Machine Learning Based Automatic Neovascularization Detection on Optic Disc Region

2018 ◽  
Vol 22 (3) ◽  
pp. 886-894 ◽  
Author(s):  
Shuang Yu ◽  
Di Xiao ◽  
Yogesan Kanagasingam
Keyword(s):  
Machine Learning ◽  
Optic Disc ◽  
Disc Region

scholarly journals Automatic identification of myopic maculopathy related imaging features in optic disc region via machine learning methods

2021 ◽  
Vol 19 (1) ◽  
Author(s):  
Yuchen Du ◽  
Qiuying Chen ◽  
Ying Fan ◽  
Jianfeng Zhu ◽  
Jiangnan He ◽  
...  
Keyword(s):  
Machine Learning ◽  
Optic Disc ◽  
Early Stage ◽  
Image Features ◽  
Pathologic Myopia ◽  
High Risk Population ◽  
Imaging Features ◽  
Chorioretinal Atrophy ◽  
Disc Region ◽  
Myopic Maculopathy

Abstract Background Myopic maculopathy (MM) is the most serious and irreversible complication of pathologic myopia, which is a major cause of visual impairment and blindness. Clinic proposed limited number of factors related to MM. To explore additional features strongly related with MM from optic disc region, we employ a machine learning based radiomics analysis method, which could explore and quantify more hidden or imperceptible MM-related features to the naked eyes and contribute to a more comprehensive understanding of MM and therefore may assist to distinguish the high-risk population in an early stage. Methods A total of 457 eyes (313 patients) were enrolled and were divided into severe MM group and without severe MM group. Radiomics analysis was applied to depict features significantly correlated with severe MM from optic disc region. Receiver Operating Characteristic were used to evaluate these features’ performance of classifying severe MM. Results Eight new MM-related image features were discovered from the optic disc region, which described the shapes, textural patterns and intensity distributions of optic disc region. Compared with clinically reported MM-related features, these newly discovered features exhibited better abilities on severe MM classification. And the mean values of most features were markedly changed between patients with peripapillary diffuse chorioretinal atrophy (PDCA) and macular diffuse chorioretinal atrophy (MDCA). Conclusions Machine learning and radiomics method are useful tools for mining more MM-related features from the optic disc region, by which complex or even hidden MM-related features can be discovered and decoded. In this paper, eight new MM-related image features were found, which would be useful for further quantitative study of MM-progression. As a nontrivial byproduct, marked changes between PDCA and MDCA was discovered by both new image features and clinic features.

scholarly journals Correction to: Automatic identification of myopic maculopathy related imaging features in optic disc region via machine learning methods

2021 ◽  
Vol 19 (1) ◽  
Author(s):  
Yuchen Du ◽  
Qiuying Chen ◽  
Ying Fan ◽  
Jianfeng Zhu ◽  
Jiangnan He ◽  
...  
Keyword(s):  
Machine Learning ◽  
Optic Disc ◽  
Automatic Identification ◽  
Imaging Features ◽  
Learning Methods ◽  
Machine Learning Methods ◽  
Disc Region ◽  
Myopic Maculopathy

An amendment to this paper has been published and can be accessed via the original article.

scholarly journals Region Focus Network for Joint Optic Disc and Cup Segmentation

2020 ◽  
Vol 34 (01) ◽  
pp. 751-758
Author(s):  
Ge Li ◽  
Changsheng Li ◽  
Chan Zeng ◽  
Peng Gao ◽  
Guotong Xie
Keyword(s):  
Optic Disc ◽  
Feature Representation ◽  
Fundus Images ◽  
Feature Pooling ◽  
The World ◽  
Optic Cup ◽  
Disc Region ◽  
Class Segmentation ◽  
Fusion Feature ◽  
Screening And Diagnosis

Glaucoma is one of the three leading causes of blindness in the world and is predicted to affect around 80 million people by 2020. The optic cup (OC) to optic disc (OD) ratio (CDR) in fundus images plays a pivotal role in the screening and diagnosis of glaucoma. Existing methods usually crop the optic disc region first, and subsequently perform segmentation in this region. However, these approaches come up with high complexities due to the separate operations. To remedy this issue, we propose a Region Focus Network (RF-Net) that innovatively integrates detection and multi-class segmentation into a unified architecture for end-to-end joint optic disc and cup segmentation with global optimization. The key idea of our method is designing a novel multi-class mask branch which generates a high-quality segmentation in the detected region for both disc and cup. To bridge the connection between the backbone and multi-class mask branch, a Fusion Feature Pooling (FFP) structure is presented to extract features from each level of the pyramid network and fuse them into a final feature representation for segmentation. Extensive experimental results on the REFUGE-2018 challenge dataset and the Drishti-GS dataset show that the proposed method achieves the best performance, compared with competitive approaches reported in the literature and the official leaderboard. Our code will be released soon.

scholarly journals Classification of optic disc shape in glaucoma using machine learning based on quantified ocular parameters

PLoS ONE ◽  
2017 ◽  
Vol 12 (12) ◽  
pp. e0190012 ◽  
Author(s):  
Kazuko Omodaka ◽  
Guangzhou An ◽  
Satoru Tsuda ◽  
Yukihiro Shiga ◽  
Naoko Takada ◽  
...  
Keyword(s):  
Machine Learning ◽  
Optic Disc

scholarly journals Retinal Fundus image processing and ensemble learning: optic disc and optic cup detection

2021 ◽  
Author(s):  
Shima Mohammadali Pishnamaz
Keyword(s):  
Machine Learning ◽  
Image Processing ◽  
Optic Disc ◽  
Gradient Boosting ◽  
Support Vector ◽  
Fundus Images ◽  
Fundus Image ◽  
Extreme Gradient Boosting ◽  
Optic Cup ◽  
Retinal Fundus

Ophthalmologists have widely used retinal fundus imaging systems to examine the health of the optic nerve, vitreous, macula, retina and their blood vessels. Many critical diseases, such as glaucoma and diabetic retinopathy, can be diagnosed by analyzing retinal fundus images. Retinal image-based glaucoma detection is a comprehensive diagnostic approach that examines the head cup-to-disc ratio (CDR) as an important indicator for detecting the presence and the extent of glaucoma in a patient. The accurate segmentations of the optic disc (OD) and optic cup (OC) are critical for the calculation of CDR. Machine learning based algorithms can be very helpful to efficiently exploit the vast amounts of retinal fundus data. In this thesis project, the main goal is to develop image processing and machine learning algorithms to automatically detect OD and OC from fundus images. This goal has been achieved by developing and applying several image enhancement techniques. First, an algorithm is proposed and tested on several fundus images to detect OD. The proposed algorithm is based on a combination of Contrast Limited Adaptive Histogram Equalization (CLAHE), Alternating Sequential Filters (ASF), thresholding, and Circular Hough Transform (CHT) methods. The results section highlights that the proposed algorithm is highly efficient in segmentation of OD from other parts of the fundus image. Several classification and modeling methods are studied in order to classify detected OD into OC and non-OC regions. In this thesis project three main ensemble modeling algorithms are studied to segment OC. The studied ensemble models are Random Forest, Gradient Boosting Machines (GBM), and Extreme Gradient Boosting Machines (XGBoost). The comparison between these models shows that they have more accurate results than conventional classification methods such as Logistic Regression (LR) or Support Vector Machines (SVM). This study shows that XGBoost is the fastest and most accurate approach to segment optic cup within the optic disc region.

scholarly journals Full Accuracy of Machine Learning for Differentiation between Optic Neuropathies and Pseudopapilledema

2019 ◽  
Author(s):  
Jin Mo Ahn ◽  
Sangsoo Kim ◽  
Kwang-Sung Ahn ◽  
Sung-Hoon Cho ◽  
Ungsoo Kim
Keyword(s):  
Machine Learning ◽  
Visual Acuity ◽  
Optic Disc ◽  
Optic Neuropathy ◽  
Characteristic Curve ◽  
Machine Learning Techniques ◽  
Fundus Photography ◽  
Ischemic Optic Neuropathy ◽  
Machine Learning Classifiers ◽  
Learning Classifiers

Abstract Background: This study is to evaluate the accuracy of machine learning for differentiation between optic neuropathies and pseudopapilledema (PPE). Methods: Two hundred and ninety-five images of optic neuropathies, 295 images of PPE, and 779 control images were used. Pseudopapilledema was defined as follows: cases with elevated optic nerve head and blurred disc margin, with normal visual acuity (>0.8 Snellen visual acuity), visual field, color vision, and pupillary reflex. The optic neuropathy group included cases of ischemic optic neuropathy (177), optic neuritis (48), diabetic optic neuropathy (17), papilledema (22), and retinal disorders (31). We compared four machine learning classifiers (our model, GoogleNet Inception v3, 19-layer Very Deep Convolution Network from Visual Geometry group (VGG), and 50-layer Deep Residual Learning (ResNet)). Accuracy and area under receiver operating characteristic curve (AUROC) were analyzed Results: The accuracy of machine learning classifiers ranged from 95.89% to 98.63% (our model: 95.89%, Inception V3: 96.45%, ResNet: 98.63%, and VGG: 96.80%). A high AUROC score was noted in both ResNet and VGG (0.999). Conclusions: Machine learning techniques can be combined with fundus photography as an effective approach to distinguish between PPE and elevated optic disc associated with optic neuropathies. Keywords: Machine Learning; Pseudopapilledema; Optic neuropathy; Optic disc swelling.

Neovascularization Detection on Optic Disc Region Using Deep Learning

2021 ◽  
pp. 111-120
Author(s):  
Cesar Carrillo-Gomez ◽  
Mariko Nakano ◽  
Ana Gonzalez-H.Leon ◽  
Juan Carlos Romo-Aguas ◽  
Hugo Quiroz-Mercado ◽  
...  
Keyword(s):  
Deep Learning ◽  
Optic Disc ◽  
Disc Region

Automatic detection of optic disc using distance regularized level-set segmentation for glaucoma screening system

2021 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Naganagouda Patil ◽  
Preethi N. Patil ◽  
P.V. Rao
Keyword(s):  
Optic Disc ◽  
Level Set ◽  
Retinal Image ◽  
Retinal Images ◽  
World Population ◽  
Human Beings ◽  
Content Type ◽  
Glaucoma Screening ◽  
Disc Region ◽  
Existing Result

PurposeThe abnormalities of glaucoma have high impact on deciding and representing the causes that effects severity of blindness in human beings. The simulation experimental results would help the ophthalmologist in diagnosing of glaucoma abnormality accurately. The significant effect of glaucoma has a huge impact on the quality of human life, and its growth rate in world population tremendously increases. Glaucoma is considered as second largest cause for the blindness in the world; hence identification of it marks the importance of its detection at the earliest.Design/methodology/approachThe prime objective of the work proposed is to build up a human intervention free image preparing framework for glaucoma screening. The disc calculation is assessed on retinal image dataset called retinal Image for glaucoma Analysis. The proposed method briefs a novel optic disc division calculation depending on applying a level-set strategy on a confined optic disc image. In the instance of low quality image, a twofold level set is designed, in which the principal level set is viewed as restriction for the optic disc. To keep the veins from meddling with the level-set procedure, an inpainting strategy has been applied. Also a significant commitment is to include the varieties in notion adopted by the ophthalmologists in distinguishing the disc localization and diagnosing the glaucoma. Most of the past investigations are prepared and tested depending on just a single feature, which can be thought to be one-sided for the ophthalmologist.FindingsIn continuation, the correctness has been determined depending on the quantity of image that matched with the investigation pattern adopted by the ophthalmologist. The 175 retinal images were utilized to test the results of proposed work with the manual markings of ophthalmologists. The error-free calculation in marking the optic disc region and centroid was 98.95% in comparison with the existing result of 87.34%.Originality/valueIn continuation, the correctness has been determined depending on the quantity of image that matched with the investigation pattern adopted by the ophthalmologist. The 175 retinal images were utilized to test the results of proposed work with the manual markings of ophthalmologists. The error-free calculation in marking the optic disc region and centroid was 98.95% in comparison with the existing result of 87.34%.

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