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PURPOSE: To evaluate and compare subbasal corneal nerve parameters of the inferior whorl in patients with dry eye disease (DED), neuropathic corneal pain (NCP), and controls using a novel deep-learning-based algorithm to analyze in-vivo confocal microscopy (IVCM) images. METHODS: Subbasal nerve plexus (SNP) images of the inferior whorl of patients with DED (n = 49, 77 eyes), NCP (n = 14, 24 eyes), and controls (n = 41, 59 eyes) were taken with IVCM and further analyzed using an open-source artificial intelligence (AI)-based algorithm previously developed by our group. This algorithm automatically segments nerves, immune cells, and neuromas in the SNP. The following parameters were compared between groups: nerve area density, average nerve thickness, average nerve segment tortuosity, junction point density, neuroma density, and immune cell density. RESULTS: 160 eyes of 104 patients (63 % females), aged 56.8 ± 15.4 years, were included. The mean nerve area density was significantly lower in the DED (P = 0.012) and NCP (P < 0.001) groups compared to the control group. The junction point density was lower in the NCP group compared with control (P = 0.001) and DED (P = 0.004) groups. The immune cell density was higher in the DED group compared with controls (P < 0.001). CONCLUSIONS: Deep-learning-based analysis of IVCM images of the corneal SNP inferior whorl distinguished a decreased mean nerve area density in patients with DED and NCP compared with controls and an increased immune cell density in patients with oGVHD- and SS-associated DED. These findings suggest that the inferior whorl could be used as landmark to distinguish between patients with DED and NCP.
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Photoreceptors (PRs) and retinal pigment epithelial (RPE) cells form a functional unit called the PR-RPE complex. The PR-RPE complex plays a critical role in maintaining retinal homeostasis and function, and the quantification of its structure and topographical arrangement across the macula are important for understanding the etiology, mechanisms, and progression of many retinal diseases. However, the three-dimensional cellular morphology of the PR-RPE complex in living human eyes has not been completely described due to limitations in imaging techniques. We used the cellular resolution and depth-sectioning capabilities of a custom, high-speed Fourier domain mode-locked laser-based adaptive optics-optical coherence tomography (FDML-AO-OCT) platform to characterize human PR-RPE complex topography across the temporal macula from eleven healthy volunteers. With the aid of a deep learning algorithm, key metrics were extracted from the PR-RPE complex of averaged AO-OCT volumes including PR and RPE cell density, PR outer segment length (OSL), and PR/RPE ratio. We found a tight grouping among our cohort for PR density, with a mean (±SD) value of 53,329 (±8106) cells/mm2 at 1° decreasing to 8669 (±737) cells/mm2 at 12°. We observed a power function relationship between eccentricity and both PR density and PR/RPE ratio. We found similar variability in our RPE density measures, with a mean value of 7335 (±681) cells/mm2 at 1° decreasing to 5547 (±356) cells/mm2 at 12°, exhibiting a linear relationship with a negative slope of -123 cells/mm2 per degree. OSL monotonically decreased from 33.3 (±2.4) µm at 1° to 18.0 (±1.8) µm at 12°, following a second-order polynomial relationship. PR/RPE ratio decreased from 7.3 (±0.9) µm at 1° to 1.5 (±0.1) µm at 12°. The normative data from this investigation will help lay a foundation for future studies of retinal pathology.
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Age and elevated intraocular pressure (IOP) are the two primary risk factors for glaucoma, an optic neuropathy that is the leading cause of irreversible blindness. In most people, IOP is tightly regulated over a lifetime by the conventional outflow tissues. However, the mechanistic contributions of age to conventional outflow dysregulation, elevated IOP and glaucoma are unknown. To address this gap in knowledge, we studied how age affects the morphology, biomechanical properties and function of conventional outflow tissues in C57BL/6 mice, which have an outflow system similar to humans. As reported in humans, we observed that IOP in mice was maintained within a tight range over their lifespan. Remarkably, despite a constellation of age-related changes to the conventional outflow tissues that would be expected to hinder aqueous drainage and impair homeostatic function (decreased cellularity, increased pigment accumulation, increased cellular senescence and increased stiffness), outflow facility, a measure of conventional outflow tissue fluid conductivity, was stable with age. We conclude that the murine conventional outflow system has significant functional reserve in healthy eyes. However, these age-related changes, when combined with other underlying factors, such as genetic susceptibility, are expected to increase risk for ocular hypertension and glaucoma.
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Envejecimiento , Homeostasis , Presión Intraocular , Ratones Endogámicos C57BL , Animales , Presión Intraocular/fisiología , Envejecimiento/fisiología , Ratones , Glaucoma/fisiopatología , Glaucoma/patología , Glaucoma/metabolismo , Masculino , HumanosRESUMEN
The Editor-in-Chief and Deputy Editor of Biomedical Optics Express announce the prize for the best paper published in the Journal between 2020 and 2022.
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PURPOSE: The aim of this study was to facilitate deep learning systems in image annotations for diagnosing keratitis type by developing an automated algorithm to classify slit-lamp photographs (SLPs) based on illumination technique. METHODS: SLPs were collected from patients with corneal ulcer at Kellogg Eye Center, Bascom Palmer Eye Institute, and Aravind Eye Care Systems. Illumination techniques were slit beam, diffuse white light, diffuse blue light with fluorescein, and sclerotic scatter (ScS). Images were manually labeled for illumination and randomly split into training, validation, and testing data sets (70%:15%:15%). Classification algorithms including MobileNetV2, ResNet50, LeNet, AlexNet, multilayer perceptron, and k-nearest neighborhood were trained to distinguish 4 type of illumination techniques. The algorithm performances on the test data set were evaluated with 95% confidence intervals (CIs) for accuracy, F1 score, and area under the receiver operator characteristics curve (AUC-ROC), overall and by class (one-vs-rest). RESULTS: A total of 12,132 images from 409 patients were analyzed, including 41.8% (n = 5069) slit-beam photographs, 21.2% (2571) diffuse white light, 19.5% (2364) diffuse blue light, and 17.5% (2128) ScS. MobileNetV2 achieved the highest overall F1 score of 97.95% (CI, 97.94%-97.97%), AUC-ROC of 99.83% (99.72%-99.9%), and accuracy of 98.98% (98.97%-98.98%). The F1 scores for slit beam, diffuse white light, diffuse blue light, and ScS were 97.82% (97.80%-97.84%), 96.62% (96.58%-96.66%), 99.88% (99.87%-99.89%), and 97.59% (97.55%-97.62%), respectively. Slit beam and ScS were the 2 most frequently misclassified illumination. CONCLUSIONS: MobileNetV2 accurately labeled illumination of SLPs using a large data set of corneal images. Effective, automatic classification of SLPs is key to integrating deep learning systems for clinical decision support into practice workflows.
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Iluminación , Redes Neurales de la Computación , Humanos , Luz , Lámpara de Hendidura , CórneaRESUMEN
Age and elevated intraocular pressure (IOP) are the two primary risk factors for glaucoma, an optic neuropathy that is the leading cause of irreversible blindness. In most people, IOP is tightly regulated over a lifetime by the conventional outflow tissues. However, the mechanistic contributions of age to conventional outflow dysregulation, elevated IOP and glaucoma are unknown. To address this gap in knowledge, we studied how age affects the morphology, biomechanical properties and function of conventional outflow tissues in C57BL/6 mice, which have an outflow system similar to humans. As reported in humans, we observed that IOP in mice was maintained within a tight range over their lifespan. Remarkably, despite a constellation of age-related changes to the conventional outflow tissues that would be expected to hinder aqueous drainage and impair homeostatic function (decreased cellularity, increased pigment accumulation, increased cellular senescence and increased stiffness), outflow facility, a measure of conventional outflow tissue fluid conductivity, was stable with age. We conclude that the murine conventional outflow system has significant functional reserve in healthy eyes. However, these age-related changes, when combined with other underlying factors, such as genetic susceptibility, are expected to increase risk for ocular hypertension and glaucoma.
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Anatomical consistency in biomarker segmentation is crucial for many medical image analysis tasks. A promising paradigm for achieving anatomically consistent segmentation via deep networks is incorporating pixel connectivity, a basic concept in digital topology, to model inter-pixel relationships. However, previous works on connectivity modeling have ignored the rich channel-wise directional information in the latent space. In this work, we demonstrate that effective disentanglement of directional sub-space from the shared latent space can significantly enhance the feature representation in the connectivity-based network. To this end, we propose a directional connectivity modeling scheme for segmentation that decouples, tracks, and utilizes the directional information across the network. Experiments on various public medical image segmentation benchmarks show the effectiveness of our model as compared to the state-of-the-art methods. Code is available at https://github.com/Zyun-Y/DconnNet.
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PURPOSE: The aim of this study was to perform automated segmentation of corneal nerves and other structures in corneal confocal microscopy (CCM) images of the subbasal nerve plexus (SNP) in eyes with ocular surface diseases (OSDs). METHODS: A deep learning-based 2-stage algorithm was designed to perform segmentation of SNP features. In the first stage, to address applanation artifacts, a generative adversarial network-enabled deep network was constructed to identify 3 neighboring corneal layers on each CCM image: epithelium, SNP, and stroma. This network was trained/validated on 470 images of each layer from 73 individuals. The segmented SNP regions were further classified in the second stage by another deep network as follows: background, nerve, neuroma, and immune cells. Twenty-one-fold cross-validation was used to assess the performance of the overall algorithm on a separate data set of 207 manually segmented SNP images from 43 patients with OSD. RESULTS: For the background, nerve, neuroma, and immune cell classes, the Dice similarity coefficients of the proposed automatic method were 0.992, 0.814, 0.748, and 0.736, respectively. The performance metrics for automatic segmentations were statistically better or equal as compared to human segmentation. In addition, the resulting clinical metrics had good to excellent intraclass correlation coefficients between automatic and human segmentations. CONCLUSIONS: The proposed automatic method can reliably segment potential CCM biomarkers of OSD onset and progression with accuracy on par with human gradings in real clinical data, which frequently exhibited image acquisition artifacts. To facilitate future studies on OSD, we made our data set and algorithms freely available online as an open-source software package.
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Córnea , Neuroma , Humanos , Algoritmos , Benchmarking , Microscopía ConfocalRESUMEN
Purpose: Choroidal vascular changes occur with normal aging and age-related macular degeneration (AMD). Here, we evaluate choroidal thickness and vascularity in aged rhesus macaques to better understand the choroid's role in this nonhuman primate model of AMD. Methods: We analyzed optical coherence tomography (OCT) images of 244 eyes from 122 rhesus macaques (aged 4-32 years) to measure choroidal thickness (CT) and choroidal vascularity index (CVI). Drusen number, size, and volume were measured by semiautomated annotation and segmentation of OCT images. We performed regression analyses to determine any association of CT or CVI with age, sex, and axial length and to determine if the presence and volume of soft drusen impacted these choroidal parameters. Results: In rhesus macaques, subfoveal CT decreased with age at 3.2 µm/y (R2 = 0.481, P < 0.001), while CVI decreased at 0.66% per year (R2 = 0.257, P < 0.001). Eyes with soft drusen exhibited thicker choroid (179.9 ± 17.5 µm vs. 162.0 ± 27.9 µm, P < 0.001) and higher CVI (0.612 ± 0.051 vs. 0.577 ± 0.093, P = 0.005) than age-matched control animals. Neither CT or CVI appeared to be associated with drusen number, size, or volume in this cohort. However, some drusen in macaques were associated with underlying choroidal vessel enlargement resembling pachydrusen in human patients with AMD. Conclusions: Changes in the choroidal vasculature in rhesus macaques resemble choroidal changes in human aging, but eyes with drusen exhibit choroidal thickening, increased vascularity, and phenotypic characteristics of pachydrusen observed in some patients with AMD.
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Degeneración Macular , Drusas Retinianas , Humanos , Animales , Macaca mulatta , Estudios Retrospectivos , Retina , Coroides/irrigación sanguínea , Envejecimiento , Tomografía de Coherencia Óptica/métodosRESUMEN
Purpose: To develop a fully-automatic hybrid algorithm to jointly segment and quantify biomarkers of polypoidal choroidal vasculopathy (PCV) on indocyanine green angiography (ICGA) and spectral domain-OCT (SD-OCT) images. Design: Evaluation of diagnostic test or technology. Participants: Seventy-two participants with PCV enrolled in clinical studies at Singapore National Eye Center. Methods: The dataset consisted of 2-dimensional (2-D) ICGA and 3-dimensional (3-D) SD-OCT images which were spatially registered and manually segmented by clinicians. A deep learning-based hybrid algorithm called PCV-Net was developed for automatic joint segmentation of biomarkers. The PCV-Net consisted of a 2-D segmentation branch for ICGA and 3-D segmentation branch for SD-OCT. We developed fusion attention modules to connect the 2-D and 3-D branches for effective use of the spatial correspondence between the imaging modalities by sharing learned features. We also used self-supervised pretraining and ensembling to further enhance the performance of the algorithm without the need for additional datasets. We compared the proposed PCV-Net to several alternative model variants. Main Outcome Measures: The PCV-Net was evaluated based on the Dice similarity coefficient (DSC) of the segmentations and the Pearson's correlation and absolute difference of the clinical measurements obtained from the segmentations. Manual grading was used as the gold standard. Results: The PCV-Net showed good performance compared to manual grading and alternative model variants based on both quantitative and qualitative analyses. Compared to the baseline variant, PCV-Net improved the DSC by 0.04 to 0.43 across the different biomarkers, increased the correlations, and decreased the absolute differences of clinical measurements of interest. Specifically, the largest average (mean ± standard error) DSC improvement was for intraretinal fluid, from 0.02 ± 0.00 (baseline variant) to 0.45 ± 0.06 (PCV-Net). In general, improving trends were observed across the model variants as more technical specifications were added, demonstrating the importance of each aspect of the proposed method. Conclusion: The PCV-Net has the potential to aid clinicians in disease assessment and research to improve clinical understanding and management of PCV. Financial Disclosures: Proprietary or commercial disclosure may be found after the references.
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Optical coherence tomography (OCT) helps ophthalmologists assess macular edema, accumulation of fluids, and lesions at microscopic resolution. Quantification of retinal fluids is necessary for OCT-guided treatment management, which relies on a precise image segmentation step. As manual analysis of retinal fluids is a time-consuming, subjective, and error-prone task, there is increasing demand for fast and robust automatic solutions. In this study, a new convolutional neural architecture named RetiFluidNet is proposed for multi-class retinal fluid segmentation. The model benefits from hierarchical representation learning of textural, contextual, and edge features using a new self-adaptive dual-attention (SDA) module, multiple self-adaptive attention-based skip connections (SASC), and a novel multi-scale deep self-supervision learning (DSL) scheme. The attention mechanism in the proposed SDA module enables the model to automatically extract deformation-aware representations at different levels, and the introduced SASC paths further consider spatial-channel interdependencies for concatenation of counterpart encoder and decoder units, which improve representational capability. RetiFluidNet is also optimized using a joint loss function comprising a weighted version of dice overlap and edge-preserved connectivity-based losses, where several hierarchical stages of multi-scale local losses are integrated into the optimization process. The model is validated based on three publicly available datasets: RETOUCH, OPTIMA, and DUKE, with comparisons against several baselines. Experimental results on the datasets prove the effectiveness of the proposed model in retinal OCT fluid segmentation and reveal that the suggested method is more effective than existing state-of-the-art fluid segmentation algorithms in adapting to retinal OCT scans recorded by various image scanning instruments.
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Edema Macular , Tomografía de Coherencia Óptica , Humanos , Tomografía de Coherencia Óptica/métodos , Retina/diagnóstico por imagen , Algoritmos , Redes Neurales de la Computación , Procesamiento de Imagen Asistido por Computador/métodosRESUMEN
Objective quantification of photoreceptor cell morphology, such as cell diameter and outer segment length, is crucial for early, accurate, and sensitive diagnosis and prognosis of retinal neurodegenerative diseases. Adaptive optics optical coherence tomography (AO-OCT) provides three-dimensional (3-D) visualization of photoreceptor cells in the living human eye. The current gold standard for extracting cell morphology from AO-OCT images involves the tedious process of 2-D manual marking. To automate this process and extend to 3-D analysis of the volumetric data, we propose a comprehensive deep learning framework to segment individual cone cells in AO-OCT scans. Our automated method achieved human-level performance in assessing cone photoreceptors of healthy and diseased participants captured with three different AO-OCT systems representing two different types of point scanning OCT: spectral domain and swept source.
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The Editor-in-Chief and Deputy Editor of Biomedical Optics Express introduce a new prize for the best paper published in the Journal between 2019 and 2021.
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PURPOSE: There is a need to understand physicians' diagnostic uncertainty in the initial management of microbial keratitis (MK). This study aimed to understand corneal specialists' diagnostic uncertainty by establishing risk thresholds for treatment of MK that could be used to inform a decision curve analysis for prediction modeling. METHODS: A cross-sectional survey of corneal specialists with at least 2 years clinical experience was conducted. Clinicians provided the percentage risk at which they would always or never treat MK types (bacterial, fungal, herpetic, and amoebic) based on initial ulcer sizes and locations (<2 mm2 central, <2 mm2 peripheral, and >8 mm2 central). RESULTS: Seventy-two of 99 ophthalmologists participated who were 50% female with an average of 14.7 (SD = 10.1) years of experience, 60% in academic practices, and 38% outside the United States. Clinicians reported they would "never" and "always" treat a <2 mm2 central MK infection if the median risk was 0% and 20% for bacterial (interquartile range, IQR = 0-5 and 5-50), 4.5% and 27.5% for herpetic (IQR = 0-10 and 10-50), 5% and 50% for fungal (IQR = 0-10 and 20-75), and 5% and 50.5% for amoebic (IQR = 0-20 and 32-80), respectively. Mixed-effects models showed lower thresholds to treat larger and central infections (P < 0.001, respectively), and thresholds to always treat differed between MK types for the United States (P < 0.001) but not international clinicians. CONCLUSIONS: Risk thresholds to treat differed by practice locations and MK types, location, and size. Researchers can use these thresholds to understand when a clinician is uncertain and to create decision support tools to guide clinicians' treatment decisions.
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PURPOSE: To investigate baseline mesopic microperimetry (MP) and spectral domain optical coherence tomography (OCT) in the Rate of Progression in USH2A-related Retinal Degeneration (RUSH2A) study. DESIGN: Natural history study METHODS: Setting: 16 clinical sites in Europe and North AmericaStudy Population: Participants with Usher syndrome type 2 (USH2) (N = 80) or autosomal recessive nonsyndromic RP (ARRP) (N = 47) associated with biallelic disease-causing sequence variants in USH2AObservation Procedures: General linear models were used to assess characteristics including disease duration, MP mean sensitivity and OCT intact ellipsoid zone (EZ) area. The associations between mean sensitivity and EZ area with other measures, including best corrected visual acuity (BCVA) and central subfield thickness (CST) within the central 1 mm, were assessed using Spearman correlation coefficients. MAIN OUTCOME MEASURES: Mean sensitivity on MP; EZ area and CST on OCT. RESULTS: All participants (N = 127) had OCT, while MP was obtained at selected sites (N = 93). Participants with Usher syndrome type 2 (USH2, N = 80) and nonsyndromic autosomal recessive Retinitis Pigmentosa (ARRP, N = 47) had the following similar measurements: EZ area (median (interquartile range [IQR]): 1.4 (0.4, 3.1) mm2 vs 2.3 (0.7, 5.7) mm2) and CST (median (IQR): 247 (223, 280) µm vs 261 (246, 288), and mean sensitivity (median (IQR): 3.5 (2.1, 8.4) dB vs 5.1 (2.9, 9.0) dB). Longer disease duration was associated with smaller EZ area (P < 0.001) and lower mean sensitivity (P = 0.01). Better BCVA, larger EZ area, and larger CST were correlated with greater mean sensitivity (r > 0.3 and P < 0.01). Better BCVA and larger CST were associated with larger EZ area (r > 0.6 and P < 0.001). CONCLUSIONS: Longer disease duration correlated with more severe retinal structure and function abnormalities, and there were associations between MP and OCT metrics. Monitoring changes in retinal structure-function relationships during disease progression will provide important insights into disease mechanism in USH2A-related retinal degeneration.
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Degeneración Retiniana , Síndromes de Usher , Humanos , Síndromes de Usher/diagnóstico , Síndromes de Usher/genética , Pruebas del Campo Visual , Tomografía de Coherencia Óptica/métodos , Agudeza Visual , Índice de Severidad de la EnfermedadRESUMEN
OBJECTIVE: The purpose of the study was to perform a post hoc analysis to explore the effect of baseline anatomic characteristics identified on OCT on best-corrected visual acuity (BCVA) responses to risuteganib from the completed phase II study in subjects with dry age-related macular degeneration (AMD). DESIGN: Post hoc analysis of a randomized, double-masked, placebo-controlled, phase II study. SUBJECTS: Eyes with intermediate dry AMD with BCVA between 20/40 and 20/200. Patients with concurrent vision-influencing or macula-obscuring ocular pathologies were excluded. METHODS: Patients were randomized to receive a 1-mg intravitreal risuteganib injection or a sham injection at baseline. A second 1-mg intravitreal injection of risuteganib was given at week 16 to those in the treatment arm. Two independent, masked reading centers evaluated the baseline anatomic characteristics on OCT to explore features associated with positive responses to risuteganib. MAIN OUTCOME MEASURES: Treatment response was defined as a gain of ≥ 8 letters in BCVA from baseline to week 28 in the treatment arm, compared with baseline to week 12 in the sham group. Anatomic parameters, measured by retinal segmentation platforms, including measures of retinal thickness were compared between the responders and nonresponders to risuteganib. RESULTS: Thirty-nine patients completed the study and underwent analysis. In the treatment arm, 48% of eyes demonstrated treatment responses, compared with 7% in the sham group. In the quantitative anatomic assessment, enhanced ellipsoid integrity, greater outer retinal thickness, and decreased geographic atrophy were associated with increased BCVA gains to risuteganib. CONCLUSIONS: This post hoc analysis demonstrated that baseline OCT features may help determine the likelihood of a functional response to risuteganib. The characterization of higher-order OCT features may provide important information regarding biomarkers for treatment response and could facilitate optimized clinical trial enrollment and enrichment.
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Atrofia Geográfica , Degeneración Macular , Humanos , Inhibidores de la Angiogénesis , Angiografía con Fluoresceína , Atrofia Geográfica/diagnóstico , Atrofia Geográfica/tratamiento farmacológico , Degeneración Macular/diagnóstico , Degeneración Macular/tratamiento farmacológico , Ranibizumab , Tomografía de Coherencia Óptica , Factor A de Crecimiento Endotelial Vascular , Agudeza VisualRESUMEN
The new Biomedical Optics Express Editor-in-Chief Ruikang (Ricky) Wang and new Deputy Editor Sina Farsiu share their introductory message as they start their editorial terms on 1 January 2022.
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PURPOSE: To assess the generalizability of a deep learning-based algorithm to segment the ellipsoid zone (EZ). METHODS: The dataset consisted of 127 spectral-domain optical coherence tomography volumes from eyes of participants with USH2A-related retinal degeneration enrolled in the RUSH2A clinical trial (NCT03146078). The EZ was segmented manually by trained readers and automatically by deep OCT atrophy detection, a deep learning-based algorithm originally developed for macular telangiectasia Type 2. Performance was evaluated using the Dice similarity coefficient between the segmentations, and the absolute difference and Pearson's correlation of measurements of interest obtained from the segmentations. RESULTS: With deep OCT atrophy detection, the average (mean ± SD, median) Dice similarity coefficient was 0.79 ± 0.27, 0.90. The average absolute difference in total EZ area was 0.62 ± 1.41, 0.22 mm2 with a correlation of 0.97. The average absolute difference in the maximum EZ length was 222 ± 288, 126 µm with a correlation of 0.97. CONCLUSION: Deep OCT atrophy detection segmented EZ in USH2A-related retinal degeneration with good performance. The algorithm is potentially generalizable to other diseases and other biomarkers of interest as well, which is an important aspect of clinical applicability.
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Aprendizaje Profundo , Degeneración Retiniana , Algoritmos , Atrofia , Proteínas de la Matriz Extracelular/genética , Humanos , Degeneración Retiniana/diagnóstico , Tomografía de Coherencia Óptica/métodos , Agudeza VisualRESUMEN
The purpose of this study was to develop an automatic deep learning-based approach and corresponding free, open-source software to perform segmentation of the Schlemm's canal (SC) lumen in optical coherence tomography (OCT) scans of living mouse eyes. A novel convolutional neural network (CNN) for semantic segmentation grounded in a U-Net architecture was developed by incorporating a late fusion scheme, multi-scale input image pyramid, dilated residual convolution blocks, and attention-gating. 163 pairs of intensity and speckle variance (SV) OCT B-scans acquired from 32 living mouse eyes were used for training, validation, and testing of this CNN model for segmentation of the SC lumen. The proposed model achieved a mean Dice Similarity Coefficient (DSC) of 0.694 ± 0.256 and median DSC of 0.791, while manual segmentation performed by a second expert grader achieved a mean and median DSC of 0.713 ± 0.209 and 0.763, respectively. This work presents the first automatic method for segmentation of the SC lumen in OCT images of living mouse eyes. The performance of the proposed model is comparable to the performance of a second human grader. Open-source automatic software for segmentation of the SC lumen is expected to accelerate experiments for studying treatment efficacy of new drugs affecting intraocular pressure and related diseases such as glaucoma, which present as changes in the SC area.
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Segmento Anterior del Ojo/diagnóstico por imagen , Aprendizaje Profundo , Glaucoma de Ángulo Abierto/diagnóstico por imagen , Esclerótica/diagnóstico por imagen , Tomografía de Coherencia Óptica , Algoritmos , Animales , Glaucoma de Ángulo Abierto/fisiopatología , Presión Intraocular/fisiología , Ratones , Ratones Endogámicos C57BL , Redes Neurales de la Computación , Tonometría OcularRESUMEN
Optical coherence tomography (OCT) has seen widespread success as an in vivo clinical diagnostic 3D imaging modality, impacting areas including ophthalmology, cardiology, and gastroenterology. Despite its many advantages, such as high sensitivity, speed, and depth penetration, OCT suffers from several shortcomings that ultimately limit its utility as a 3D microscopy tool, such as its pervasive coherent speckle noise and poor lateral resolution required to maintain millimeter-scale imaging depths. Here, we present 3D optical coherence refraction tomography (OCRT), a computational extension of OCT which synthesizes an incoherent contrast mechanism by combining multiple OCT volumes, acquired across two rotation axes, to form a resolution-enhanced, speckle-reduced, refraction-corrected 3D reconstruction. Our label-free computational 3D microscope features a novel optical design incorporating a parabolic mirror to enable the capture of 5D plenoptic datasets, consisting of millimetric 3D fields of view over up to ±75° without moving the sample. We demonstrate that 3D OCRT reveals 3D features unobserved by conventional OCT in fruit fly, zebrafish, and mouse samples.