprising 6 mm × 6 mm OCTA images, enabling the model to acquire a fundamental understanding of OCTA image characteristics and quality indicators. Subsequent fine-tuning on 12 mm × 12 mm UW-OCTA images aims to enhance accuracy in quality assessment. This transfer learning strategy leverages the generic features learned during pre-training and adjusts the model to evaluate UW-OCTA image quality effectively. Experimental results demonstrate that our proposed method achieves superior performance compared to ResNet18, ResNet34, and ResNet50, with an AUC of 0.9026 and a Kappa value of 0.7310. Additionally, ablation studies, including the omission of pre-training on 6 mm × 6 mm OCTA images and the substitution of the backbone network with the ViT base version, resulted in varying degrees of decline in AUC and Kappa values, confirming the efficacy of each module within our methodology.

Quantification of attenuation and speckle features from endoscopic OCT images for the diagnosis of human brain glioma

Application of optical coherence tomography (OCT) in neurosurgery mostly includes the discrimination between intact and malignant tissues aimed at the detection of brain tumor margins. For particular tissue types, the existing approaches demonstrate low performance, which stimulates the further research for their improvement. The analysis of speckle patterns of brain OCT images is proposed to be taken into account for the discrimination between human brain glioma tissue and intact cortex and white matter. The speckle properties provide additional information of tissue structure, which could help to increase the efficiency of tissue differentiation. The wavelet analysis of OCT speckle patterns was applied to extract the power of local brightness fluctuations in speckle and its standard deviation. The speckle properties are analysed together with attenuation ones using a set of ex vivo brain tissue samples, including glioma of different grades. Various combinations of these features are considered to perform linear discriminant analysis for tissue differentiation. The results reveal that it is reasonable to include the local brightness fluctuations at first two wavelet decomposition levels in the analysis of OCT brain images aimed at neurosurgical diagnosis.

Impact of Solar Eclipses on Vision: Insights from Optical Coherence Tomography and Optical Coherence Tomography Angiography Analysis*

Purpose Solar retinopathy, resulting from solar eclipse exposure, poses risks to visual health. This study explores acute and chronic phase findings using clinical examinations and optical coherence tomography (OCT) and optical coherence tomography angiography (OCT-A) with a focus on longitudinal assessment. Methods Seven eyes with a history of unprotected solar eclipse exposure were included. Clinical examination, fundus photography, OCT, and OCT-A imaging were performed at initial assessment, as well as at one-month and six-month follow-up intervals. Data analysis included descriptive statistics. Results The cases, exposed without protection, underwent assessments, revealing variable visual acuity, outer retinal layer, and Henle fiber layer changes during follow-up. Regression of hyperreflectivity within the outer retinal and Henle fiber layers was observed over time in all eyes, although persistent microdefects within the outer retinal layer were noted in specific cases. OCT-A imaging revealed a larger foveal avascular zone, which persisted over a six-month period in select cases. Additionally, affected eyes exhibited a decrease in superficial vascular density, with subsequent improvement noted during the six-month period. Conclusion Solar retinopathy can result in visual impairment, accompanied by alterations observed in the Henle fiber layer using OCT. Additionally, OCT-A findings indicate possible vascular involvement. This study underscores the significance of adopting protective measures during solar eclipses and emphasizes the value of employing longitudinal multimodal imaging techniques to comprehend the pathophysiology of the condition.

Hybrid deep learning and optimal graph search method for optical coherence tomography layer segmentation in diseases affecting the optic nerve

Accurate segmentation of retinal layers in optical coherence tomography (OCT) images is critical for assessing diseases that affect the optic nerve, but existing automated algorithms often fail when pathology causes irregular layer topology, such as extreme thinning of the ganglion cell-inner plexiform layer (GCIPL). Deep LOGISMOS, a hybrid approach that combines the strengths of deep learning and 3D graph search to overcome their limitations, was developed to improve the accuracy, robustness and generalizability of retinal layer segmentation. The method was trained on 124 OCT volumes from both eyes of 31 non-arteritic anterior ischemic optic neuropathy (NAION) patients and tested on three cross-sectional datasets with available reference tracings: Test-NAION (40 volumes from both eyes of 20 NAION subjects), Test-G (29 volumes from 29 glaucoma subjects/eyes), and Test-JHU (35 volumes from 21 multiple sclerosis and 14 control subjects/eyes) and one longitudinal dataset without reference tracings: Test-G-L (155 volumes from 15 glaucoma patients/eyes). In the three test datasets with reference tracings (Test-NAION, Test-G, and Test-JHU), Deep LOGISMOS achieved very high Dice similarity coefficients (%) on GCIPL: 89.97±3.59, 90.63±2.56, and 94.06±1.76, respectively. In the same context, Deep LOGISMOS outperformed the Iowa reference algorithms by improving the Dice score by 17.5, 5.4, and 7.5, and also surpassed the deep learning framework nnU-Net with improvements of 4.4, 3.7, and 1.0. For the 15 severe glaucoma eyes with marked GCIPL thinning (Test-G-L), it demonstrated reliable regional GCIPL thickness measurement over five years. The proposed Deep LOGISMOS approach has potential to enhance precise quantification of retinal structures, aiding diagnosis and treatment management of optic nerve diseases.

Segmentation and quantitative analysis of optical coherence tomography (OCT) images of laser burned skin based on deep learning.

Evaluation of skin recovery is an important step in the treatment of burns. However, conventional methods only observe the surface of the skin and cannot quantify the injury volume. Optical coherence tomography (OCT) is a non-invasive, non-contact, real-time technique. Swept-frequency OCT uses near infrared light and analyzes the intensity of light echo at different depths to generate images from optical interference signals. To quantify the dynamic recovery of skin burns over time, laser induced skin burns in mice were evaluated using deep learning of swept-frequency OCT images.A laser-induced mouse skin thermal injury model was established in thirty Kunming mice, and OCT images of normal and burned areas of mouse skin were acquired at day 0, day 1, day 3, day 7, and day 14 after laser irradiation. This resulted in 7000 normal and 1400 burn B-scan images which were divided into training, validation, and test sets at 8:1.5:0.5 ratio for the normal data and 8:1:1 for the burn data. Normal images were manually annotated, and the deep learning U-Net model (verified with PSPNe and HRNet models) was used to segment the skin into three layers: the dermal epidermal layer, subcutaneous fat layer, and muscle layer. For the burn images, the models were trained to segment just the damaged area. Three-dimensional reconstruction technology was then used to reconstruct the damaged tissue and calculate the damaged tissue volume. The average IoU value and f-score of the normal tissue layer U-Net segmentation model were 0.876 and 0.934 respectively. The IoU value of the burn area segmentation model reached 0.907 and f-score value reached 0.951. Compared with manual labeling, the U-Net model was faster with higher accuracy for skin stratification. OCT and U-Net segmentation can provide rapid and accurate analysis of tissue changes and clinical guidance in the treatment of burns.

Human versus Artificial Intelligence: Validation of a Deep Learning Model for Retinal Layer and Fluid Segmentation in Optical Coherence Tomography Images from Patients with Age-Related Macular Degeneration

Artificial intelligence (AI) models have received considerable attention in recent years for their ability to identify optical coherence tomography (OCT) biomarkers with clinical diagnostic potential and predict disease progression. This study aims to externally validate a deep learning (DL) algorithm by comparing its segmentation of retinal layers and fluid with a gold-standard method for manually adjusting the automatic segmentation of the Heidelberg Spectralis HRA + OCT software Version 6.16.8.0. A total of sixty OCT images of healthy subjects and patients with intermediate and exudative age-related macular degeneration (AMD) were included. A quantitative analysis of the retinal thickness and fluid area was performed, and the discrepancy between these methods was investigated. The results showed a moderate-to-strong correlation between the metrics extracted by both software types, in all the groups, and an overall near-perfect area overlap was observed, except for in the inner segment ellipsoid (ISE) layer. The DL system detected a significant difference in the outer retinal thickness across disease stages and accurately identified fluid in exudative cases. In more diseased eyes, there was significantly more disagreement between these methods. This DL system appears to be a reliable method for accessing important OCT biomarkers in AMD. However, further accuracy testing should be conducted to confirm its validity in real-world settings to ultimately aid ophthalmologists in OCT imaging management and guide timely treatment approaches.

GAMNet: a gated attention mechanism network for grading myopic traction maculopathy in OCT images

GAMNet: a gated attention mechanism network for grading myopic traction maculopathy in OCT images

Discriminating Glioblastoma from Normal Brain Tissue In Vivo Using Optical Coherence Tomography and Angiography: A Texture and Microvascular Analysis Approach

Brain tumors arise from abnormal cell growth in the brain. Glioblastoma, the most common and aggressive type, poses significant challenges for identification during surgery. The primary goal of this study is to identify and differentiate normal brain tissue from glioblastoma tissue using optical coherence tomography (OCT) and OCT angiography (OCTA). These techniques offer a non-invasive way to analyze the morphological and microvascular alternations associated with glioblastoma in an animal model. To monitor the changes in morphology and vascular distribution of brain tissue as glioblastoma tumors grow, time-series OCT and OCTA results were collected for comparison. Texture analysis of OCT images was proposed using the gray-level co-occurrence matrix (GLCM), from which homogeneity and variance were calculated as discriminative parameters. Additionally, OCTA was used to assess microvascular characteristics, including vessel diameter, density, and fractal dimension. The findings demonstrate that the proposed methods can effectively distinguish between normal and cancerous brain tissue in vivo.

The factors associated with retinal pigment epithelium tear development in the early phase after treatment initiation for age-related macular degeneration.

This study aimed to evaluate the factors associated with retinal pigment epithelium (RPE) tear development in the early phase after anti-vascular endothelial growth factor (VEGF) drug initiation in eyes with neovascular age-related macular degeneration (nAMD) and retinal pigment epithelial detachment (PED). Treatment-naive eyes with nAMD and PED for which anti-VEGF drug injections had been initiated and followed up for at least 3months after the 1st anti-VEGF drug injection, were retrospectively investigated. Baseline characteristics of the PEDs, including type, height, and area, were evaluated using fundus photographs, fluorescein angiography, and optical coherence tomography images. The association between patient age, sex, medical history, PED characteristics, and the development of RPE tears within 3months of starting anti-VEGF therapy was examined. This study included 244 eyes (230 patients; mean age 75.0years, 159 males and 71 females). RPE tears occurred in 13 eyes (5.3%) within 3months of the start of anti-VEGF therapy. Multivariate analysis showed an association of the development of RPE tears with PED height (every 100µm, odds ratio [OR]: 1.50, 95% confidence interval [CI]: 1.07-2.12, p = 0.019), PED area (every 10 mm2, OR: 3.02, CI: 1.22-7.46, p = 0.016), and the presence of fibrovascular PED (OR: 59.22, CI: 4.12-850.59, p = 0.002). Eyes with cleft (the hypo-reflective space beneath the fibrovascular PED) were more likely to develop an RPE tear (p = 0.01, χ-square test). Fibrovascular PED, large PED area, high PED height, and the cleft finding are independent risk factors for the development of RPE tears early after the administration of anti-VEGF drugs.

Enhancing the structure–function relationship in glaucoma using anatomical compensation of retinal nerve fibre layer

Background/aimsTo investigate whether compensating retinal nerve fibre layer (RNFL) thickness measurements for demographic and anatomical ocular factors can strengthen the structure–function relationship in patients with glaucoma.Methods600 eyes from 412 patients...

Characteristics and Treatment Outcomes of Hypotony Maculopathy Post-Glaucoma Filtering Surgery: An Optical Coherence Tomography-Based Study.

This study assesses morphological characteristics using SD-OCT in patients with hypotony maculopathy (HM) following glaucoma filtering surgery and evaluates the results of its treatment. Retrospective analysis of all HM patients between January 2019 and March 2023. Inclusion criteria consisted of both pre-operative and post-revision SD-OCT images of the macula and the presence of HM as observed on OCT images preoperatively. HM was graded according to its appearance in OCT both pre- and post-revision surgery. Change in visual acuity and IOP were assessed. 45 eyes of 45 patients were included. 21 eyes had HM limited to retinal pigment epithelium (RPE), 18 eyes had involvement of RPE and photoreceptor layers and 6 eyes had additional intra- or subretinal edema. After revision surgery with IOP elevation, 64% of eyes had complete HM regression with no HM signs in OCT imaging. 80% of patients achieved at least one grade improvement in HM. Preoperative visual acuity increased from 0.7±0.4 (logMAR) to 0.4±0.4 at 2 weeks postoperatively, over the course of an increase of IOP from 3.5±1.8mmHg to 17.1±10.6mmHg at day one. Eyes with complete HM regression had higher IOP at day 1 compared to those without improvement (P=0.04). The median time between HM onset and revision was 10.0 days for those with complete regression and 27 days for those without improvement (P=0.04). Bleb revision procedures for HM following glaucoma filtering surgery show promising outcomes, including notable improvements in visual acuity and IOP. The timing of revision surgery appears to influence outcome. In our study earlier intervention was associated with better results. Even delayed surgeries can lead to an improvement, although complete morphological restoration may not be achieved in advanced grades of HM.

Unveiling Key Pathological Indicators for Disease Progression in Vogt Koyanagi Harada Disease and Sympathetic Ophthalmia Through Advanced Choroidal Volume Analysis

ABSTRACT Purpose To evaluate the association between quantitative parameters derived from volume analysis of optical coherence tomography (OCT) data and disease worsening in Vogt-Koyanagi-Harada disease (VKHD) and sympathetic ophthalmia (SO). Methods This retrospective study, conducted at Osaka University Hospital, employed swept-source OCT scans from patients diagnosed with VKHD or SO between October 2012 and January 2021. The choroidal vessel structure was segmented and visualized in three dimensions, generating quantitative vessel volume maps. Region-specific choroidal vessel volume (CVV), choroidal volume (CV), and vessel index (VI) were scrutinized for their potential correlation with disease severity. Results Thirty-five eyes of 18 VKHD and 2 SO patient (8 females, 10 males) were evaluated. OCT-derived CVV maps revealed regional CV alterations in VKHD and SO patients. Two parameters, i.e. CV at 3- and 6-month follow-ups (p = 0.044, p = 0.040, respectively, with area under the ROC curve of 0.70) and CVV at 6 months (p = 0.046, area under the ROC curve of 0.71), were significantly higher in recurrent VKHD and SO compared to effectively treated cases. Conclusions The volume analysis of OCT images facilitates a three-dimensional visualization of choroidal alterations, which may serve as a reflection of disease severity in VKHD and SO patients. Furthermore, noninvasive initial CVV or CV measurements may serve as potential biomarkers for predicting disease recurrence in VKHD and SO.

Ensemble learning for retinal disease recognition under limited resources.

Retinal optical coherence tomography (OCT) images provide crucial insights into the health of the posterior ocular segment. Therefore, the advancement of automated image analysis methods is imperative to equip clinicians and researchers with quantitative data, thereby facilitating informed decision-making. The application of deep learning (DL)-based approaches has gained extensive traction for executing these analysis tasks, demonstrating remarkable performance compared to labor-intensive manual analyses. However, the acquisition of retinal OCT images often presents challenges stemming from privacy concerns and the resource-intensive labeling procedures, which contradicts the prevailing notion that DL models necessitate substantial data volumes for achieving superior performance. Moreover, limitations in available computational resources constrain the progress of high-performance medical artificial intelligence, particularly in less developed regions and countries. This paper introduces a novel ensemble learning mechanism designed for recognizing retinal diseases under limited resources (e.g., data, computation). The mechanism leverages insights from multiple pre-trained models, facilitating the transfer and adaptation of their knowledge to retinal OCT images. This approach establishes a robust model even when confronted with limited labeled data, eliminating the need for an extensive array of parameters, as required in learning from scratch. Comprehensive experimentation on real-world datasets demonstrates that the ensemble models constructed by the proposed ensemble method show superior performance over the baseline models under sparse labeled data, especially the triple ensemble model, which achieves the accuracy of 92.06%, which is 8.27%, 7.99%, and 11.14% better than the three baseline models, respectively. In addition, compared with the three baseline models learned from scratch, the triple ensemble model has fewer trainable parameters, only 3.677M, which is lower than the three baseline models of 8.013M, 4.302M, and 20.158M, respectively.

Abstract PO1-07-08: Value of high resolution full field optical coherence tomography and dynamic cell imaging (D-FFOCT) for one-stop rapid diagnosis breast clinic

Abstract BACKGROUND: Full field optical coherence tomography combined to dynamic cell imaging (D-FFOCT) is a new, simple to use, nondestructive, quick technique than can provide sufficient spatial resolution to mimic histopathological analysis. The objective of this study was to evaluate diagnostic performance of D-FFOCT for one-stop rapid diagnosis breast clinic. METHODS: D-FFOCT was applied to fresh untreated breast and nodes biopsies. Four different readers (senior and junior radiologist, surgeon and pathologist) analyzed the samples without knowing final histological diagnosis or ACR classification. The results were compared to conventional processing and staining (Hematoxylin-eosin). RESULTS: A total of 217 biopsies were performed on 152 patients. There were 144 breast biopsies and 61 lymph nodes with 101 infiltrative cancers (49,27%), 99 benign lesions (48,29%), 3 ductal in situ carcinoma (1,46%) and 2 atypias (0,98%). The diagnostic performances results were as follow: sensitivity: 77% [0.7;0.82], specificity: 64% [0.58;0.71], PPV: 74% [0.68;0.78] and NPV: 75% [0.72;0.78]. A large images atlas was created as well as a diagnosis algorithm from the readers experience. CONCLUSION: D-FFOCT provides interesting results defining malignancy on fresh breast and nodes samples. By training with the diagnosis algorithm and the images atlas or by using machine learning, radiologists could obtain even better outcomes allowing quick detection of breast cancer and lymph node involvement. diagnosis algorithm Figure 1 Images in DCI and FFOCT Two infiltrating cancer diagnosed on pathology (A,B). Focusing on cells (C). Normal galactophoric duct (D). Adenofibroma (E) Citation Format: Alexis Simon, Yasmina Badachi, Jacques Ropers, Isaura Laurent, Lida Dong, Elisabeth Da Maia, Agnes Bourcier, Geoffroy Canlorbe, Catherine Uzan. Value of high resolution full field optical coherence tomography and dynamic cell imaging (D-FFOCT) for one-stop rapid diagnosis breast clinic [abstract]. In: Proceedings of the 2023 San Antonio Breast Cancer Symposium; 2023 Dec 5-9; San Antonio, TX. Philadelphia (PA): AACR; Cancer Res 2024;84(9 Suppl):Abstract nr PO1-07-08.

Performance analysis of a liquid lens for laser ablation using OCT imaging

Performance analysis of a liquid lens for laser ablation using OCT imaging

Pedicle transposition flap, inverted flap, free flap, and standard peel for large full thickness macular holes: a comparative study.

Purpose: To compare anatomical and functional outcomes of four different techniques for the treatment of large idiopathic full-thickness macular holes (FTMH). Methods: Single-centre retrospective study including 129 eyes of 126 patients with large (>500μm) FTMH between January 2018-October 2022. All patients underwent 23/25G vitrectomy and gas with standard internal limiting membrane (ILM) peel, pedicle transposition, inverted, or free flap technique. Postoperative OCT images were assessed by 2 independent masked graders. Results: Mean age was 73.2 years (SD 8.4) with median F/U of 5 months (IQR 8). Overall anatomical success rate was 81%; significantly lower (59%) for the standard ILM peel (p<0.0001). The pedicle transposition flap showed superior visual recovery compared to the free flap (+27 vs +12 ETDRS letters, p=0.02). At 3 months, restoration of the ELM was significantly better for the pedicle transposition flap compared to free flap and standard ILM peel (p=0.008 and p=0.03), and superior to all the other techniques at 6 months (p=0.02, p=0.04 and p=0.006). Conclusions: Standard ILM peel alone offers inferior outcomes for the management of large FTMH. Of the alternative ILM techniques, despite similar closure rates, foveal microstructural recovery is most complete following the pedicle transposition flap, and least complete following the free flap.