SequenceGAN: Generating Fundus Fluorescence Angiography Sequences from Structure Fundus Image

2021 ◽  
pp. 110-120
Author(s):  
Wanyue Li ◽  
Yi He ◽  
Wen Kong ◽  
Jing Wang ◽  
Guohua Deng ◽  
...  
Keyword(s):  
Fluorescence Angiography ◽  
Fundus Image ◽  
Angiography Sequences

scholarly journals Dye-free retinal angiography using blood-oxygenation modulation

2018 ◽  
Author(s):  
Lewis E MacKenzie ◽  
Tushar R Choudhary ◽  
Javier Fernandez Ramos ◽  
Nigel Benjamin ◽  
Christian Delles ◽  
...  
Keyword(s):  
Blood Circulation ◽  
Multispectral Imaging ◽  
Skin Irritation ◽  
Blood Oxygenation ◽  
Fluorescence Angiography ◽  
Blood Oxygen ◽  
Blood Oxygen Saturation ◽  
Non Invasive ◽  
Invasive Angiography ◽  
Angiography Sequences

Fluorescence angiography (FA) is widely used for studying and diagnosing abnormalities in the retinal blood circulation, but has associated risks of nausea, skin irritation, and even death. We describe a new non-invasive angiography technique: Blood Oxygenation Modulation Angiography, in which multispectral imaging of a transient perturbation in blood-oxygen saturation, yields angiography sequences similar to FA, including key features such as sequential filling of choroidal and retinal-vessels, which underpin assessment of circulation health. This is the first non-invasive angiography technique capable of visualizing these circulation features.

Two-Tier Grading System for NPDR Severities of Diabetic Retinopathy in Retinal Fundus Images

2020 ◽  
Vol 14 ◽  
Author(s):  
Charu Bhardwaj ◽  
Shruti Jain ◽  
Meenakshi Sood
Keyword(s):  
Diabetic Retinopathy ◽  
Statistical Analysis ◽  
Early Stage ◽  
Image Feature ◽  
Grading System ◽  
Fundus Images ◽  
Fundus Image ◽  
Retinal Fundus Images ◽  
Retinal Fundus ◽  
Timely Treatment

: Diabetic Retinopathy is the leading cause of vision impairment and its early stage diagnosis relies on regular monitoring and timely treatment for anomalies exhibiting subtle distinction among different severity grades. The existing Diabetic Retinopathy (DR) detection approaches are subjective, laborious and time consuming which can only be carried out by skilled professionals. All the patents related to DR detection and diagnoses applicable for our research problem were revised by the authors. The major limitation in classification of severities lies in poor discrimination between actual lesions, background noise and other anatomical structures. A robust and computationally efficient Two-Tier DR (2TDR) grading system is proposed in this paper to categorize various DR severities (mild, moderate and severe) present in retinal fundus images. In the proposed 2TDR grading system, input fundus image is subjected to background segmentation and the foreground fundus image is used for anomaly identification followed by GLCM feature extraction forming an image feature set. The novelty of our model lies in the exhaustive statistical analysis of extracted feature set to obtain optimal reduced image feature set employed further for classification. Classification outcomes are obtained for both extracted as well as reduced feature set to validate the significance of statistical analysis in severity classification and grading. For single tier classification stage, the proposed system achieves an overall accuracy of 100% by k- Nearest Neighbour (kNN) and Artificial Neural Network (ANN) classifier. In second tier classification stage an overall accuracy of 95.3% with kNN and 98.0% with ANN is achieved for all stages utilizing optimal reduced feature set. 2TDR system demonstrates overall improvement in classification performance by 2% and 6% for kNN and ANN respectively after feature set reduction, and also outperforms the accuracy obtained by other state of the art methods when applied to the MESSIDOR dataset. This application oriented work aids in accurate DR classification for effective diagnosis and timely treatment of severe retinal ailment.

scholarly journals Defining indocyanine green fluorescence to assess anastomotic perfusion during gastrointestinal surgery: systematic review

BJS Open ◽  
2021 ◽  
Vol 5 (2) ◽  
Author(s):  
M D Slooter ◽  
M S E Mansvelders ◽  
P R Bloemen ◽  
S S Gisbertz ◽  
W A Bemelman ◽  
...  
Keyword(s):  
Systematic Review ◽  
Indocyanine Green ◽  
Patient Outcomes ◽  
Gastrointestinal Surgery ◽  
Fluorescence Angiography ◽  
Future Research ◽  
Fluorescence Signal ◽  
Pixel Intensity ◽  
Indocyanine Green Fluorescence ◽  
Quantitative Parameters

Abstract Background The aim of this systematic review was to identify all methods to quantify intraoperative fluorescence angiography (FA) of the gastrointestinal anastomosis, and to find potential thresholds to predict patient outcomes, including anastomotic leakage and necrosis. Methods This systematic review adhered to the PRISMA guidelines. A PubMed and Embase literature search was performed. Articles were included when FA with indocyanine green was performed to assess gastrointestinal perfusion in human or animals, and the fluorescence signal was analysed using quantitative parameters. A parameter was defined as quantitative when a diagnostic numeral threshold for patient outcomes could potentially be produced. Results Some 1317 articles were identified, of which 23 were included. Fourteen studies were done in patients and nine in animals. Eight studies applied FA during upper and 15 during lower gastrointestinal surgery. The quantitative parameters were divided into four categories: time to fluorescence (20 studies); contrast-to-background ratio (3); pixel intensity (2); and numeric classification score (2). The first category was subdivided into manually assessed time (7 studies) and software-derived fluorescence–time curves (13). Cut-off values were derived for manually assessed time (speed in gastric conduit wall) and derivatives of the fluorescence–time curves (Fmax, T1/2, TR and slope) to predict patient outcomes. Conclusion Time to fluorescence seems the most promising category for quantitation of FA. Future research might focus on fluorescence–time curves, as many different parameters can be derived and the fluorescence intensity can be bypassed. However, consensus on study set-up, calibration of fluorescence imaging systems, and validation of software programs is mandatory to allow future data comparison.

scholarly journals Development of a Fundus Image-Based Deep Learning Diagnostic Tool for Various Retinal Diseases

2021 ◽  
Vol 11 (5) ◽  
pp. 321
Author(s):  
Kyoung Min Kim ◽  
Tae-Young Heo ◽  
Aesul Kim ◽  
Joohee Kim ◽  
Kyu Jin Han ◽  
...  
Keyword(s):  
Network Models ◽  
Retinal Disease ◽  
Age Related Macular Degeneration ◽  
Diagnostic Tools ◽  
Retinal Images ◽  
Dense Layer ◽  
Retinal Diseases ◽  
Fundus Image ◽  
Neural Network Models ◽  
Age Related

Artificial intelligence (AI)-based diagnostic tools have been accepted in ophthalmology. The use of retinal images, such as fundus photographs, is a promising approach for the development of AI-based diagnostic platforms. Retinal pathologies usually occur in a broad spectrum of eye diseases, including neovascular or dry age-related macular degeneration, epiretinal membrane, rhegmatogenous retinal detachment, retinitis pigmentosa, macular hole, retinal vein occlusions, and diabetic retinopathy. Here, we report a fundus image-based AI model for differential diagnosis of retinal diseases. We classified retinal images with three convolutional neural network models: ResNet50, VGG19, and Inception v3. Furthermore, the performance of several dense (fully connected) layers was compared. The prediction accuracy for diagnosis of nine classes of eight retinal diseases and normal control was 87.42% in the ResNet50 model, which added a dense layer with 128 nodes. Furthermore, our AI tool augments ophthalmologist’s performance in the diagnosis of retinal disease. These results suggested that the fundus image-based AI tool is applicable for the medical diagnosis process of retinal diseases.

scholarly journals Image Preprocessing in Classification and Identification of Diabetic Eye Diseases

2021 ◽  
Author(s):  
Rubina Sarki ◽  
Khandakar Ahmed ◽  
Hua Wang ◽  
Yanchun Zhang ◽  
Jiangang Ma ◽  
...  
Keyword(s):  
Image Processing ◽  
Image Quality ◽  
Geometric Transformation ◽  
Diabetic Patients ◽  
Fundus Image ◽  
Classification Problems ◽  
Specificity And Sensitivity ◽  
Retinal Fundus Image ◽  
Retinal Fundus ◽  
Traditional Image

AbstractDiabetic eye disease (DED) is a cluster of eye problem that affects diabetic patients. Identifying DED is a crucial activity in retinal fundus images because early diagnosis and treatment can eventually minimize the risk of visual impairment. The retinal fundus image plays a significant role in early DED classification and identification. An accurate diagnostic model’s development using a retinal fundus image depends highly on image quality and quantity. This paper presents a methodical study on the significance of image processing for DED classification. The proposed automated classification framework for DED was achieved in several steps: image quality enhancement, image segmentation (region of interest), image augmentation (geometric transformation), and classification. The optimal results were obtained using traditional image processing methods with a new build convolution neural network (CNN) architecture. The new built CNN combined with the traditional image processing approach presented the best performance with accuracy for DED classification problems. The results of the experiments conducted showed adequate accuracy, specificity, and sensitivity.

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