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

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.

Choroidal Vessel and Stromal Volumetric Analysis After Photodynamic Therapy or Focal Laser for Central Serous Chorioretinopathy.

Purpose: To utilize volumetric analysis to quantify volumetric changes in choroidal vessels and stroma after photodynamic therapy (PDT) and focal laser photocoagulation (PC) for central serous chorioretinopathy (CSCR). Methods: This retrospective, comparative study included 58 eyes (58 patients) with CSCR (PC, 33 eyes; PDT, 25 eyes) followed up with swept-source optical coherence tomography at 3 months after treatment. Three-dimensional (3D) choroidal vessel and stromal volumes in each area of the central 1.5-mm-diameter circle, the torus-shaped area with 6-mm-diameter circle excluding the area of the central 1.5-mm-diameter circle, and the treated area of the Early Treatment Diabetic Retinopathy Study (ETDRS) grid centered at the fovea were analyzed using a deep learning-based method. Changes in volume at baseline and 1 and 3 months after treatment were compared. Results: The mean patient age was 49.3 ± 10.5 years. In the central 1.5-mm-diameter circle, the mean vessel and stromal volume rates significantly decreased after the treatment in both the PDT and PC groups (P = 0.00029 and P = 0.0014, respectively), and significant differences between the PDT and PC groups of continuous variables within times were observed in both volumes (P = 0.024 and P = 0.037, respectively). In the torus-shaped area and treated area, the PDT and PC groups both showed similar decreases in vessel and stromal volume over time. Conclusions: In the 3D optical coherence tomography volumetric analysis, both PDT and focal PC reduced choroid vessel volume in eyes with CSCR. Translational Relevance: This new finding is useful in elucidating the pathogenesis and healing mechanisms of CSCR.

Three-Dimensional Volume Calculation of Intrachoroidal Cavitation Using Deep-Learning-Based Noise Reduction of Optical Coherence Tomography.

Purpose: Intrachoroidal cavitations (ICCs) are peripapillary pathological lesions generally associated with high myopia that can cause visual field (VF) defects. The current study aimed to evaluate a three-dimensional (3D) volume parameter of ICCs segmented from volumetric swept-source optical coherence tomography (SS-OCT) images processed using deep learning (DL)-based noise reduction and to investigate its correlation with VF sensitivity. Methods: Thirteen eyes of 12 consecutive patients with peripapillary ICCs were enrolled. DL-based denoising and further analyses were applied to parapapillary 6 × 6-mm volumetric SS-OCT scans. Then, 3D ICC volume and two-dimensional depth and length measurements of the ICCs were calculated. The correlations between ICC parameters and VF sensitivity were investigated. Results: The ICCs were located in the inferior hemiretina in all eyes. ICC volume (P = 0.02; regression coefficient [RC], -0.007) and ICC length (P = 0.04; RC, -4.51) were negatively correlated with the VF mean deviation, whereas ICC depth (P = 0.15) was not. All of the parameters, including ICC volume (P = 0.01; RC, -0.004), ICC depth (P = 0.02; RC, -0.008), and ICC length (P = 0.045; RC, -2.11), were negatively correlated with the superior mean total deviation. Conclusions: We established the volume of ICCs as a new 3D parameter, and it reflected their influence on visual function. The automatic delineation and 3D rendering may lead to improved detection and pathological understanding of ICCs. Translational Relevance: This study demonstrated the correlation between the 3D volume of ICCs and VF sensitivity.

Diagnosis of Choroidal Disease With Deep Learning-Based Image Enhancement and Volumetric Quantification of Optical Coherence Tomography.

Purpose: The purpose of this study was to quantify choroidal vessels (CVs) in pathological eyes in three dimensions (3D) using optical coherence tomography (OCT) and a deep-learning analysis. Methods: A single-center retrospective study including 34 eyes of 34 patients (7 women and 27 men) with treatment-naïve central serous chorioretinopathy (CSC) and 33 eyes of 17 patients (7 women and 10 men) with Vogt-Koyanagi-Harada disease (VKH) or sympathetic ophthalmitis (SO) were imaged consecutively between October 2012 and May 2019 with a swept source OCT. Seventy-seven eyes of 39 age-matched volunteers (26 women and 13 men) with no sign of ocular pathology were imaged for comparison. Deep-learning-based image enhancement pipeline enabled CV segmentation and visualization in 3D, after which quantitative vessel volume maps were acquired to compare normal and diseased eyes and to track the clinical course of eyes in the disease group. Region-based vessel volumes and vessel indices were utilized for disease diagnosis. Results: OCT-based CV volume maps disclose regional CV changes in patients with CSC, VKH, or SO. Three metrics, (i) choroidal volume, (ii) CV volume, and (iii) CV index, exhibit high sensitivity and specificity in discriminating pathological choroids from healthy ones. Conclusions: The deep-learning analysis of OCT images described here provides a 3D visualization of the choroid, and allows quantification of features in the datasets to identify choroidal disease and distinguish between different diseases. Translational Relevance: This novel analysis can be applied retrospectively to existing OCT datasets, and it represents a significant advance toward the automated diagnosis of choroidal pathologies based on observations and quantifications of the vasculature.

Automated layer segmentation of macular OCT images using dual-scale gradient information.

A novel automated boundary segmentation algorithm is proposed for fast and reliable quantification of nine intra-retinal boundaries in optical coherence tomography (OCT) images. The algorithm employs a two-step segmentation schema based on gradient information in dual scales, utilizing local and complementary global gradient information simultaneously. A shortest path search is applied to optimize the edge selection. The segmentation algorithm was validated with independent manual segmentation and a reproducibility study. It demonstrates high accuracy and reproducibility in segmenting normal 3D OCT volumes. The execution time is about 16 seconds per volume (480x512x128 voxels). The algorithm shows potential for quantifying images from diseased retinas as well.

Deep learning based noise reduction method for automatic 3D segmentation of the anterior of lamina cribrosa in optical coherence tomography volumetric scans.

A deep-learning (DL) based noise reduction algorithm, in combination with a vessel shadow compensation method and a three-dimensional (3D) segmentation technique, has been developed to achieve, to the authors best knowledge, the first automatic segmentation of the anterior surface of the lamina cribrosa (LC) in volumetric ophthalmic optical coherence tomography (OCT) scans. The present DL-based OCT noise reduction algorithm was trained without the need of noise-free ground truth images by utilizing the latest development in deep learning of de-noising from single noisy images, and was demonstrated to be able to cover more locations in the retina and disease cases of different types to achieve high robustness. Compared with the original single OCT images, a 6.6 dB improvement in peak signal-to-noise ratio and a 0.65 improvement in the structural similarity index were achieved. The vessel shadow compensation method analyzes the energy profile in each A-line and automatically compensates the pixel intensity of locations underneath the detected blood vessel. Combining the noise reduction algorithm and the shadow compensation and contrast enhancement technique, medical experts were able to identify the anterior surface of the LC in 98.3% of the OCT images. The 3D segmentation algorithm employs a two-round procedure based on gradients information and information from neighboring images. An accuracy of 90.6% was achieved in a validation study involving 180 individual B-scans from 36 subjects, compared to 64.4% in raw images. This imaging and analysis strategy enables the first automatic complete view of the anterior LC surface, to the authors best knowledge, which may have the potentials in new LC parameters development for glaucoma diagnosis and management.

Microscope-Integrated Intraoperative Ultrahigh-Speed Swept-Source Optical Coherence Tomography for Widefield Retinal and Anterior Segment Imaging.

BACKGROUND AND OBJECTIVE: To demonstrate the feasibility of retinal and anterior segment intraoperative widefield imaging using an ultrahigh-speed, swept-source optical coherence tomography (SS-OCT) surgical microscope attachment. PATIENTS AND METHODS: A prototype post-objective SS-OCT using a 1,050-nm wavelength, 400 kHz A-scan rate, vertical cavity surface-emitting laser (VCSEL) light source was integrated to a commercial ophthalmic surgical microscope after the objective. Each widefield OCT data set was acquired in 3 seconds (1,000 × 1,000 A-scans, 12 × 12 mm2 for retina and 10 × 10 mm2 for anterior segment). RESULTS: Intraoperative SS-OCT was performed in 20 eyes of 20 patients. In six of seven membrane peels and five of seven rhegmatogenous retinal detachment repair surgeries, widefield retinal imaging enabled evaluation pre- and postoperatively. In all seven cataract cases, anterior imaging evaluated the integrity of the posterior lens capsule. CONCLUSIONS: Ultrahigh-speed SS-OCT enables widefield intraoperative viewing in the posterior and anterior eye. Widefield imaging visualizes ocular structures and pathology without requiring OCT realignment. [Ophthalmic Surg Lasers Imaging Retina. 2018;49:94-102.].

Optical Coherence Tomography in the UK Biobank Study - Rapid Automated Analysis of Retinal Thickness for Large Population-Based Studies.

PURPOSE: To describe an approach to the use of optical coherence tomography (OCT) imaging in large, population-based studies, including methods for OCT image acquisition, storage, and the remote, rapid, automated analysis of retinal thickness. METHODS: In UK Biobank, OCT images were acquired between 2009 and 2010 using a commercially available "spectral domain" OCT device (3D OCT-1000, Topcon). Images were obtained using a raster scan protocol, 6 mm x 6 mm in area, and consisting of 128 B-scans. OCT image sets were stored on UK Biobank servers in a central repository, adjacent to high performance computers. Rapid, automated analysis of retinal thickness was performed using custom image segmentation software developed by the Topcon Advanced Biomedical Imaging Laboratory (TABIL). This software employs dual-scale gradient information to allow for automated segmentation of nine intraretinal boundaries in a rapid fashion. RESULTS: 67,321 participants (134,642 eyes) in UK Biobank underwent OCT imaging of both eyes as part of the ocular module. 134,611 images were successfully processed with 31 images failing segmentation analysis due to corrupted OCT files or withdrawal of subject consent for UKBB study participation. Average time taken to call up an image from the database and complete segmentation analysis was approximately 120 seconds per data set per login, and analysis of the entire dataset was completed in approximately 28 days. CONCLUSIONS: We report an approach to the rapid, automated measurement of retinal thickness from nearly 140,000 OCT image sets from the UK Biobank. In the near future, these measurements will be publically available for utilization by researchers around the world, and thus for correlation with the wealth of other data collected in UK Biobank. The automated analysis approaches we describe may be of utility for future large population-based epidemiological studies, clinical trials, and screening programs that employ OCT imaging.

Three-dimensional and high-speed swept-source optical coherence tomography for in vivo investigation of human anterior eye segments.

A two- and three-dimensional swept source optical coherence tomography (SS-OCT) system, which uses a ready-to-ship scanning light source, is demonstrated. The light source has a center wavelength of 1.31 mum, -3 dB wavelength range of 110 nm, scanning rate of 20 KHz, and high linearity in frequency scanning. This paper presents a simple calibration method using a fringe analysis technique for spectral rescaling. This SS-OCT system is capable of realtime display of two-dimensional OCT and can obtain three-dimensional OCT with a measurement time of 2 s. In vivo human anterior eye segments are investigated two- and three-dimensionally. The system sensitivity is experimentally determined to be 114 dB. The three-dimensional OCT volumes reveal the structures of the anterior eye segments, which are difficult to observe in two-dimensional OCT images.

Automated segmentation of outer retinal layers in macular OCT images of patients with retinitis pigmentosa.

To provide a tool for quantifying the effects of retinitis pigmentosa (RP) seen on spectral domain optical coherence tomography images, an automated layer segmentation algorithm was developed. This algorithm, based on dual-gradient information and a shortest path search strategy, delineates the inner limiting membrane and three outer retinal boundaries in optical coherence tomography images from RP patients. In addition, an automated inner segment (IS)/outer segment (OS) contour detection method based on the segmentation results is proposed to quantify the locus of points at which the OS thickness goes to zero in a 3D volume scan. The segmentation algorithm and the IS/OS contour were validated with manual segmentation data. The segmentation and IS/OS contour results on repeated measures showed good within-day repeatability, while the results on data acquired on average 22.5 months afterward demonstrated a possible means to follow disease progression. In particular, the automatically generated IS/OS contour provided a possible objective structural marker for RP progression.