Development and Validation of an Automatic Ultrawide-Field Fundus Imaging Enhancement System for Facilitating Clinical Diagnosis:A Cross-Sectional Multicenter Study

In ophthalmology,the quality of fundus images is critical for accurate diagnosis,both in clinical practice and in artificial intelligence(AI)-assisted diagnostics.Despite the broad view provided by ultrawide-field(UWF)imaging,pseudocolor images may conceal critical lesions necessary for precise diagnosis.To address this,we introduce UWF-Net,a sophisticated image enhancement algorithm that takes disease characteristics into consideration.Using the Fudan University ultra-wide-field image(FDUWI)dataset,which includes 11294 Optos pseudocolor and 2415 Zeiss true-color UWF images,each of which is rigorously annotated,UWF-Net combines global style modeling with feature-level lesion enhancement.Pathological consistency loss is also applied to maintain fundus feature integrity,significantly improving image quality.Quantitative and qualitative evaluations demonstrated that UWF-Net outperforms existing methods such as contrast limited adaptive histogram equalization(CLAHE)and structure and illumination constrained generative adversarial network(StillGAN),delivering superior retinal image quality,higher quality scores,and preserved feature details after enhancement.In disease classification tasks,images enhanced by UWF-Net showed notable improvements when processed with existing classification systems over those enhanced by StillGAN,demonstrating a 4.62%increase in sensitivity(SEN)and a 3.97%increase in accuracy(ACC).In a multicenter clinical setting,UWF-Net-enhanced images were preferred by ophthalmologic technicians and doctors,and yielded a significant reduction in diagnostic time((13.17±8.40)s for UWF-Net enhanced images vs(19.54±12.40)s for original images)and an increase in diagnostic accuracy(87.71%for UWF-Net enhanced images vs 80.40%for original images).Our research verifies that UWF-Net markedly improves the quality of UWF imaging,facilitating better clinical outcomes and more reliable AI-assisted disease classification.The clinical integration of UWF-Net holds great promise for enhancing diagnostic processes and patient care in ophthalmology.

Topic highlight on texture and color enhancement imaging in gastrointestinal diseases

Olympus Corporation developed texture and color enhancement imaging(TXI)as a novel image-enhancing endoscopic technique.This topic highlights a series of hot-topic articles that investigated the efficacy of TXI for gastrointestinal disease identification in the clinical setting.A randomized controlled trial demonstrated improvements in the colorectal adenoma detection rate(ADR)and the mean number of adenomas per procedure(MAP)of TXI compared with those of white-light imaging(WLI)observation(58.7%vs 42.7%,adjusted relative risk 1.35,95%CI:1.17-1.56;1.36 vs 0.89,adjusted incident risk ratio 1.48,95%CI:1.22-1.80,respectively).A cross-over study also showed that the colorectal MAP and ADR in TXI were higher than those in WLI(1.5 vs 1.0,adjusted odds ratio 1.4,95%CI:1.2-1.6;58.2%vs 46.8%,1.5,1.0-2.3,respectively).A randomized controlled trial demonstrated non-inferiority of TXI to narrow-band imaging in the colorectal mean number of adenomas and sessile serrated lesions per procedure(0.29 vs 0.30,difference for non-inferiority-0.01,95%CI:-0.10 to 0.08).A cohort study found that scoring for ulcerative colitis severity using TXI could predict relapse of ulcerative colitis.A cross-sectional study found that TXI improved the gastric cancer detection rate compared to WLI(0.71%vs 0.29%).A cross-sectional study revealed that the sensitivity and accuracy for active Helicobacter pylori gastritis in TXI were higher than those of WLI(69.2%vs 52.5%and 85.3%vs 78.7%,res-pectively).In conclusion,TXI can improve gastrointestinal lesion detection and qualitative diagnosis.Therefore,further studies on the efficacy of TXI in clinical practice are required.

Improved visibility of colorectal tumor by texture and color enhancement imaging with indigo carmine

BACKGROUND Accurate diagnosis and early resection of colorectal polyps are important to prevent the occurrence of colorectal cancer.However,technical factors and morphological factors of polyps itself can lead to missed diagnoses.Imageenhanced endoscopy and chromoendoscopy(CE)have been developed to facilitate an accurate diagnosis.There have been no reports on visibility using a combination of texture and color enhancement imaging(TXI)and CE for colorectal tumors.AIM To investigate the visibility of margins and surfaces with the combination of TXI and CE for colorectal lesions.METHODS This retrospective study included patients who underwent lower gastrointestinal endoscopy at the Toyoshima Endoscopy Clinic.We extracted polyps that were resected and diagnosed as adenomas or serrated polyps(hyperplastic polyps and sessile serrated lesions)from our endoscopic database.An expert endoscopist performed the lower gastrointestinal endoscopies and observed the lesion using white light imaging(WLI),TXI,CE,and TXI+CE modalities.Indigo carmine dye was used for CE.Three expert endoscopists rated the visibility of the margin and surface patterns in four ranks,from 1 to 4.The primary outcomes were the average visibility scores for the margin and surface patterns based on the WLI,TXI,CE,and TXI+CE observations.Visibility scores between the four modalities were compared by the Kruskal-Wallis and Dunn tests.RESULTS A total of 48 patients with 81 polyps were assessed.The histological subtypes included 50 tubular adenomas,16 hyperplastic polyps,and 15 sessile serrated lesions.The visibility scores for the margins based on WLI,TXI,CE,and TXI+CE were 2.44±0.93,2.90±0.93,3.37±0.74,and 3.75±0.49,respectively.The visibility scores for the surface based on WLI,TXI,CE,and TXI+CE were 2.25±0.80,2.84±0.84,3.12±0.72,and 3.51±0.60,respectively.The visibility scores for the detection and surface on TXI were significantly lower than that on CE but higher than that on WLI(P<0.001).The visibility scores for the margin and surface on TXI+CE were significantly higher than those on CE(P<0.001).In the sub-analysis of adenomas,the visibility for the margin and surface on TXI+CE was significantly better than that on WLI,TXI,and CE(P<0.001).In the sub-analysis of serrated polyps,the visibility for the margin and surface on TXI+CE was also significantly better than that on WLI,TXI,and CE(P<0.001).CONCLUSION TXI+CE enhanced the visibility of the margin and surface compared to WLI,TXI,and CE for colorectal lesions.

Image enhancement with intensity transformation on embedding space

In recent times,an image enhancement approach,which learns the global transformation function using deep neural networks,has gained attention.However,many existing methods based on this approach have a limitation:their transformation functions are too simple to imitate complex colour transformations between low-quality images and manually retouched high-quality images.In order to address this limitation,a simple yet effective approach for image enhancement is proposed.The proposed algorithm based on the channel-wise intensity transformation is designed.However,this transformation is applied to the learnt embedding space instead of specific colour spaces and then return enhanced features to colours.To this end,the authors define the continuous intensity transformation(CIT)to describe the mapping between input and output intensities on the embedding space.Then,the enhancement network is developed,which produces multi-scale feature maps from input images,derives the set of transformation functions,and performs the CIT to obtain enhanced images.Extensive experiments on the MIT-Adobe 5K dataset demonstrate that the authors’approach improves the performance of conventional intensity transforms on colour space metrics.Specifically,the authors achieved a 3.8%improvement in peak signal-to-noise ratio,a 1.8%improvement in structual similarity index measure,and a 27.5%improvement in learned perceptual image patch similarity.Also,the authors’algorithm outperforms state-of-the-art alternatives on three image enhancement datasets:MIT-Adobe 5K,Low-Light,and Google HDRþ.

Unsupervised Multi-Expert Learning Model for Underwater Image Enhancement

Underwater image enhancement aims to restore a clean appearance and thus improves the quality of underwater degraded images.Current methods feed the whole image directly into the model for enhancement.However,they ignored that the R,G and B channels of underwater degraded images present varied degrees of degradation,due to the selective absorption for the light.To address this issue,we propose an unsupervised multi-expert learning model by considering the enhancement of each color channel.Specifically,an unsupervised architecture based on generative adversarial network is employed to alleviate the need for paired underwater images.Based on this,we design a generator,including a multi-expert encoder,a feature fusion module and a feature fusion-guided decoder,to generate the clear underwater image.Accordingly,a multi-expert discriminator is proposed to verify the authenticity of the R,G and B channels,respectively.In addition,content perceptual loss and edge loss are introduced into the loss function to further improve the content and details of the enhanced images.Extensive experiments on public datasets demonstrate that our method achieves more pleasing results in vision quality.Various metrics(PSNR,SSIM,UIQM and UCIQE) evaluated on our enhanced images have been improved obviously.

More Than Lightening:A Self-Supervised Low-Light Image Enhancement Method Capable for Multiple Degradations

Low-light images suffer from low quality due to poor lighting conditions,noise pollution,and improper settings of cameras.To enhance low-light images,most existing methods rely on normal-light images for guidance but the collection of suitable normal-light images is difficult.In contrast,a self-supervised method breaks free from the reliance on normal-light data,resulting in more convenience and better generalization.Existing self-supervised methods primarily focus on illumination adjustment and design pixel-based adjustment methods,resulting in remnants of other degradations,uneven brightness and artifacts.In response,this paper proposes a self-supervised enhancement method,termed as SLIE.It can handle multiple degradations including illumination attenuation,noise pollution,and color shift,all in a self-supervised manner.Illumination attenuation is estimated based on physical principles and local neighborhood information.The removal and correction of noise and color shift removal are solely realized with noisy images and images with color shifts.Finally,the comprehensive and fully self-supervised approach can achieve better adaptability and generalization.It is applicable to various low light conditions,and can reproduce the original color of scenes in natural light.Extensive experiments conducted on four public datasets demonstrate the superiority of SLIE to thirteen state-of-the-art methods.Our code is available at https://github.com/hanna-xu/SLIE.

A Modified CycleGAN for Multi-Organ Ultrasound Image Enhancement via Unpaired Pre-Training

Handheld ultrasound devices are known for their portability and affordability,making them widely utilized in underdeveloped areas and community healthcare for rapid diagnosis and early screening.However,the image quality of handheld ultrasound devices is not always satisfactory due to the limited equipment size,which hinders accurate diagnoses by doctors.At the same time,paired ultrasound images are difficult to obtain from the clinic because imaging process is complicated.Therefore,we propose a modified cycle generative adversarial network(cycleGAN) for ultrasound image enhancement from multiple organs via unpaired pre-training.We introduce an ultrasound image pre-training method that does not require paired images,alleviating the requirement for large-scale paired datasets.We also propose an enhanced block with different structures in the pre-training and fine-tuning phases,which can help achieve the goals of different training phases.To improve the robustness of the model,we add Gaussian noise to the training images as data augmentation.Our approach is effective in obtaining the best quantitative evaluation results using a small number of parameters and less training costs to improve the quality of handheld ultrasound devices.

A Novel Multi-Stream Fusion Network for Underwater Image Enhancement

Due to the selective absorption of light and the existence of a large number of floating media in sea water, underwater images often suffer from color casts and detail blurs. It is therefore necessary to perform color correction and detail restoration. However,the existing enhancement algorithms cannot achieve the desired results. In order to solve the above problems, this paper proposes a multi-stream feature fusion network. First, an underwater image is preprocessed to obtain potential information from the illumination stream, color stream and structure stream by histogram equalization with contrast limitation, gamma correction and white balance, respectively. Next, these three streams and the original raw stream are sent to the residual blocks to extract the features. The features will be subsequently fused. It can enhance feature representation in underwater images. In the meantime, a composite loss function including three terms is used to ensure the quality of the enhanced image from the three aspects of color balance, structure preservation and image smoothness. Therefore, the enhanced image is more in line with human visual perception.Finally, the effectiveness of the proposed method is verified by comparison experiments with many stateof-the-art underwater image enhancement algorithms. Experimental results show that the proposed method provides superior results over them in terms of MSE,PSNR, SSIM, UIQM and UCIQE, and the enhanced images are more similar to their ground truth images.

Underwater Image Enhancement Based on Multi-scale Adversarial Network

In this study,an underwater image enhancement method based on multi-scale adversarial network was proposed to solve the problem of detail blur and color distortion in underwater images.Firstly,the local features of each layer were enhanced into the global features by the proposed residual dense block,which ensured that the generated images retain more details.Secondly,a multi-scale structure was adopted to extract multi-scale semantic features of the original images.Finally,the features obtained from the dual channels were fused by an adaptive fusion module to further optimize the features.The discriminant network adopted the structure of the Markov discriminator.In addition,by constructing mean square error,structural similarity,and perceived color loss function,the generated image is consistent with the reference image in structure,color,and content.The experimental results showed that the enhanced underwater image deblurring effect of the proposed algorithm was good and the problem of underwater image color bias was effectively improved.In both subjective and objective evaluation indexes,the experimental results of the proposed algorithm are better than those of the comparison algorithm.

Pre-operative enhanced magnetic resonance imaging combined with clinical features predict early recurrence of hepatocellular carcinoma after radical resection

BACKGROUND Indentifying predictive factors for postoperative recurrence of hepatocellular carcinoma(HCC)has great significance for patient prognosis.AIM To explore the value of gadolinium ethoxybenzyl diethylenetriamine pentaacetic acid(Gd-EOB-DTPA)enhanced magnetic resonance imaging(MRI)combined with clinical features in predicting early recurrence of HCC after resection.METHODS A total of 161 patients with pathologically confirmed HCC were enrolled.The patients were divided into early recurrence and non-early recurrence group based on the follow-up results.The clinical,laboratory,pathological results and Gd-EOB-DTPA enhanced MRI imaging features were analyzed.RESULTS Of 161 patients,73 had early recurrence and 88 were had non-early recurrence.Univariate analysis showed that patient age,gender,serum alpha-fetoprotein level,the Barcelona Clinic Liver Cancer stage,China liver cancer(CNLC)stage,microvascular invasion(MVI),pathological satellite focus,tumor size,tumor number,tumor boundary,tumor capsule,intratumoral necrosis,portal vein tumor thrombus,large vessel invasion,nonperipheral washout,peritumoral enhancement,hepatobiliary phase(HBP)/tumor signal intensity(SI)/peritumoral SI,HBP peritumoral low signal and peritumoral delay enhancement were significantly associated with early recurrence of HCC after operation.Multivariate logistic regression analysis showed that patient age,MVI,CNLC stage,tumor boundary and large vessel invasion were independent predictive factors.External data validation indicated that the area under the curve of the combined predictors was 0.861,suggesting that multivariate logistic regression was a reasonable predictive model for early recurrence of HCC.CONCLUSION Gd-EOB-DTPA enhanced MRI combined with clinical features would help predicting the early recurrence of HCC after operation.

Texture and color enhancement imaging in magnifying endoscopic evaluation of colorectal adenomas

BACKGROUND Olympus Corporation has developed texture and color enhancement imaging(TXI)as a novel image-enhancing endoscopic technique.AIM To investigate the effectiveness of TXI in identifying colorectal adenomas using magnifying observation.METHODS Colorectal adenomas were observed by magnified endoscopy using white light imaging(WLI),TXI,narrow band imaging(NBI),and chromoendoscopy(CE).This study adopted mode 1 of TXI.Adenomas were confirmed by histological examination.TXI visibility was compared with the visibility of WLI,NBI,and CE for tumor margin,and vessel and surface patterns of the Japan NBI expert team(JNET)classification.Three expert endoscopists and three non-expert endoscopists evaluated the visibility scores,which were classified as 1,2,3,and 4.RESULTS Sixty-one consecutive adenomas were evaluated.The visibility score for tumor margin of TXI(3.47±0.79)was significantly higher than that of WLI(2.86±1.02,P<0.001),but lower than that of NBI(3.76±0.52,P<0.001),regardless of the endoscopist’s expertise.TXI(3.05±0.79)had a higher visibility score for the vessel pattern of JNET classification than WLI(2.17±0.90,P<0.001)and CE(2.47±0.87,P<0.001),but lower visibility score than NBI(3.79±0.47,P<0.001),regardless of the experience of endoscopists.For the visibility score for the surface pattern of JNET classification,TXI(2.89±0.85)was superior to WLI(1.95±0.79,P<0.01)and CE(2.75±0.90,P=0.002),but inferior to NBI(3.67±0.55,P<0.001).CONCLUSION TXI provided higher visibility than WLI,lower than NBI,and comparable to or higher than CE in the magnified observation of colorectal adenomas.

Texture and color enhancement imaging for detecting colorectal adenomas: Good, but not good enough

Texture and color enhancement imaging(TXI)has been developed as a novel image-enhancing endoscopy.However,the effectiveness of TXI detecting adenomas is inferior to narrow band imaging.Thus,future studies will need to focus on investigating the feasibility of such combination in clinical settings in order to provide patients with more accurate diagnoses.

Mucosal patterns change after Helicobacter pylori eradication:Evaluation using blue laser imaging in patients with atrophic gastritis

BACKGROUND Mucosal patterns(MPs)observed on blue laser imaging in patients with atrophic gastritis can be classified as spotty,cracked,and mottled.Furthermore,we hypothesized that the spotty pattern may change to the cracked pattern after Helicobacter pylori(H.pylori)eradication.AIM To further substantiate and comprehensively investigate MP changes after H.pylori eradication in a larger number of patients.METHODS We included 768 patients who were diagnosed with atrophic gastritis with evaluable MP using upper gastrointestinal endoscopy at the Nishikawa Gastrointestinal Clinic,Japan.Among them,325 patients were H.pylori-positive,and of them,101 patients who underwent upper gastrointestinal endoscopy before and after H.pylori eradication were evaluated for post-eradication MP changes.The patients’MPs were interpreted by three experienced endoscopists who were blinded to their clinical features.RESULTS Among 76 patients with the spotty pattern before or after H.pylori eradication,the pattern disappeared or decreased in 67 patients[88.2%,95%confidence interval(CI):79.0%-93.6%),appeared or increased in 8 patients(10.5%,95%CI:5.4%-19.4%),and showed no change in 1 patient(1.3%,95%CI:0.2%-7.1%).In 90 patients with the cracked pattern before or after H.pylori eradication,the pattern disappeared or decreased in 7 patients(7.8%,95%CI:3.8%-15.2%),appeared or increased in 79 patients(87.8%,95%CI:79.4%-93.0%),and showed no change in 4 patients(4.4%,95%CI:1.7%-10.9%).In 70 patients with the mottled pattern before or after H.pylori eradication,the pattern disappeared or decreased in 28 patients(40.0%,95%CI:29.3%-51.7%),appeared or increased in 35 patients(50.0%,95%CI:38.6%-61.4%),and showed no change in 7 patients(10.0%,95%CI:4.9%-19.2%).CONCLUSION After H.pylori eradication,MPs changed from spotty to cracked in most patients,which may help endoscopists easily and precisely evaluate H.pylori-related gastritis status.

A Text Image Watermarking Algorithm Based on Image Enhancement

Digital watermarking technology is adequate for copyright protection and content authentication.There needs to be more research on the watermarking algorithm after printing and scanning.Aiming at the problem that existing anti-print scanning text image watermarking algorithms cannot take into account the invisibility and robustness of the watermark,an anti-print scanning watermarking algorithm suitable for text images is proposed.This algorithm first performs a series of image enhancement preprocessing operations on the printed scanned image to eliminate the interference of incorrect bit information on watermark embedding and then uses a combination of Discrete Wavelet Transform(DWT)-Singular Value Decomposition(SVD)to embed the watermark.Experiments show that the average Normalized Correlation(NC)of the watermark extracted by this algorithm against attacks such as Joint Photographic Experts Group(JPEG)compression,JPEG2000 compression,and print scanning is above 0.93.Especially,the average NC of the watermark extracted after print scanning attacks is greater than 0.964,and the average Bit Error Ratio(BER)is 5.15%.This indicates that this algorithm has strong resistance to various attacks and print scanning attacks and can better take into account the invisibility of the watermark.

Development of a Compact Photoacoustic Tomography Imaging System with Dual Single-Element Transducers for Image Enhancement

Objective:This paper proposes a new photoacoustic computed tomography(PACT)imaging system employing dual ultrasonic transducers with different frequencies.When imaging complex biological tissues,photoacoustic(PA)signals with multiple frequencies are produced simultaneously;however,due to the limited bandwidth of a single-frequency transducer,the received PA signals with specific frequencies may be missing,leading to a low imaging quality.Methods:In contrast to our previous work,the proposed system has a compact volume as well as specific selection of the detection center frequency of the transducer,which can provide a comprehensive range for the detection of PA signals.In this study,a series of numerical simulation and phantom experiments were performed to validate the efficacy of the developed PACT system.Results:The images generated by our system combined the advantages of both high resolution and ideal brightness/contrast.Conclusion:The interchangeability of transducers with different frequencies provides potential for clinical deployment under the circumstance where a single frequency transducer cannot perform well.

Removal of Stripes in Remote Sensing Images Based on Statistics Combined with Image Enhancement

A method to remove stripes from remote sensing images is proposed based on statistics and a new image enhancement method.The overall processing steps for improving the quality of remote sensing images are introduced to provide a general baseline.Due to the differences in satellite sensors when producing images,subtle but inherent stripes can appear at the stitching positions between the sensors.These stitchingstripes cannot be eliminated by conventional relative radiometric calibration.The inherent stitching stripes cause difficulties in downstream tasks such as the segmentation,classification and interpretation of remote sensing images.Therefore,a method to remove the stripes based on statistics and a new image enhancement approach are proposed in this paper.First,the inconsistency in grayscales around stripes is eliminated with the statistical method.Second,the pixels within stripes are weighted and averaged based on updated pixel values to enhance the uniformity of the overall image radiation quality.Finally,the details of the images are highlighted by a new image enhancement method,which makes the whole image clearer.Comprehensive experiments are performed,and the results indicate that the proposed method outperforms the baseline approach in terms of visual quality and radiation correction accuracy.

Underwater Image Enhancement Based on IMSRCR and CLAHE-WGIF

Aiming at the scattering and absorption of light in the water body,which causes the problems of color shift,uneven brightness,poor sharpness and missing details in the acquired underwater images,an underwater image enhancement algorithm based on IMSRCR and CLAHE-WGIF is proposed.Firstly,the IMSRCR algorithm proposed in this paper is used to process the original underwater image with adaptive color shift correction;secondly,the image is converted to HSV color space,and the segmentation exponential algorithm is used to process the S component to enhance the image saturation;finally,multi-scale Retinex is used to decompose the V component image into detail layer and base layer,and adaptive two-dimensional gamma correction is made to the base layer to adjust the brightness unevenness,while the detail layer is processed by CLAHE-WGIF algorithm to enhance the image contrast and detail information.The experimental results show that our algorithm has some advantages over existing algorithms in both subjective and objective evaluations,and the information entropy of the image is improved by 6.3%on average,and the UIQM and UCIQE indexes are improved by 12.9%and 20.3%on average.

Pixel’s Quantum Image Enhancement Using Quantum Calculus

The current study provides a quantum calculus-based medical image enhancement technique that dynamically chooses the spatial distribution of image pixel intensity values.The technique focuses on boosting the edges and texture of an image while leaving the smooth areas alone.The brain Magnetic Resonance Imaging(MRI)scans are used to visualize the tumors that have spread throughout the brain in order to gain a better understanding of the stage of brain cancer.Accurately detecting brain cancer is a complex challenge that the medical system faces when diagnosing the disease.To solve this issue,this research offers a quantum calculus-based MRI image enhancement as a pre-processing step for brain cancer diagnosis.The proposed image enhancement approach improves images with low gray level changes by estimating the pixel’s quantum probability.The suggested image enhancement technique is demonstrated to be robust and resistant to major quality changes on a variety ofMRIscan datasets of variable quality.ForMRI scans,the BRISQUE“blind/referenceless image spatial quality evaluator”and the NIQE“natural image quality evaluator”measures were 39.38 and 3.58,respectively.The proposed image enhancement model,according to the data,produces the best image quality ratings,and it may be able to aid medical experts in the diagnosis process.The experimental results were achieved using a publicly available collection of MRI scans.

RF-Net: Unsupervised Low-Light Image Enhancement Based on Retinex and Exposure Fusion

Low-light image enhancement methods have limitations in addressing issues such as color distortion,lack of vibrancy,and uneven light distribution and often require paired training data.To address these issues,we propose a two-stage unsupervised low-light image enhancement algorithm called Retinex and Exposure Fusion Network(RFNet),which can overcome the problems of over-enhancement of the high dynamic range and under-enhancement of the low dynamic range in existing enhancement algorithms.This algorithm can better manage the challenges brought about by complex environments in real-world scenarios by training with unpaired low-light images and regular-light images.In the first stage,we design a multi-scale feature extraction module based on Retinex theory,capable of extracting details and structural information at different scales to generate high-quality illumination and reflection images.In the second stage,an exposure image generator is designed through the camera response mechanism function to acquire exposure images containing more dark features,and the generated images are fused with the original input images to complete the low-light image enhancement.Experiments show the effectiveness and rationality of each module designed in this paper.And the method reconstructs the details of contrast and color distribution,outperforms the current state-of-the-art methods in both qualitative and quantitative metrics,and shows excellent performance in the real world.

Optical coherence tomography enhanced depth imaging of chorioretinal folds in patients with orbital tumors

AIM:To characterize spectral-domain optical coherence tomography(SD-OCT)features of chorioretinal folds in orbital mass imaged using enhanced depth imaging(EDI).METHODS:Prospective observational case-control study was conducted in 20 eyes of 20 patients,the uninvolved eye served as a control.All the patients underwent clinical fundus photography,computed tomography,EDI SDOCT imaging before and after surgery.Two patients with cavernous hemangiomas underwent intratumoral injection of bleomycin A5;the remaining patients underwent tumor excision.Patients were followed 1 to 14mo following surgery(average follow up,5.8mo).RESULTS:Visual acuity prior to surgery ranged from 20/20 to 20/200.Following surgery,5 patients’visual acuity remained unchanged while the remaining 15 patients had a mean letter improvement of 10(range 4 to 26 letters).Photoreceptor inner/outer segment defects were found in 10 of 15 patients prior to surgery.Following surgical excision,photoreceptor inner/outer segment defects fully resolved in 8 of these 10 patients.CONCLUSION:Persistence of photoreceptor inner/outer segment defects caused by compression of the globe by an orbital mass can be associated with reduced visual prognosis.Our findings suggest that photoreceptor inner/outer segment defects on EDI SD-OCT could be an indicator for immediate surgical excision of an orbital mass causing choroidal compression.