Depth Images Research Articles

DEGAN: Detail-Enhanced Generative Adversarial Network for Monocular Depth-Based 3D Reconstruction

Although deep networks-based 3D reconstruction methods can recover the 3D geometry given few inputs, they may produce unfaithful reconstruction when predicting occluded parts of 3D objects. To address the issue, we propose Detail-Enhanced Generative Adversarial Network (DEGAN) which consists of Encoder–Decoder-Based Generator (EDGen) and Voxel-Point Embedding Network-Based Discriminator (VPDis) for 3D reconstruction from a monocular depth image of an object. Firstly, EDGen decodes the features from the 2.5D voxel grid representation of an input depth image and generates the 3D occupancy grid under GAN losses and a sampling point loss. The sampling loss can improve the accuracy of predicted points with high uncertainty. VPDis helps reconstruct the details under voxel and point adversarial losses, respectively. Experimental results show that DEGAN not only outperforms several state-of-the-art methods on both public ModelNet and ShapeNet datasets but also predicts more reliable occluded/missing parts of 3D objects.

Multimodal Data Fusion for Precise Lettuce Phenotype Estimation Using Deep Learning Algorithms

Effective lettuce cultivation requires precise monitoring of growth characteristics, quality assessment, and optimal harvest timing. In a recent study, a deep learning model based on multimodal data fusion was developed to estimate lettuce phenotypic traits accurately. A dual-modal network combining RGB and depth images was designed using an open lettuce dataset. The network incorporated both a feature correction module and a feature fusion module, significantly enhancing the performance in object detection, segmentation, and trait estimation. The model demonstrated high accuracy in estimating key traits, including fresh weight (fw), dry weight (dw), plant height (h), canopy diameter (d), and leaf area (la), achieving an R2 of 0.9732 for fresh weight. Robustness and accuracy were further validated through 5-fold cross-validation, offering a promising approach for future crop phenotyping.

Crack Depth Evaluation and Imaging Using Lamb Wavefield Measurements by a Movable PZT Sensor

Crack Depth Evaluation and Imaging Using Lamb Wavefield Measurements by a Movable PZT Sensor

Environmental Cationic Dye/Porous Silica Nanospheres for Printed Cotton Fabrics with Enhanced Coloration Performance.

The wastewater of printing and dyeing is difficult to treat due to the degradation resistance of dye and the use of massive chemicals, causing a threat to the ecosystem. Pigment printing of fabrics possesses great advantages like high efficiency and flexible production, but there are some challenges like the risk of color depth and hand feeling due to the large size of the pigment and poor adsorption of light. In order to improve the coloration ability of pigment, herein, a novel kind of cationic dye/porous silica nanospheres were prepared through the adsorption of methylene blue and rhodamine B onto electronegative porous silica nanospheres and applied in printing on woven cotton fabric. It was found that the nanospheres exhibited an average diameter, good color depth, hand feeling, and high image quality for pigment preparation. In comparison with dye-printed fabrics, fabrics with dye/WHMS show higher color depth, resulting from the lower visible light reflectance. Notably, the strong interaction between dyes and hydrophilic porous silica nanospheres (WHMS) facilitates the improvement of absorption efficiency and stability. Meanwhile, the printed fabrics with dye/WHMS demonstrate splendid color fastness. The dry/wet rubbing fastnesses are 4 and 3-4, respectively. Furthermore, dye/WHMS-printed cotton fabrics possess a satisfactory hand feeling and breathability. The consumptions of dye wastewater were reduced, and the printing process was simplified. We think that this work may provide a novel insight into printed textiles with enhanced color effects and alleviate the environmental impact of conventional textile coloration.

Subfoveal Choroidal Thickness in Patients with Histiocytosis and Multimodal Imaging Features of Choroidal Infiltrates.

To evaluate choroidal findings in patients with histiocytosis including subfoveal choroidal thickness (SFCT), and multimodal imaging in eyes with choroidal infiltrates visible by ophthalmoscopy; and to determine if abnormalities change with histiocytosis-directed (kinase inhibitor) therapy. 91 patients with histiocytosis and 41 age- and gender-matched controls. Retrospective comparative study at single tertiary cancer referral center. Clinical exam, fundus photography and OCT were used to assess choroidal findings. Clinically evident choroidal infiltrates by ophthalmoscopy were recorded and choroidal vascular architecture was qualitatively examined. SFCT and was measured using enhanced depth imaging spectral domain optical coherence tomography (EDI SD-OCT) from the outer portion of Bruch's membrane to the choroidal scleral interface. SFCT compared to matched controls; secondary outcome was change in SFCT on histiocytosis-directed (kinase inhibitor) therapy. Multimodal imaging of choroidal infiltrates visible by ophthalmoscopy. One hundred and eighty two eyes of 91 patients (46 males, 45 females) with histiocytiosis (Erdheim-Chester 35, Rosai-Dorfman 21, Xanthogranuloma 7, Mixed histiocytosis 11, Langerhans cell histiocytosis 15 and other 2) were examined. In histiocytosis patients, the mean SFCT was 336.2 +/- 94.9 μm compared with 250.3 +/- 60.7μm in the control group (p<0.0001). 69% of histiocystosis patients had SFCT > 275μm compared to 27% in controls (p< 0.0001). Subtype of histiocytosis, sites of bone or central nervous disease, posterior segment/other sites of ophthalmic disease, or mutational profile did not correlate with SFCT. In a subgroup analysis of 35 patients naïve to prior treatment, with > 6 mos follow-up, the proportion of SFCT > 275 μm significantly decreased (p-value = 0.0016) on histiocytosis-directed (kinase inhibitor) therapy. 19.8% of patients had clinically evident choroidal infiltration: majority were yellow creamy, geographic, located posteriorly and hyperautofluorescent; with enlarged Haller's vein bordering the infiltrate, choriocapillaris compression and loss of choroidal architecture by OCT. In this cohort, 19.8% of histiocytosis patients had clinically evident infiltration of their choroid. Furthermore, the majority of patients with histiocytosis had increased subfoveal choroidal thickness compared with age- and gender-matched controls. The thickened choroid decreases on histiocytosis-directed (kinase inhibitor) therapy and may be a marker of response to systemic treatment.

Multi-View Fusion-Based Automated Full-Posture Cattle Body Size Measurement

Cattle farming is an important part of the global livestock industry, and cattle body size is the key indicator of livestock growth. However, traditional manual methods for measuring body sizes are not only time-consuming and labor-intensive but also incur significant costs. Meanwhile, automatic measurement techniques are prone to being affected by environmental conditions and the standing postures of livestock. To overcome these challenges, this study proposes a multi-view fusion-driven automatic measurement system for full-attitude cattle body measurements. Outdoors in natural light, three Zed2 cameras were installed covering different views of the channel. Multiple images, including RGB images, depth images, and point clouds, were automatically acquired from multiple views using the YOLOv8n algorithm. The point clouds from different views undergo multiple denoising to become local point clouds of the cattle body. The local point clouds are coarsely and finely aligned to become a complete point cloud of the cattle body. After detecting the 2D key points on the RGB image created by the YOLOv8x-pose algorithm, the 2D key points are mapped onto the 3D cattle body by combining the internal parameters of the camera and the depth values of the corresponding pixels of the depth map. Based on the mapped 3D key points, the body sizes of cows in different poses are automatically measured, including height, length, abdominal circumference, and chest circumference. In addition, support vector machines and Bézier curves are employed to rectify the missing and deformed circumference body sizes caused by environmental effects. The automatic body measurement system measured the height, length, abdominal circumference, and chest circumference of 47 Huaxi Beef Cattle, a breed native to China, and compared the results with manual measurements. The average relative errors were 2.32%, 2.27%, 3.67%, and 5.22%, respectively, when compared with manual measurements, demonstrating the feasibility and accuracy of the system.

Quantifying choroidal and retinal thicknesses variations via optical coherence tomography in different stages of pediatric keratoconus

PurposeKeratoconus (KCN) is characterized by corneal thinning and bulging, leading to vision impairment. Assessing choroidal thickness (CT) in pediatric KCN ( pKCN) patients can provide insights for better understanding and managing the disease. CT may serve as a potential indicator of disease activity in KCN patients. This study aims to evaluate CT and retinal thickness (RT) in different stages of pKCN patients and compare the findings with an age-matched control group.Methods and patientsThis cross-sectional study included patients under 18 years old who met specific criteria. CT and RT was measured in all subjects using an optical coherence tomography device (Spectralis OCT, version 6.0, Heidelberg Engineering, Germany) with enhanced depth imaging mode, without pupil dilation. Mean CT and RT in pKCN was compared with healthy subjects and assessed among different disease grades using the ABCD grading method.ResultsThe study included 125 eyes (66 patients) in the pKCN group and 42 control eyes (21 individuals). Grade 2 KCN showed the highest prevalence at 26.4% (N = 33), while grades 3 and 1 had prevalence rates of 24% (N = 30). CT in all specified areas (all P-values < 0.001), as well as RT in the subfoveal area (P-value < 0.001) and 1500 μm nasal to the fovea (P-value = 0.025), were significantly greater in the pKCN group compared to controls. Furthermore, CT and RT differed significantly among the pKCN grades (P-values < 0.001).ConclusionCT was found to be elevated in pKCN patients, similar to adult KCN cases. CT could potentially serve as a clinical marker for disease activity in pKCN; however, further studies are needed.

Association of optic disc drusen and small scleral canals - A systematic review and meta-analysis.

The pathophysiology of optic disc drusen (ODD) has long been discussed. According to one leading theory, they develop from calcified mitochondria extruded from axons compressed by an unusually small scleral canal. To examine this hypothesis, we conducted a systematic review and meta-analysis evaluating the scleral canal size in patients with ODD (PO) in comparison to healthy subjects (HS). We searched MEDLINE, Cochrane Central, EMBASE and gray literature to identify relevant articles. A subgroup analysis compared patients with buried ODD (POb) and patients with visible ODD (POv). The study was registered with the International Prospective Register of Systematic Reviews on December 9th, 2022 (Registration: CRD42022375110). We included fifteen articles in the review and fourteen in the meta-analysis. The mean diameter of the scleral canal computed using both fundus photography (DF) and spectral-domain with enhanced depth imaging optical coherence tomography (DO-EDI) was significantly smaller in PO compared to HS (standardized mean difference -1.21 [-1.85 to -0.56] and -0.66 [-0.94 to -0.37] respectively). DO-EDI, but not DF, was higher in POv as compared to POb. When including all-generation OCT in the analysis, the difference between POv and POb, but not between PO and HS, remained. Several intertwined hypotheses can explain these subgroup and sensitivity results: an enlargement of the canal as the ODD grow, an enlargement with time, or a measurement bias (artefactual enlargement of the canal due to a posterior shadow on OCT). In conclusion, this review and meta-analysis further supports the role of a small scleral canal in the development of ODD.

A novel multimodal image feature fusion mechanism:Application to rabbit liveweight estimation in commercial farms

In the realm of commercial rabbit farming, assessment of liveweight for each rabbit is crucial for production management. Traditional manual weighing methods are labor-intensive, time-consuming, and can induce stress and disease transmission. To solve this problem, this paper introduced a liveweight estimation method for rabbit utilizing near-infrared and depth image, including a segmentation stage and a weight estimation stage. Specifically, a dual-stream feature fusion mechanism was proposed to effectively integrate information from both near-infrared and depth imaging. The network was trained and validated on a dataset of 1,957 overhead images from 300 rabbits collected from a commercial farm, achieving superior performance with an R-Square (R2) of 0.95, Root Mean Square Error (RMSE) of 194.95 g, Mean Absolute Deviation (MAD) of 155.10 g, Mean Absolute Percentage Error (MAPE) of 4.08%, and Mean Coefficient of Variation (MCV) of 3.03%. Ablation experiments revealed that employing the proposed dual-stream feature fusion mechanism led to reductions of 52.87g, 38.19g, 1.01%, and 0.75% in RMSE, MAD, MAPE, and MCV, respectively, compared to not employing the mechanism. Compared to machine learning method, our proposed approach demonstrates superior performance in RMSE, MAD, MAPE, and MCV, achieving more accurate liveweight estimation for large-scale commercial farms.

MPCTrans: Multi-Perspective Cue-Aware Joint Relationship Representation for 3D Hand Pose Estimation via Swin Transformer.

The objective of 3D hand pose estimation (HPE) based on depth images is to accurately locate and predict keypoints of the hand. However, this task remains challenging because of the variations in hand appearance from different viewpoints and severe occlusions. To effectively address these challenges, this study introduces a novel approach, called the multi-perspective cue-aware joint relationship representation for 3D HPE via the Swin Transformer (MPCTrans, for short). This approach is designed to learn multi-perspective cues and essential information from hand depth images. To achieve this goal, three novel modules are proposed to utilize features from multiple virtual views of the hand, namely, the adaptive virtual multi-viewpoint (AVM), hierarchy feature estimation (HFE), and virtual viewpoint evaluation (VVE) modules. The AVM module adaptively adjusts the angles of the virtual viewpoint and learns the ideal virtual viewpoint to generate informative multiple virtual views. The HFE module estimates hand keypoints through hierarchical feature extraction. The VVE module evaluates virtual viewpoints by using chained high-level functions from the HFE module. Transformer is used as a backbone to extract the long-range semantic joint relationships in hand depth images. Extensive experiments demonstrate that the MPCTrans model achieves state-of-the-art performance on four challenging benchmark datasets.

Feature fusion and context interaction for RGB-D indoor semantic segmentation

Depth information in indoor scenes is crucial to understanding the spatial and occluding relationships of objects. Depth information contains rich geometric details and is unaffected by lighting, texture, and color, significantly enhancing traditional color images. RGB images and depth images have been widely applied in various image analysis. However, effectively leveraging the complementary information from both modalities remains a challenge. To address this issue, we propose a unified and effective feature fusion and context interaction network (FCINet). It includes three modules. Firstly, we construct a multi-modal feature fusion module (MFFM), which can achieve the aggregation of two modalities along spatial and channel dimensions. Secondly, we construct a global and local information interaction context module (GLCM) to encode rich semantic information. Finally, we construct a feature alignment and fusion module (FAFM), which integrates upsampled low-level features with high-level features to mitigate spatial information loss. Experimental results indicate that the model achieved favorable outcomes on both the NYU Depth v2 dataset and the more complex SUN RGB-D dataset.

DDNet: Depth Dominant Network for Semantic Segmentation of RGB-D Images.

Convolutional neural networks (CNNs) have been widely applied to parse indoor scenes and segment objects represented by color images. Nonetheless, the lack of geometric and context information is a problem for most RGB-based methods, with which depth features are only used as an auxiliary module in RGB-D semantic segmentation. In this study, a novel depth dominant network (DDNet) is proposed to fully utilize the rich context information in the depth map. The critical insight is that obvious geometric information from the depth image is more conducive to segmentation than RGB data. Compared with other methods, DDNet is a depth-based network with two branches of CNNs to extract color and depth features. As the core of the encoder network, the depth branch is given a larger fusion weight to extract geometric information, while semantic information and complementary geometric information are provided by the color branch for the depth feature maps. The effectiveness of our proposed depth-based architecture has been demonstrated by comprehensive experimental evaluations and ablation studies on challenging RGB-D semantic segmentation benchmarks, including NYUv2 and a subset of ScanNetv2.

Distribution and associated factors of choroidal thickness in highly myopic eyes-a real-world study based on a Chinese population.

To measure the subfoveal choroidal thickness (SFCT) in highly myopic eyes at different locations using enhanced depth imaging spectral-domain optical coherence tomography (EDI SD-OCT). To identify the ocular and systemic risk factors associated with choroidal thinning in high myopia. Based on the Beijing Eye Study, a detailed ophthalmic examination was performed including EDI SD-OCT for the measurement of SFCT. OCT images were obtained from 103 highly myopic eyes (≥ -6.00 dioptres) and 227 normal eyes randomly selected from the baseline population, matched for age and sex. The mean SFCT was 110.6 ± 85.2 μm in highly myopic eyes (range, 3-395 μm). Mean regional choroidal thickness was lowest on the nasal and inferior sides of the macula, and slightly higher on the temporal and superior sides than at the fovea. On multivariate analysis, SFCT was associated with age (b = -0.48; P < 0.001), axial length (b = -0.44; P < 0.001), gender (b = -0.31; P < 0.05) and staphyloma (b = -0.26; P = 0.05). In highly myopic eyes, SFCT decreased by 5.1 μm/year of age, by 9.2 µm/D of myopia, and by 22.6 µm/mm of axial length. The SFCT decreases with age and increased axial length in highly myopic eyes. The formation of a posterior staphyloma has been identified as a major contributor to choroidal thinning and is therefore a reliable indicator for risk management. The involvement of choroidal abnormalities may be a significant factor in the development of myopic degeneration.

Point cloud segmentation method based on an image mask and its application verification

Abstract Accurately perceiving three-dimensional (3D) environments or objects is crucial for the advancement of artificial intelligence (AI) interaction technologies. Currently, various types of sensors are employed to obtain point cloud data for 3D object detection or segmentation tasks. While this multi-sensor approach provides more precise 3D data than monocular or stereo cameras, it is also more expensive. The advent of RGB-D cameras, which provide both RGB images and depth information, addresses this issue.&amp;#xD;In this study, we propose a point cloud segmentation method based on image masks. By using an RGB-D camera to capture color and depth images, we generate image masks through object recognition and segmentation. Given the mapping relationship between RGB image pixels and point clouds, these image masks can be further used to extract the point cloud data of the target objects. The experimental results revealed that the average accuracy of target segmentation was 84.78%, which was close to that of PointNet++. Compared with three traditional segmentation algorithms, the accuracy was improved by nearly 23.97%. The running time of our algorithm is reduced by 95.76% compared to the PointNet++ algorithm, which has the longest running time; and by 15.65% compared to the LCCP algorithm, which has the shortest running time among traditional methods. Compared with PointNet++, the segmentation accuracy was improved. This method addressed the issues of low robustness and excessive reliance on manual feature extraction in traditional point cloud segmentation methods, providing valuable support and reference for the accurate segmentation of 3D point clouds.

IVA-former: invisible–visible query guided amodal mask measurement network for desktop object via hierarchical transformer

Abstract Instance segmentation of desktop objects is important for service robots. Most of the previous works for desktop environments are restricted to measuring the visible area of target objects. However, when a target object is placed behind another, the algorithm that only performs visible area segmentation is unable to provide accurate appearance information for the occluded objects. To solve this problem, we propose the invisible–visible query guided amodal mask measurement network based on a hierarchical transformer for desktop scenes, which can perceive the entire appearance of objects in the presence of occlusions. In this method, the RGB-D backbone is adopted to fuse the features from both RGB and depth images. Then, the pixel decoder is used to generate multi-scale feature maps. We then adopt a hierarchical transformer decoder to predict invisible, visible, and amodal masks simultaneously. To enhance the associations between the three prediction branches, we propose a query transform module to transfer object queries between adjacent branches. Since amodal masks are a combination of invisible and visible masks, we propose an invisible–visible mixture loss that takes masks from both invisible and visible branches to further supervise the network. Our method is trained on synthetic datasets for desktop objects and evaluated on both visible and amodal real-world datasets. Compared to other recent segmentation algorithms, our method achieves competitive performance.

A Novel Polarized Light Microscope for the Examination of Birefringent Crystals in Synovial Fluid

Background: The gold standard for crystal arthritis diagnosis relies on the identification of either monosodium urate (MSU) or calcium pyrophosphate (CPP) crystals in synovial fluid. With the goal of enhanced crystal detection, we adapted a standard compensated polarized light microscope (CPLM) with a polarized digital camera and multi-focal depth imaging capabilities to create digital images from synovial fluid mounted on microscope slides. Using this single-shot computational polarized light microscopy (SCPLM) method, we compared rates of crystal detection and raters’ preference for image. Methods: Microscope slides from patients with either CPP, MSU, or no crystals in synovial fluid were acquired using CPLM and SCPLM methodologies. Detection rate, sensitivity, and specificity were evaluated by presenting expert crystal raters with (randomly sorted) CPLM and SCPLM digital images, from FOV above clinical samples. For each FOV and each method, each rater was asked to identify crystal suspects and their level of certainty for each crystal suspect and crystal type (MSU vs. CPP). Results: For the 283 crystal suspects evaluated, SCPLM resulted in higher crystal detection rates than did CPLM, for both CPP (51%. vs. 28%) and MSU (78% vs. 46%) crystals. Similarly, sensitivity was greater for SCPLM for CPP (0.63 vs. 0.35) and MSU (0.88 vs. 0.52) without giving up much specificity resulting in higher AUC. Conclusions: Subjective and objective measures of greater detection and higher certainty were observed for SCPLM over CPLM, particularly for CPP crystals. The digital data associated with these images can ultimately be incorporated into an automated crystal detection system that provides a quantitative report on crystal count, size, and morphology.

3D Head Pose Estimation via Normal Maps: A Generalized Solution for Depth Image, Point Cloud, and Mesh

Head pose estimation plays a crucial role in various applications, including human–machine interaction, autonomous driving systems, and 3D reconstruction. Current methods address the problem primarily from a 2D perspective, which limits the efficient utilization of 3D information. Herein, a novel approach, called pose orientation‐aware network (POANet), which leverages normal maps for orientation information embedding, providing abundant and robust head pose information, is introduced. POANet incorporates the axial signal perception module and the rotation matrix perception module, these lightweight modules make the approach achieve state‐of‐the‐art (SOTA) performance with few computational costs. This method can directly analyze various topological 3D data without extensive preprocessing. For depth images, POANet outperforms existing methods on the Biwi Kinect head pose dataset, reducing the mean absolute error (MAE) by ≈30% compared to the SOTA methods. POANet is the first method to perform rigid head registration in a landmark‐free manner. It also incorporates few‐shot learning capabilities and achieves an MAE of about on the Headspace dataset. These features make POANet a superior alternative to traditional generalized Procrustes analysis for mesh data processing, offering enhanced convenience for human phenotype studies.

YPR-SLAM: A SLAM System Combining Object Detection and Geometric Constraints for Dynamic Scenes

Traditional SLAM systems assume a static environment, but moving objects break this ideal assumption. In the real world, moving objects can greatly influence the precision of image matching and camera pose estimation. In order to solve these problems, the YPR-SLAM system is proposed. First of all, the system includes a lightweight YOLOv5 detection network for detecting both dynamic and static objects, which provides pre-dynamic object information to the SLAM system. Secondly, utilizing the prior information of dynamic targets and the depth image, a method of geometric constraint for removing motion feature points from the depth image is proposed. The Depth-PROSAC algorithm is used to differentiate the dynamic and static feature points so that dynamic feature points can be removed. At last, the dense cloud map is constructed by the static feature points. The YPR-SLAM system is an efficient combination of object detection and geometry constraint in a tightly coupled way, eliminating motion feature points and minimizing their adverse effects on SLAM systems. The performance of the YPR-SLAM was assessed on the public TUM RGB-D dataset, and it was found that YPR-SLAM was suitable for dynamic situations.

Finely tunable NIR‐II organic scaffolds for fluorescence/photoacoustic duplex imaging–guided noninvasive photothermal therapy of glioblastoma

AbstractOrganic agents possessing NIR‐II and photoacoustic duplex imaging capabilities, coupled with high‐efficiency photothermal conversion, offer significant potential for noninvasive and precise phototheranostics of glioblastoma, which is further augmented when these agents can concurrently exhibit tumor targeting and blood–brain barrier (BBB) permeability. This study reports a series of finely tunable NIR‐II molecular luminophores based on the aza‐BODIPY scaffold, featuring unique twisted and rotatable structures. They are further constructed to folate‐decorated polymeric nanoparticles, exhibiting remarkable NIR‐II/photoacoustic imaging performance and superior photothermal conversion efficiency (49.7%). Folate modification enables tumor targeting and BBB permeability through receptor‐mediated transcytosis, allowing for precise and efficient phototherapy in 4T1‐/glioblastoma‐bearing mice after a single intravenous injection and irradiation. This study presents a rational molecular engineering approach and a versatile structural scaffold for designing NIR‐II emitters with tailored photophysical properties and desirable phototherapeutic efficacy, thereby offering novel perspectives on the development of advanced depth imaging probes and brain tumor therapeutics.

Rheumatic fever and long-term use of benzathine penicillin as possible risk factors for extensive macular atrophy with pseudodrusen in a Brazilian cohort

BackgroundAlthough there has been a large increase in the number of extensive macular atrophy with pseudodrusen (EMAP) cases, the basic aspects of this disease remain unknown. Brazilian patients have a common past history of rheumatic fever (RF) and/or benzathine penicillin (BP) treatment possibly related to the disease. We analyzed how RF and BP might be correlated with EMAP in Brazilian patients.DesignObservational, retrospective, case-control study.MethodsThe databases of three private eye clinics in Brazil were searched for patients with an EMAP-like appearance. Each patient was asked about a previous history of RF and/or long-term use of BP. Patients underwent best-corrected visual acuity (BCVA) measurement, color fundus imaging, fundus autofluorescence (FAF) imaging, optical coherence tomography (OCT) imaging, and electroretinography (ERG). The following characteristics were analyzed: subretinal drusenoid deposits (SDD), pigment mottling, retinal pigment epithelial/basement membrane (RPE/BM) separation, outer retinal or RPE atrophy, and identification of a paving stone-like appearance. The choroidal thickness was measured using enhanced depth imaging OCT. The central atrophic area was measured manually on ultra-wide-field FAF.ResultsA total of 154 eyes of 77 patients (women, 66.2%; mean age, 58.6 years) with EMAP were included; 90.9% of patients were diagnosed with RF; 94.8% had been treated with BP and treatment was started at an average age of 7.3 years (mean duration, 11.8 years). The treatment duration was significant for the area of atrophy (P = 0.027) in which each 1-year increase in treatment duration led to an average reduction of 6.91 mm2 in area. The age at diagnosis of RF was significant (P = 0.026) for SDD. The increase of 1 year in the diagnosis of RF (late disease) led to a reduction of 24% in the chance of central SDD being present. On OCT, 65.5% eyes had SDD and more than 70% had a split RPE/BM and outer retinal or RPE atrophy. The choroidal thickness in patients with EMAP was significantly (P < 0.001) thinner than the control group. The ERG was abnormal in all eyes.ConclusionThese findings may suggest a relation between RF and EMAP in Brazilian patients. Patients with EMAP should be questioned about a history of RF.