ScLNet: A cornea with scleral lens OCT layers segmentation dataset and new multi-task model

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.

Accurate segmentation of tumor-infected regions in retinal Optical Coherence Tomography (OCT) images is critical for early diagnosis and clinical decision-making. However, conventional deep learning and transformer-based models often struggle to delineate overlapping and inter-dependent pixel features, leading to reduced segmentation precision. This study proposes a novel Dependent Inter-Feature Segmentation Method (DIFSM) to improve the localization and segmentation of retinal tumor regions in OCT images. The proposed DIFSM framework integrates advanced image preprocessing, inter-feature dependency analysis, and a Vision Transformer (ViT) architecture to distinguish differentiable and non-differentiable features. Overlapping pixel regions are identified through inter-feature intensity and gradient analysis, which are indicative of tumor-affected areas. The Vision Transformer is trained using matched and unmatched inter-feature representations to enhance contextual learning and resolve feature ambiguities. Experiments were conducted on the OCTID dataset comprising high-resolution retinal OCT images, and performance was evaluated using Dice coefficient, Intersection over Union (IoU), precision, sensitivity, specificity, and Mean Square Matching Error (MSME). Comparative analysis was performed against state-of-the-art segmentation models. The proposed DIFSM model achieved a Dice coefficient of 96.2% and an IoU of 94.8%, demonstrating excellent spatial overlap with expert-validated tumor regions. Precision, sensitivity, and specificity reached 96.8%, 96.6%, and 96.7%, respectively, while MSME was reduced to 6.11%. Compared to existing methods, DIFSM improved segmentation accuracy by 14.39%, precision by 14.11%, and reduced MSME by 13.5%. The model consistently outperformed benchmark approaches in detecting macular hole and central serous retinopathy-associated tumor regions while maintaining robustness to noise and structural variability. The proposed DIFSM framework effectively addresses the limitations of existing OCT segmentation methods by explicitly modeling inter-feature dependencies and resolving overlapping pixel ambiguities using a Vision Transformer. The significant improvements in segmentation accuracy and error reduction highlight its potential as a reliable and clinically applicable tool for automated retinal tumor detection in ophthalmic imaging. DIFSM offers a promising direction for enhancing OCT-based diagnostic systems and supporting ophthalmologists in early disease identification and treatment planning.

Aim To develop an optimal method for automated segmentation of atherosclerotic plaque structural components in optical coherence tomography (OCT) images using an ensemble of deep learning neural network models based on a comparison of nine artificial neural network architectures.Material and methods This study utilized a multidisciplinary OCT dataset obtained from examinations of 103 patients. Each OCT session was annotated as a set of two-dimensional binary masks corresponding to the pixel boundaries of four key morphological features of plaques: vascular lumen, fibrous cap, lipid core, and microvessels. Nine deep learning models, including U-Net, DeepLabV3, and others, were used to segment the anatomical structures. Model hyperparameters were tuned using Bayesian optimization, and the performance was assessed using the Dice Similarity Coefficient (DSC) metric.Results The models demonstrated high segmentation accuracy for the vascular lumen (DSC: 0.987) and moderate accuracy for the fibrous cap (DSC: 0.736) and lipid core (DSC: 0.751). Microvessel segmentation proved more challenging, with a final DSC accuracy of 61%. A weighted ensemble of models, accounting for the uneven representation of different morphological structures and model confidence, achieved an average DSC of 88.2%, which significantly improved the overall accuracy compared to individual models. This accuracy value exceeds those of all individual models, including the highest DSC values of 0.784 (for microvessels) and 0.751 (for lipid core), indicating a consistent improvement in the segmentation accuracy by integrating the models.Conclusion The study confirms the effectiveness of the proposed strategy for segmentation of atherosclerotic plaques in OCT images. This strategy is based on using specialized models for various morphological features and a weighted ensemble adapted to the uneven representation of different morphological structures and morphological complexity. This approach provided a high segmentation accuracy despite the pronounced inequality in the representation of classes. The study results can contribute to the development of decision support methods in cardiology aimed at improving diagnostic accuracy and treatment of cardiovascular diseases.

Prospective assessment of corneal biomechanical properties and intraocular pressure after scleral lens wear: A 12-month follow-up study

Contact lenses are classified on the basis of the bearing zone area as Corneal, Corneo-scleral and Scleral lenses. Scleral lenses are large in diameter (18 - 23mm) and have a bearing completely on sclera. It has a large tear reservoir which helps in epithelial protection, enhances epithelial healing and enhances vision. Scleral lenses have evolved over the years in the material, design and fitting parameters. The earlier lenses were made of polymethacrylate, with poor oxygen permeability and caused corneal hypoxia. Later, came in the fenestrated scleral lenses. Although they had the advantage of minimal lens suction, they caused corneal exposure to air. The modern scleral contact lenses are made of material with high oxygen permeability and are non-fenestrated. These are called the rigid gas permeable scleral contact lenses. The scleral contact lenses are available in two types. They could be Preformed lenses or Impression technique designed. The Impression technique type, wherein a mold is prepared after impression, are no more in use. The Preformed scleral lenses come with a set of lenses with variable diameters and fitting parameters. Amongst the various parameters used in fitting of scleral contact lenses, the measurement of sagittal height is the most critical for a good fit. The sagittal height measurement has been made accurate with the advent of optical coherence tomography. The lens could be designed as spherical, toric or bifocal. Most common lens design used is spherical scleral contact lenses. Toric scleral lenses could be front toric, back toric or bitoric and used for patients with irregular astigmatism and corneal ectasia. Boston scleral lenses are one of the most popular scleral contact lenses. A highly customized lens for the patient is designed using the topographic guided software and gives the scope of changing lens fitting parameters instantly. The Boston scleral lenses find the maximum application on patients with end stage ocular surface disorders. The growing popularity of scleral contact lenses is due to the encouraging results in various indications. These can be broadly divided into indications for vision improvement (primary ectasia-includes conditions such as keratoconus, keratoglobus and pellucid marginal degeneration and the secondary ectasia group includes post-refractive surgery, including post-laser assisted in-situ keratomileusis, post-laser assisted epithelial keratoplasty, postphotorefractive keratectomy and postradial keratotomy) and indications for corneal protection (Steven Johnson syndrome, graft versus host disease, ocular cicatricial pemphigoid, neurotrophic corneal disease, atopic keratoconjunctivitis, Sjögren's syndrome, persistent epithelial corneal defects). It is important for every ophthalmologist to be aware of the option of treating patients with ocular surface disorders and corneal ectasia with scleral contact lenses. The article describes the various aspects of scleral contact lenses –classification, historical milestones in development, various modern scleral lens designs and fitting parameters. Published literature, indication and results of rigid gas permeable scleral contact lenses is also discussed.

Optical coherence tomography (OCT) is becoming one of the most important detection modalities for fast and noninvasive assessment of ophthalmological diseases. Diabetic macular edema (DME) is one of the important reasons leads to blindness. Its pathological features are mainly manifested in the accumulation of fluid in the retina. An automated method is proposed to identify and quantify the volume of cystoid macular edema in Spectral Domain OCT (SD-OCT) images. In the first stage of preprocessing, we balance the apparent signal-to-noise of each retinal OCT image. Because the signal-to-noise of OCT images is variable from patient to patient, and balance of the signal-to-noise ensures consistent segmentation of cystoid fluid. Speckle noise is the main reason leads to quality degrading in OCT images. The denoising method should be efficient for the noise suppression, and the edge information can be preserved at the same time. Then we used the anisotropic diffusion filter to suppress shot noise. The intensity inhomogeneity in OCT images may lead to false detection in the further segmentation work. Then we used the gamma transformation to change the brightness, which eliminates the effect availably. In the second stage of segmentation, we solve the problem of segmentation effectively by the improved level set method and calculated the area of edema area, which provides quantitative analytic tools for clinical diagnosis and therapy. Finally, the proposed method was evaluated on 15 SD-OCT retinal images from DME adults. Leave-one-out evaluation resulted in a precision, sensitivity and dice similarity coefficient (DSC) of 81.12%, 86.90% and 80.05%, respectively.

Previous studies have demonstrated that optical coherence tomography (OCT) could be used to delineate alterations in the microstructure of cartilage, and have suggested that changes in the polarization state of light as detected by OCT could provide information on the birefringence properties of articular cartilage as influenced by disease. In this study we have used both OCT and polarization sensitive optical coherence tomography (PS-OCT) technologies to evaluate normal and abnormal bovine articular cartilage according to established structural, organizational, and birefringent characteristics of degenerative joint disease (DJD) in order to determine if this technology can be used to differentiate various stages of DJD as a minimally invasive imaging tool. Fresh bovine femoral-tibial joints were obtained from an abattoir, and 45 cartilage specimens were harvested from 8 tibial plateaus. Whole ex vivo specimens of normal and degenerative articular cartilage were imaged by both OCT and PS-OCT, then fixed and processed for histological evaluation. OCT/PS-OCT images and corresponding histology sections of each specimen were scored according to a modified Mankin structural grading scale and compared. OCT and PS-OCT imaging allowed structural evaluation of intact articular cartilage along a 6 mm surface length to a depth of 2 mm with a transverse resolution of 12 microm and an axial resolution of 10 microm. The OCT and PS-OCT images demonstrated characteristic alterations in the structure of articular cartilage with a high correlation to histological evaluation (kappa = 0.776). The OCT images were able to demonstrate early to advanced structural changes of articular cartilage while the optical phase retardation images obtained by PS-OCT imaging were able to discriminate areas where disorganization of the cartilage matrix was present, however, these characteristics are much different than those reported where OCT images alone were used to characterize tissue birefringence. No evidence of differences in OCT or PS-OCT images were detected between specimens of similar structural characteristics where proteoglycan was judged present or absent by safranin-O Fast Green staining. The combined use of OCT and PS-OCT technologies to obtain images from a single system is able to demonstrate and discriminate between characteristics of very early stages of surface irregularities not previously reported for OCT imaging, to deep clefts and collagen matrix disorganization for tissue at depths of up to 2 mm with good correlation to histology. PS-OCT and accumulated optical phase retardation images of articular cartilage as constructed from alterations in Stokes vector parameters appear to give a valuable but different assessment of alterations in tissue birefringence and organization than have been reported for OCT images obtained with the use of polarized or non-polarized light sources. This is the first time that alterations in the polarization state of light reflected from within the tissue have been demonstrated to be consistent with changes observed in the orientation and organization of the collagen matrix in advanced stages of DJD. The degree of phase transformation of light reflected from within the tissue as determined by PS-OCT imaging does not appear to be altered by the presence or absence of proteoglycan.

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.

Scleral Lenses in the Management of Ocular Surface Disease

To assess scleral lens fluid reservoir (FR) change simultaneously in four quadrants with single acquisition using novel ANTERION anterior segment swept-source optical coherence tomography (SS-OCT). A prospective, observational, clinical study of 18 subjects (30 eyes) was performed on adults fitted with a scleral lens for ocular surface disease (n=8), irregular cornea/scar (n=7), and corneal ectasia (n=15). ANTERION anterior segment SS-OCT imaging was obtained at the initial visit and at the follow-up to determine pre and post scleral lens settling, measured in microns, centrally and peripherally. Peripheral measurements were grouped into four quadrants. Repeated-measures ANOVA was performed comparing vault post minus pre differences by quadrant, and TTests comparing difference in FR by lens design were performed with a significant threshold at P <0.05. The mean central scleral lens settling was significant at -48.3±41.7 μm. The change in FR by quadrant was superior (S): -47.8±67.3 μm, inferior (I): -68.0±102.2 μm, nasal (N) -46.3±63.4 μm, and temporal (T): -56.7±49.3 μm. There were no significant differences in lens settling between the quadrants. Within the three categories, the irregular cornea group experienced significantly greater lens settling. There was no significant difference in central FR when comparing lens design or lens diameter. The ANTERION SS-OCT allows for high-resolution central and peripheral assessment of FR in scleral lens wear. With increased technology available for scleral lens customization, this imaging modality can assist in more detailed assessment in quadrant-specific scleral lens designs.

Aims/Purpose: The detection of subretinal fluid (SRF) and intraretinal fluid (IRF) in optical coherence tomography (OCT) images plays a crucial role in the diagnosis and treatment of certain eye diseases. In this study, we investigated the performance of a Transfer Learning artificial intelligence (AI) technique, which we used to perform the model adaptation from the domain of Heidelberg OCT images to Optopol devices.Methods: A data set of 133 Optopol OCT images. 69 images indicating subretinal or intraretinal fluid and 64 fluid‐free images, were analysed. The AI algorithm that was initially trained on OCT images from Heidelberg device was fine‐tuned to perform the biomarker segmentation in Optopol (Revo NX, SOCT Copernicus REVO, REVO FC) OCT images. Manual assessments by the medical experts were used to evaluate the algorithm's performance.Results: Our findings demonstrate the promising performance of the domain‐adapted model. in detecting SRF and IRF, when initially trained on OCT images from a different device. The algorithm achieved an accuracy of 80% while classifying the presence versus absence of the fluids on the balanced test data set.Conclusions: The study demonstrates a successful application of a transfer learning technique for detection of subretinal and intraretinal fluids in OCT images acquired with the Optopol devices, indicating its potential for enhancing eye diagnostics. Further studies utilizing larger data sets, further biomarkers and diverse OCT devices are warranted to validate the algorithm's robustness and generalizability.

Aim: To investigate and compare the presentation of granulomatous lesions in optical coherence tomography (OCT) images and compare this to previous studies of nonmelanoma skin tumors. Methods: Two patients with granulomas, tophi and granuloma annulare (GA), respectively, were photographed digitally, OCT-scanned and biopsied in the said order. Normal skin was OCT-scanned for comparison, but not biopsied. The OCT images from each lesion were compared with their histologic images as well as with OCT images with similar characteristics obtained from nonmelanoma skin tumors. Results: The OCT images of the tophi showed hyperreflective, rounded cloud-like structures in dermis, their upper part sharply delineated by a hyporeflective fringe. The deeper areas appeared blurred. The crystalline structures were delineated by a hyporeflective fringe. OCT images of GA showed two different structures in dermis: a hyporeflective rounded one, and one that was lobulated and wing-like. Conclusion: Granulomatous tissue surrounding urate deposits appeared as a clear hyporeflective fringe surrounding a light, hyperreflective area. The urate crystals appeared as hyperreflective areas, shielding the deeper part of dermis, meaning OCT could only visualize the upper part of the lesions. The lobulated, wing-like structure in GA may resemble diffuse GA or a dense lymphocytic infiltrate as seen on histology. The rounded structure in GA may represent an actual granuloma or either diffuse GA or a dense lymphocytic infiltrate as described above. This case suggests that OCT images granulomatous tissue as absorbent, hyporeflective areas, and urate crystals appear as reflective areas, obscuring the underlying tissue. In GA a new image shape looking like a wing has been found. The frequency, specificity and sensitivity of this new pattern in OCT imaging will require further studies.

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.

Anterior segment optical coherence tomography (OCT) provides a wealth of opportunities for modern contact lens practice. OCT imaging has numerous clinical and research applications related to the tear film, cornea, conjunctiva, sclera and ocular adnexae, in addition to soft, rigid, and hybrid contact lenses. This review summarises the potential use of OCT imaging in modern scleral contact lens practice including initial lens selection, assessing the scleral contact lens fit with respect to the cornea and sclera, and accurately quantifying the ocular response to lens wear. Recent advances in the understanding of anterior segment metrics including scleral thickness, curvature, toricity, and the anatomy of the corneoscleral limbal junction are also discussed.

Recent studies on Parkinson's Disease (PD) discovered potential markers in brain MR images for its diagnosis and staging.One of them is the neuromelanin, which can be found in regions such as the substantia nigra and the locus coeruleus, both located in the brainstem.The analysis of these regions is normally conducted manually by a specialized professional, which can be costly and timeconsuming.Fortunately, there have been advances in automatic segmentation approaches in several areas.In medical imaging, U-Nets are showing state-of-the-art performance, but we did not find many works using this architecture to extract brainstem structures.A likely cause for this is the scarcity of large databases with well annotated segmentation of these areas.Thus, this paper explores the use of a dataset of our research group composed of T1-weighted MR images to train U-Net models with different types of slices to automatically demarcate the brainstem (as a first stage).The analysis starts with a scripted segmentation of the target region using the Freesurfer package.Thereby, we trained 4 models and evaluated them using Dice Similarity Coefficient (DSC) and Intersection over Union (IoU).Three models were trained on single anatomical planes, and one on multi-plane slices.Also, we applied grid search, varying optimizers, numbers of filters in the first layer, and learning rates.The best model trained with the larger subset with the best performance was the axial (DSC: 91.76% and IoU: 86.95%), followed by sagittal (DSC: 90.61% and IoU: 84.11%), coronal (DSC: 89.38% and IoU: 68.76%), and all slices (DSC: 16.36% and IoU: 7.58%).The next steps of our research are to differentiate the midbrain out of the brainstem, considering manual segmentation as ground truth, and test some approaches on determining the threshold and evaluating separately the 2 hemispheres of the midbrain.