Layer Segmentation Research Articles

Multiobjective Evolutionary Superpixel Segmentation for PolSAR Image Classification

Superpixel segmentation has been widely used in the field of computer vision. The generations of PolSAR superpixels have also been widely studied for their feasibility and high efficiency. The initial numbers of PolSAR superpixels are usually designed manually by experience, which has a significant impact on the final performance of superpixel segmentation and the subsequent interpretation tasks. Additionally, the effective information of PolSAR superpixels is not fully analyzed and utilized in the generation process. Regarding these issues, a multiobjective evolutionary superpixel segmentation for PolSAR image classification is proposed in this study. It contains two layers, an automatic optimization layer and a fine segmentation layer. Fully considering the similarity information within the superpixels and the difference information among the superpixels simultaneously, the automatic optimization layer can determine the suitable number of superpixels automatically by the multiobjective optimization for PolSAR superpixel segmentation. Considering the difficulty of the search for accurate boundaries of complex ground objects in PolSAR images, the fine segmentation layer can further improve the qualities of superpixels by fully using the boundary information of good-quality superpixels in the evolution process for generating PolSAR superpixels. The experiments on different PolSAR image datasets validate that the proposed approach can automatically generate high-quality superpixels without any prior information.

Advancing Ocular Imaging: A Hybrid Attention Mechanism-Based U-Net Model for Precise Segmentation of Sub-Retinal Layers in OCT Images.

This paper presents a novel U-Net model incorporating a hybrid attention mechanism for automating the segmentation of sub-retinal layers in Optical Coherence Tomography (OCT) images. OCT is an ophthalmology tool that provides detailed insights into retinal structures. Manual segmentation of these layers is time-consuming and subjective, calling for automated solutions. Our proposed model combines edge and spatial attention mechanisms with the U-Net architecture to improve segmentation accuracy. By leveraging attention mechanisms, the U-Net focuses selectively on image features. Extensive evaluations using datasets demonstrate that our model outperforms existing approaches, making it a valuable tool for medical professionals. The study also highlights the model's robustness through performance metrics such as an average Dice score of 94.99%, Adjusted Rand Index (ARI) of 97.00%, and Strength of Agreement (SOA) classifications like "Almost Perfect", "Excellent", and "Very Strong". This advanced predictive model shows promise in expediting processes and enhancing the precision of ocular imaging in real-world applications.

Assessment of area and structural irregularity of retinal layers in diabetic retinopathy using machine learning and image processing techniques

Diabetes retinopathy prevention necessitates early detection, monitoring, and treatment. Non-invasive optical coherence tomography (OCT) shows structural changes in the retinal layer. OCT image evaluation necessitates retinal layer segmentation. The ability of our automated retinal layer segmentation to distinguish between normal, non-proliferative (NPDR), and proliferative diabetic retinopathy (PDR) was investigated in this study using quantifiable biomarkers such as retina layer smoothness index (SI) and area (S) in horizontal and vertical OCT images for each zone (fovea, superior, inferior, nasal, and temporal). This research includes 84 eyes from 57 individuals. The study shows a significant difference in the Area (S) of inner nuclear layer (INL) and outer nuclear layer (ONL) in the horizontal foveal zone across the three groups (p < 0.001). In the horizontal scan, there is a significant difference in the smoothness index (SI) of the inner plexiform layer (IPL) and the upper border of the outer plexiform layer (OPL) among three groups (p < 0.05). There is also a significant difference in the area (S) of the OPL in the foveal zone among the three groups (p = 0.003). The area (S) of the INL in the foveal region of horizontal slabs performed best for distinguishing diabetic patients (NPDR and PDR) from normal individuals, with an accuracy of 87.6%. The smoothness index (SI) of IPL in the nasal zone of horizontal foveal slabs was the most accurate at 97.2% in distinguishing PDR from NPDR. The smoothness index of the top border of the OPL in the nasal zone of horizontal slabs was 84.1% accurate in distinguishing NPDR from PDR. Smoothness index of IPL in the temporal zone of horizontal slabs was 89.8% accurate in identifying NPDR from PDR patients. In conclusion, optical coherence tomography can assess the smoothness index and irregularity of the inner and outer plexiform layers, particularly in the nasal and temporal regions of horizontal foveal slabs, to distinguish non-proliferative from proliferative diabetic retinopathy. The evolution of diabetic retinopathy throughout severity levels and its effects on retinal layer irregularity need more study.

Segmentation of anatomical layers and imaging artifacts in intravascular polarization sensitive optical coherence tomography using attending physician and boundary cardinality losses.

Intravascular ultrasound and optical coherence tomography are widely available for assessing coronary stenoses and provide critical information to optimize percutaneous coronary intervention. Intravascular polarization-sensitive optical coherence tomography (PS-OCT) measures the polarization state of the light scattered by the vessel wall in addition to conventional cross-sectional images of subsurface microstructure. This affords reconstruction of tissue polarization properties and reveals improved contrast between the layers of the vessel wall along with insight into collagen and smooth muscle content. Here, we propose a convolutional neural network model, optimized using two new loss terms (Boundary Cardinality and Attending Physician), that takes advantage of the additional polarization contrast and classifies the lumen, intima, and media layers in addition to guidewire and plaque shadows. Our model segments the media boundaries through fibrotic plaques and continues to estimate the outer media boundary behind shadows of lipid-rich plaques. We demonstrate that our multi-class classification model outperforms existing methods that exclusively use conventional OCT data, predominantly segment the lumen, and consider subsurface layers at most in regions of minimal disease. Segmentation of all anatomical layers throughout diseased vessels may facilitate stent sizing and will enable automated characterization of plaque polarization properties for investigation of the natural history and significance of coronary atheromas.

Clothing Style Similarity Matching and Classification Recognition Algorithm Based on Double-Layer Model in the Context of the Internet of Things

Abstract The identification of different clothing style attributes is helpful for designers to grasp the clothing style, and it is also helpful for consumers to find clothes with the same style attributes according to their own preferences. This is becoming more important in the clothing design, Internet, and e-commerce industries. It is of great significance to carry out clothing style similarity matching and classification recognition. For the problem of clothing style similarity matching and classification recognition, the traditional algorithm stays in the stage of qualitative analysis and subjective evaluation, unable to quantitatively and objectively determine clothing style, resulting in the decline of clothing style similarity matching effect and classification recognition accuracy. Therefore, a similarity matching, classification, and recognition algorithm of clothing style based on the double-layer model in the context of the Internet of Things is designed. The double-layer model is constructed through the target detection layer and target segmentation layer. The double-layer model is used to realize the segmentation of clothing image. The overall similarity evaluation index of image style is obtained according to the image gradient to complete the similarity matching of clothing style. On this basis, the clothing image features are extracted and input into the support vector machine classifier to complete the clothing classification and recognition. The experimental results show that the proposed garment style similarity matching and classification recognition algorithm based on the two-layer model has high image segmentation accuracy, good style similarity matching effect, high classification recognition accuracy, and high efficiency, which proves that the algorithm is feasible for garment style similarity matching and classification recognition using the two-layer model and can be further applied in the field of garment design.

Deep Learning-Based Retinal Layer Segmentation in Optical Coherence Tomography Scans of Patients with Inherited Retinal Diseases.

In optical coherence tomography (OCT) scans of patients with inherited retinal diseases (IRDs), the measurement of the thickness of the outer nuclear layer (ONL) has been well established as a surrogate marker for photoreceptor preservation. Current automatic segmentation tools fail in OCT segmentation in IRDs, and manual segmentation is time-consuming. Patients with IRD and an available OCT scan were screened for the present study. Additionally, OCT scans of patients without retinal disease were included to provide training data for artificial intelligence (AI). We trained a U-net-based model on healthy patients and applied a domain adaption technique to the IRD patients' scans. We established an AI-based image segmentation algorithm that reliably segments the ONL in OCT scans of IRD patients. In a test dataset, the dice score of the algorithm was 98.7%. Furthermore, we generated thickness maps of the full retinal thickness and the ONL layer for each patient. Accurate segmentation of anatomical layers on OCT scans plays a crucial role for predictive models linking retinal structure to visual function. Our algorithm for segmentation of OCT images could provide the basis for further studies on IRDs.

Self-attention CNN for retinal layer segmentation in OCT.

The structure of the retinal layers provides valuable diagnostic information for many ophthalmic diseases. Optical coherence tomography (OCT) obtains cross-sectional images of the retina, which reveals information about the retinal layers. The U-net based approaches are prominent in retinal layering methods, which are usually beneficial to local characteristics but not good at obtaining long-distance dependence for contextual information. Furthermore, the morphology of retinal layers with the disease is more complex, which brings more significant challenges to the task of retinal layer segmentation. We propose a U-shaped network combining an encoder-decoder architecture and self-attention mechanisms. In response to the characteristics of retinal OCT cross-sectional images, a self-attentive module in the vertical direction is added to the bottom of the U-shaped network, and an attention mechanism is also added in skip connection and up-sampling to enhance essential features. In this method, the transformer's self-attentive mechanism obtains the global field of perception, thus providing the missing context information for convolutions, and the convolutional neural network also efficiently extracts local features, compensating the local details the transformer ignores. The experiment results showed that our method is accurate and better than other methods for segmentation of the retinal layers, with the average Dice scores of 0.871 and 0.820, respectively, on two public retinal OCT image datasets. To perform the layer segmentation of retinal OCT image better, the proposed method incorporates the transformer's self-attention mechanism in a U-shaped network, which is helpful for ophthalmic disease diagnosis.

Radiation effects on retinal layers revealed by OCT, OCT-A, and perimetry as a function of dose and time from treatment

Optical coherence tomography (OCT) has become a key method for diagnosing and staging radiation retinopathy, based mainly on the presence of fluid in the central macula. A robust retinal layer segmentation method is required for identification of the specific layers involved in radiation-induced pathology in individual eyes over time, in order to determine damage driven by radiation injury to the microvessels and to the inner retinal neurons. Here, we utilized OCT, OCT-angiography, visual field testing, and patient-specific dosimetry models to analyze abnormal retinal layer thickening and thinning relative to microvessel density, visual function, radiation dose, and time from radiotherapy in a cross-sectional cohort of uveal melanoma patients treated with 125I-plaque brachytherapy. Within the first 24 months of radiotherapy, we show differential thickening and thinning of the two inner retinal layers, suggestive of microvessel leakage and neurodegeneration, mostly favoring thickening. Four out of 13 eyes showed decreased inner retinal capillary density associated with a corresponding normal inner retinal thickness, indicating early microvascular pathology. Two eyes showed the opposite: significant inner retinal layer thinning and normal capillary density, indicating early neuronal damage preceding a decrease in capillary density. At later time points, inner retinal thinning becomes the dominant pathology and correlates significantly with decreased vascularity, vision loss, and dose to the optic nerve. Stable multiple retinal layer segmentation provided by 3D graph-based methods aids in assessing the microvascular and neuronal response to radiation, information needed to target therapeutics for radiation retinopathy and vision loss.

Changes in optical coherence tomography biomarkers in eyes with advanced idiopathic epiretinal membrane treated with dexamethasone implantation

PurposeTo investigate the effects of vitrectomy and intravitreal dexamethasone (DEX) implantation on retinal biomarkers in patients with advanced idiopathic epiretinal membrane (IERM) and to evaluate this treatment’s anatomical and functional outcomes.MethodsThis retrospective study included 41 patients with advanced IERM who underwent vitrectomy and were divided into a pars plana vitrectomy (PPV) group (20 eyes) and a dexamethasone (DEX) group (21 eyes) based on intravitreal DEX implantation. We collected data on best-corrected visual acuity (BCVA), central macular thickness (CMT), disorganization of the retinal inner layers (DRIL), subretinal fluid, intraretinal cystoid changes (IRC), integrity of the inner-outer segment layer, and intraocular pressure.ResultsBCVA improved significantly in both groups; the DEX group had a higher visual acuity gain at 1 and 6 months (P = 0.002 and 0.023, respectively). Postoperative CMT gradually decreased in both groups, with the DEX group showing a greater decrease at 1 and 6 months (P = 0.009 and 0.033, respectively). Six months after surgery, the DRIL and IRC grades in the DEX group were significantly improved compared to those in the PPV group (P = 0.037 and 0.038, respectively). Multivariate regression analyses revealed that patients with intraoperative DEX implants were more likely to have a significant CMT reduction (≥ 100 μm) from baseline (odds ratio (OR), 9.44; 95% confidence intervals (CI), 1.58–56.56; P = 0.014) at 6 months and less likely to exhibit DRIL at 6 months postoperatively (OR, 0.08; 95% CI, 0.01–0.68; P = 0.021).ConclusionVitrectomy combined with intravitreal DEX implantation facilitates the recovery of postoperative visual acuity and improvement of anatomical outcomes in patients with advanced IERM, effectively reducing CMT and improving DRIL.

Retinal Layer Segmentation in OCT Images With Boundary Regression and Feature Polarization.

The geometry of retinal layers is an important imaging feature for the diagnosis of some ophthalmic diseases. In recent years, retinal layer segmentation methods for optical coherence tomography (OCT) images have emerged one after another, and huge progress has been achieved. However, challenges due to interference factors such as noise, blurring, fundus effusion, and tissue artifacts remain in existing methods, primarily manifesting as intra-layer false positives and inter-layer boundary deviation. To solve these problems, we propose a method called Tightly combined Cross-Convolution and Transformer with Boundary regression and feature Polarization (TCCT-BP). This method uses a hybrid architecture of CNN and lightweight Transformer to improve the perception of retinal layers. In addition, a feature grouping and sampling method and the corresponding polarization loss function are designed to maximize the differentiation of the feature vectors of different retinal layers, and a boundary regression loss function is devised to constrain the retinal boundary distribution for a better fit to the ground truth. Extensive experiments on four benchmark datasets demonstrate that the proposed method achieves state-of-the-art performance in dealing with problems of false positives and boundary distortion. The proposed method ranked first in the OCT Layer Segmentation task of GOALS challenge held by MICCAI 2022. The source code is available at https://www.github.com/tyb311/TCCT.

Early inner plexiform layer thinning and retinal nerve fiber layer thickening in excitotoxic retinal injury using deep learning-assisted optical coherence tomography

Excitotoxicity from the impairment of glutamate uptake constitutes an important mechanism in neurodegenerative diseases such as Alzheimer’s, multiple sclerosis, and Parkinson's disease. Within the eye, excitotoxicity is thought to play a critical role in retinal ganglion cell death in glaucoma, diabetic retinopathy, retinal ischemia, and optic nerve injury, yet how excitotoxic injury impacts different retinal layers is not well understood. Here, we investigated the longitudinal effects of N-methyl-D-aspartate (NMDA)-induced excitotoxic retinal injury in a rat model using deep learning-assisted retinal layer thickness estimation. Before and after unilateral intravitreal NMDA injection in nine adult Long Evans rats, spectral-domain optical coherence tomography (OCT) was used to acquire volumetric retinal images in both eyes over 4 weeks. Ten retinal layers were automatically segmented from the OCT data using our deep learning-based algorithm. Retinal degeneration was evaluated using layer-specific retinal thickness changes at each time point (before, and at 3, 7, and 28 days after NMDA injection). Within the inner retina, our OCT results showed that retinal thinning occurred first in the inner plexiform layer at 3 days after NMDA injection, followed by the inner nuclear layer at 7 days post-injury. In contrast, the retinal nerve fiber layer exhibited an initial thickening 3 days after NMDA injection, followed by normalization and thinning up to 4 weeks post-injury. Our results demonstrated the pathological cascades of NMDA-induced neurotoxicity across different layers of the retina. The early inner plexiform layer thinning suggests early dendritic shrinkage, whereas the initial retinal nerve fiber layer thickening before subsequent normalization and thinning indicates early inflammation before axonal loss and cell death. These findings implicate the inner plexiform layer as an early imaging biomarker of excitotoxic retinal degeneration, whereas caution is warranted when interpreting the ganglion cell complex combining retinal nerve fiber layer, ganglion cell layer, and inner plexiform layer thicknesses in conventional OCT measures. Deep learning-assisted retinal layer segmentation and longitudinal OCT monitoring can help evaluate the different phases of retinal layer damage upon excitotoxicity.

Non-invasive quantification of corneal vascularization using anterior segment optical coherence tomography angiography

The presence of corneal vascularization (CV) interferes with the angiogenic and immune privilege of the cornea, risking rejection in eyes following keratoplasty. Pre-operative (lymph)-angioregression is a promising therapeutic approach, but objective monitoring by non-invasive CV imaging is needed. The purpose of this study was to investigate anterior-segment optical coherence tomography angiography (AS-OCTA) for CV visualization and quantification, and to show its superiority over slit-lamp photography in high-risk eyes scheduled for keratoplasty. This institutional pilot study included 29 eyes of 26 patients (51 ± 16 years, 8 female) with significant CV scheduled for keratoplasty that were imaged by slit-lamp photography (Zeiss SL 800) and AS-OCTA (Zeiss Plex Elite 9000). After manual corneal layer segmentation correction, CV maximum/relative depth was measured with the inbuilt software. Slit-lamp photographs and AS-OCTA images were compared for visualization of vascular details. Angiotool software allowed a semi-automated determination of CV-related parameters in the vascular complex of AS-OCTA images. The predominant causes of CV were the herpes simplex virus keratitis (n = 7) and chemical burn (n = 4). Visualization of vascular morphology in AS-OCTA was superior to slit-lamp photography in all except one eye. Vascular metrics including total vessel length, number of junctions/endpoints, junction density, lacunarity, and vessel area/density were defined using Angiotool, with CV depth localization despite scarring and opacification. AS-OCTA proved effective for angioregressive treatment monitoring. AS-OCTA enables non-invasive and objective three-dimensional visualization of corneal vascularization superior to slit-lamp photography, and could be a precious tool for monitoring angioregressive preconditioning prior to keratoplasty.

Automatic segmentation of layers in chorio-retinal complex using Graph-based method for ultra-speed 1.7MHz wide field swept source FDML optical coherence tomography.

The posterior segment of the human eye complex contains two discrete microstructure and vasculature network systems, namely, the retina and choroid. We present a single segmentation framework technique for segmenting the entire layers present in the chorio-retinal complex of the human eye using optical coherence tomography (OCT) images. This automatic program is based on the graph theory method. This single program is capable of segmenting seven layers of the retina and choroid scleral interface. The graph theory was utilized to find the probability matrix and subsequent boundaries of different layers. The program was also implemented to segment angiographic maps of different chorio-retinal layers using "segmentation matrices." The method was tested and successfully validated on OCT images from six normal human eyes as well as eyes with non-exudative age-related macular degeneration (AMD). The thickness of microstructure and microvasculature for different layers located in the chorio-retinal segment of the eye was also generated and compared. A decent efficiency in terms of processing time, sensitivity, and accuracy was observed compared to the manual segmentation and other existing methods. The proposed method automatically segments whole OCT images of chorio-retinal complex with augmented probability maps generation in OCT volume dataset. We have also evaluated the segmentation results using quantitative metrics such as Dice coefficient and Hausdorff distance This method realizes a mean descent Dice similarity coefficient (DSC) value of 0.82 (range, 0.816-0.864) for RPE and CSI layer.

A review of retinal analysis by OCT in murine models Alzheimer's disease

Aims/Purpose: The use of murine models of Alzheimer's disease (AD) in the study of its pathophysiology is essential. In addition to a histological study of the retina, optical coherence tomography (OCT) allows a cost‐effective in vivo analysis of the retina. However, there is no standardization in the use of OCT in murine models of AD, leading to significant variations in study results. A review of the literature on murine models of AD analysed by OCT has been carried out. The characteristics of the animal model, the types of OCT, the retinal sectors analysed, the segmentation layers and the software used for segmentation were examined.Methods: We performed a literature search up to April 2023 using “MESH” terms in PubMed. The terms used were: “Alzheimer's mouse model”, “Optical Coherence Tomography”, “retinal optical coherence tomography” … as well as their combinations. We filter the articles by author criteria: the terms had to be in the title, in the abstract or in the article and they should be written in English or Spanish. Finally, 76 articles were used to the manuscript.Results: In comparison to studies using OCT in AD patients, only fourteen studies have utilized this technique to examine the retinas of murine AD models. Six different models, both first and second generation, were analysed, with most featuring presenilin mutations. There is no consensus regarding the measuring instrument, retinal area analysed, segmentation techniques, or software employed for analysis. Although some authors used the same OCT device, variations in other parameters hinder direct result comparison.Conclusions: It is essential to establish a unified criteria for OCT analysis of the retina in murine models of AD. This would allow the comparison of results and the establishment of measurement protocols. The use of OCT to understand the pathophysiology of AD would facilitate the longitudinal analysis of the disease and the possible identification of biomarkers.

Development of segmented titanium oxynitride layer with spinform surfaces exhibiting antibacterial properties

Development of segmented titanium oxynitride layer with spinform surfaces exhibiting antibacterial properties

A Hierarchical Bayesian Model for Cyber-Human Assessment of Movement in Upper Extremity Stroke Rehabilitation.

The evidence-based quantification of the relation between changes in movement quality and functionality can assist clinicians in achieving more effective structuring or adapting of therapy. In this paper, clinicians rated task, segment, and composite movement feature performance for 478 videos of stroke survivors executing upper extremity therapy tasks. We used the clinician ratings to develop a Hierarchical Bayesian Model (HBM) with task, segment, and composite layers for computing the statistical relation of movement quality changes to function. The model was enhanced through a detailed correlation graph (ΔHBM) that links computationally extracted kinematics with clinician-rated composite features for different task-segment combinations. Utilizing the weights and correlation graphs, we finally derive reverse cascading probabilities of the proposed HBM from kinematics to composite features, segments, and tasks. In a test involving 98 cases where clinician ratings differed, the HBM resolved 95% of these discrepancies. The model effectively aligned kinematic data with specific task-segment combinations in over 90% of cases. Once the HBM is expanded and refined through additional data it can be used for the automated calculation of statistical relations between changes in kinematics and performance of functional tasks and the generation of therapy assessment recommendations for clinicians. While our work primarily focuses on the upper extremities of stroke survivors, the HBM can be adapted to many other neurorehabilitation contexts.

Diverse Data Generation for Retinal Layer Segmentation With Potential Structure Modeling.

Accurate retinal layer segmentation on optical coherence tomography (OCT) images is hampered by the challenges of collecting OCT images with diverse pathological characterization and balanced distribution. Current generative models can produce high-realistic images and corresponding labels without quantitative limitations by fitting distributions of real collected data. Nevertheless, the diversity of their generated data is still limited due to the inherent imbalance of training data. To address these issues, we propose an image-label pair generation framework that generates diverse and balanced potential data from imbalanced real samples. Specifically, the framework first generates diverse layer masks, and then generates plausible OCT images corresponding to these layer masks using two customized diffusion probabilistic models respectively. To learn from imbalanced data and facilitate balanced generation, we introduce pathological-related conditions to guide the generation processes. To enhance the diversity of the generated image-label pairs, we propose a potential structure modeling technique that transfers the knowledge of diverse sub-structures from lowly- or non-pathological samples to highly pathological samples. We conducted extensive experiments on two public datasets for retinal layer segmentation. Firstly, our method generates OCT images with higher image quality and diversity compared to other generative methods. Furthermore, based on the extensive training with the generated OCT images, downstream retinal layer segmentation tasks demonstrate improved results. The code is publicly available at: https://github.com/nicetomeetu21/GenPSM.

Self-Guided Adversarial Network for Domain Adaptive Retinal Layer Segmentation

Self-Guided Adversarial Network for Domain Adaptive Retinal Layer Segmentation

Effect of systemic sclerosis on macular anatomy

Aims/Purpose: Systemic sclerosis, also known as scleroderma, is a rare autoimmune disease that involves the hardening of the skin and also hinders the blood supply by occluding the small arteries and capillaries that carry blood to the tissues. Our aim was to compare the retinal thickness of different segmentations at the macula.Methods: In this pilot study 22 eyes from subjects with systemic sclerosis were compared with 22 eyes from healthy subjects (controls). All participants underwent a complete ophthalmological study and optical coherence tomography (OCT) measurements (Spectralis, Heidelberg Engineering, Germany) including complete retina, inner and outer layers, intermediate retinal layers (inner nuclear layer + outer plexiform layer + outer nuclear layer) and ganglion cell complex thickness (macular retinal nerve fibre layer + ganglion cell layer + inner plexiform layer) at the macula using Early Treatment Diabetic Retinopathy Study (ETDRS) grid so foveal, parafoveal and perifoveal subfields were measured. The values of the thickness of each segmentation in relation to the thickness of the complete retina (considered layer segmentation thickness divided by complete retina thickness) were also compared.Results: Best‐corrected visual acuity was lower in systemic sclerosis group but age, gender, spherical equivalent, intraocular pressure and axial length did not show any differences between both groups. Diffuse thinning in parafovea of complete retina, inner retina, outer retina, intermediate retina and ganglion cell complex was found in systemic sclerosis group. Fovea is also thinner in complete retina and intermediate retina in systemic sclerosis group. When considering the comparisons of the results in relation to complete retina thickness no differences were found between groups.Conclusions: Systemic sclerosis induces the thinning specially in the parafovea of all the considered macular segmentations alike.

Lightweight Retinal Layer Segmentation With Global Reasoning

Lightweight Retinal Layer Segmentation With Global Reasoning