482 research outputs found
Clinical Applications of Artificial Intelligence in Glaucoma
Ophthalmology is one of the major imaging-intensive fields of medicine and thus has potential for extensive applications of artificial intelligence (AI) to advance diagnosis, drug efficacy, and other treatment-related aspects of ocular disease. AI has made impressive progress in ophthalmology within the past few years and two autonomous AIenabled systems have received US regulatory approvals for autonomously screening for mid-level or advanced diabetic retinopathy and macular edema. While no autonomous AI-enabled system for glaucoma screening has yet received US regulatory approval, numerous assistive AI-enabled software tools are already employed in commercialized instruments for quantifying retinal images and visual fields to augment glaucoma research and clinical practice. In this literature review (non-systematic), we provide an overview of AI applications in glaucoma, and highlight some limitations and considerations for AI integration and adoption into clinical practice
Image analysis algorithms for feature extraction in eye fundus images
Retinal images are widely used for diagnostic purposes by ophthalmolo-
gists. Therefore, these images are suitable for digital image analysis for their visual
enhancement and pathological risk or damage detection. Here, we implement a lu-
minosity and contrast enhancement technique based on domain knowledge. We also
review and analyze a previous approach in optic nerve head segmentation to extend its
applicability to non circular shaped contours. We introduce a di erent strategy based on the use of active contours
The Impact of Artificial Intelligence and Deep Learning in Eye Diseases: A Review
Artificial intelligence (AI) is a subset of computer science dealing with the development and training of algorithms that try to replicate human intelligence. We report a clinical overview of the basic principles of AI that are fundamental to appreciating its application to ophthalmology practice. Here, we review the most common eye diseases, focusing on some of the potential challenges and limitations emerging with the development and application of this new technology into ophthalmology
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Computational models for stuctural analysis of retinal images
This thesis was submitted for the degree of Doctor of Philosophy and awarded by Brunel University LondonThe evaluation of retina structures has been of great interest because it could be used as a non-intrusive diagnosis in modern ophthalmology to detect many important eye diseases as well as cardiovascular disorders. A variety of retinal image analysis tools have been developed to assist ophthalmologists and eye diseases experts by reducing the time required in eye screening, optimising the costs as well as providing efficient disease treatment and management systems. A key component in these tools is the segmentation and quantification of retina structures. However, the imaging artefacts
such as noise, intensity homogeneity and the overlapping tissue of retina structures can cause significant degradations to the performance of these automated image analysis tools. This thesis aims to provide robust and reliable automated retinal image analysis
technique to allow for early detection of various retinal and other diseases. In particular, four innovative segmentation methods have been proposed, including two for retinal vessel network segmentation, two for optic disc segmentation and one for retina nerve fibre layers detection. First, three pre-processing operations are combined in
the segmentation method to remove noise and enhance the appearance of the blood vessel in the image, and a Mixture of Gaussians is used to extract the blood vessel tree. Second, a graph cut segmentation approach is introduced, which incorporates the
mechanism of vectors flux into the graph formulation to allow for the segmentation of very narrow blood vessels. Third, the optic disc segmentation is performed using two alternative methods: the Markov random field image reconstruction approach detects the optic disc by removing the blood vessels from the optic disc area, and the graph cut
with compensation factor method achieves that using prior information of the blood vessels. Fourth, the boundaries of the retinal nerve fibre layer (RNFL) are detected by adapting a graph cut segmentation technique that includes a kernel-induced space and a continuous multiplier based max-flow algorithm. The strong experimental results
of our retinal blood vessel segmentation methods including Mixture of Gaussian, Graph Cut achieved an average accuracy of 94:33%, 94:27% respectively. Our optic disc segmentation methods including Markov Random Field and Compensation Factor also achieved an average sensitivity of 92:85% and 85:70% respectively. These results
obtained on several public datasets and compared with existing methods have shown that our proposed methods are robust and efficient in the segmenting retinal structures such the blood vessels and the optic disc.Brunel University Londonhttp://bura.brunel.ac.uk/bitstream/2438/10387/1/FulltextThesis.pd
REFUGE Challenge: A unified framework for evaluating automated methods for glaucoma assessment from fundus photographs
[EN] Glaucoma is one of the leading causes of irreversible but preventable blindness in working age populations. Color fundus photography (CFP) is the most cost-effective imaging modality to screen for retinal disorders. However, its application to glaucoma has been limited to the computation of a few related biomarkers such as the vertical cup-to-disc ratio. Deep learning approaches, although widely applied for medical image analysis, have not been extensively used for glaucoma assessment due to the limited size of the available data sets. Furthermore, the lack of a standardize benchmark strategy makes difficult to compare existing methods in a uniform way. In order to overcome these issues we set up the Retinal Fundus Glaucoma Challenge, REFUGE (https://refuge.grand-challenge.org), held in conjunction with MIC-CAI 2018. The challenge consisted of two primary tasks, namely optic disc/cup segmentation and glaucoma classification. As part of REFUGE, we have publicly released a data set of 1200 fundus images with ground truth segmentations and clinical glaucoma labels, currently the largest existing one. We have also built an evaluation framework to ease and ensure fairness in the comparison of different models, encouraging the development of novel techniques in the field. 12 teams qualified and participated in the online challenge. This paper summarizes their methods and analyzes their corresponding results. In particular, we observed that two of the top-ranked teams outperformed two human experts in the glaucoma classification task. Furthermore, the segmentation results were in general consistent with the ground truth annotations, with complementary outcomes that can be further exploited by ensembling the results.This work was supported by the Christian Doppler Research Association, the Austrian Federal Ministry for Digital and Economic
Affairs and the National Foundation for Research, Technology and
Development, J.I.O is supported by WWTF (Medical University of
Vienna: AugUniWien/FA7464A0249, University of Vienna: VRG12-
009). Team Masker is supported by Natural Science Foundation of
Guangdong Province of China (Grant 2017A030310647). Team BUCT
is partially supported by the National Natural Science Foundation
of China (Grant 11571031). The authors would also like to thank
REFUGE study group for collaborating with this challenge.Orlando, JI.; Fu, H.; Breda, JB.; Van Keer, K.; Bathula, DR.; Diaz-Pinto, A.; Fang, R.... (2020). REFUGE Challenge: A unified framework for evaluating automated methods for glaucoma assessment from fundus photographs. Medical Image Analysis. 59:1-21. https://doi.org/10.1016/j.media.2019.101570S12159Abramoff, M. D., Garvin, M. K., & Sonka, M. (2010). Retinal Imaging and Image Analysis. IEEE Reviews in Biomedical Engineering, 3, 169-208. doi:10.1109/rbme.2010.2084567Abrร moff, M. D., Lavin, P. T., Birch, M., Shah, N., & Folk, J. C. (2018). Pivotal trial of an autonomous AI-based diagnostic system for detection of diabetic retinopathy in primary care offices. npj Digital Medicine, 1(1). doi:10.1038/s41746-018-0040-6Al-Bander, B., Williams, B., Al-Nuaimy, W., Al-Taee, M., Pratt, H., & Zheng, Y. (2018). Dense Fully Convolutional Segmentation of the Optic Disc and Cup in Colour Fundus for Glaucoma Diagnosis. 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Dual Machine-Learning system to aid Glaucoma Diagnosis using disc and cup feature extraction.
Glaucoma is a degenerative disease that affects vision, causing damage to the optic nerve
that ends in vision loss. The classic techniques to detect it have undergone a great change since the intrusion
of machine learning techniques into the processing of eye fundus images. Several works focus on training
a convolutional neural network (CNN) by brute force, while others use segmentation and feature extraction
techniques to detect glaucoma. In this work, a diagnostic aid tool to detect glaucoma using eye fundus
images is developed, trained and tested. It consists of two subsystems that are independently trained and
tested, combining their results to improve glaucoma detection. The first subsystem applies machine learning
and segmentation techniques to detect optic disc and cup independently, combine them and extract their
physical and positional features. The second one applies transfer learning techniques to a pre-trained CNN
to detect glaucoma through the analysis of the complete eye fundus images. The results of both systems are
combined to discriminate positive cases of glaucoma and improve final detection. The results show that this
system achieves a higher classification rate than previous works. The system also provides information on
the basis for the proposed diagnosis suggestion that can help the ophthalmologist to accept or modify it
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๋ ฅํฉ์ฑ๊ณฑ์ ๊ฒฝ๋ง์ ์กฐ๊ธฐ ๋
น๋ด์ฅ์ ๋ถ๋ฅํ๋๋ฐ 0.869์ ์์ ๊ธฐ๋์ํน์ฑ๊ณก์ ์๋์ด์ 0.921์ ๋ฏผ๊ฐ๋, 0.756์ ํน์ด๋๋ฅผ ๋ณด์๋ค.
๋๋ฒ์งธ ์ฐ๊ตฌ์์๋ ๋ฅ ๋ฌ๋์ ์ด์ฉํด ์์ ๊ฒฝ์ ๋์ฌ์ง์ ํด์๋์ ๋๋น, ์๊ฐ, ๋ฐ๊ธฐ๋ฅผ ๋ณด์ ํ๋ ๋ฐฉ๋ฒ์ ์ ์ํ์๋ค. ์์ ๊ฒฝ์ ๋์ฌ์ง์ ๋
น๋ด์ฅ์ ์ง๋จํ๋๋ฐ ์์ด ํจ๊ณผ์ ์ธ ๊ฒ์ผ๋ก ์๋ ค์ ธ ์๋ค. ํ์ง๋ง, ๋
น๋ด์ฅ์ ์ง๋จ์์ ํ์์ ๋, ์์ ๋๊ณต, ๋งค์ฒด ๋ถํฌ๋ช
์ฑ ๋ฑ์ผ๋ก ์ธํด ํ๊ฐ๊ฐ ์ด๋ ค์ด ๊ฒฝ์ฐ๊ฐ ์๋ค. ์ด ํด์๋์ ๋ณด์ ์๊ณ ๋ฆฌ์ฆ์ ์ด ํด์๋ ์ ๋์ ์์ฑ์ ๊ฒฝ๋ง์ ํตํด ๊ฐ๋ฐ๋์๋ค. ์๋ณธ ๊ณ ํด์๋์ ์์ ๊ฒฝ ์ ๋ ์ฌ์ง์ ์ ํด์๋ ์ฌ์ง์ผ๋ก ์ถ์๋๊ณ , ๋ณด์ ๋ ๊ณ ํด์๋ ์์ ๊ฒฝ์ ๋์ฌ์ง์ผ๋ก ๋ณด์ ๋๋ฉฐ, ๋ณด์ ๋ ์ฌ์ง์ ์์ ๊ฒฝ์ฌ๋ฐฑ์ ๊ฐ์์ฑ๊ณผ ๊ทผ์ฒ ํ๊ด์ ์ ๋ณด์ด๋๋ก ํ์ฒ๋ฆฌ ์๊ณ ๋ฆฌ์ฆ์ ์ด์ฉํ๋ค. ์ ํด์๋์ด๋ฏธ์ง๋ฅผ ๋ณด์ ๋ ๊ณ ํด์๋์ด๋ฏธ์ง๋ก ๋ณต์ํ๋ ๊ณผ์ ์ ์ดํด์๋์ ๋์ ์ ๊ฒฝ๋ง์ ํตํด ํ์ตํ๋ค. ์ค๊ณํ ๋คํธ์ํฌ๋ ์ ํธ ๋ ์ก์ ๋น(PSNR)๊ณผ ๊ตฌ์กฐ์ ์ ์ฌ์ฑ(SSIM), ํ๊ท ํ๊ฐ์ (MOS)๋ฅผ ์ด์ฉํด ํ๊ฐ ๋์๋ค. ํ์ฌ์ ์ฐ๊ตฌ๋ ๋ฅ ๋ฌ๋์ด ์๊ณผ ์ด๋ฏธ์ง๋ฅผ 4๋ฐฐ ํด์๋์ ๊ตฌ์กฐ์ ์ธ ์ธ๋ถ ํญ๋ชฉ์ด ์ ๋ณด์ด๋๋ก ๊ฐ์ ํ ์ ์๋ค๋ ๊ฒ์ ๋ณด์ฌ์ฃผ์๋ค. ํฅ์๋ ์์ ๊ฒฝ์ ๋ ์ฌ์ง์ ์์ ๊ฒฝ์ ๋ณ๋ฆฌํ์ ์ธ ํน์ฑ์ ์ง๋จ ์ ํ๋๋ฅผ ๋ช
ํํ ํฅ์์ํจ๋ค. ์ฑ๋ฅํ๊ฐ๊ฒฐ๊ณผ ํ๊ท PSNR์ 25.01 SSIM์ 0.75 MOS๋ 4.33์ผ๋ก ๋ํ๋ฌ๋ค.
์ธ๋ฒ์งธ ์ฐ๊ตฌ์์๋ ํ์ ์ ๋ณด์ ์๊ณผ ์์(์์ ๊ฒฝ์ ๋ ์ฌ์ง๊ณผ ๋ถ์์์ด ์๋ ๋ง๋ง์ ๊ฒฝ์ฌ์ ์ธต ์ฌ์ง)์ ์ด์ฉํด ๋
น๋ด์ฅ ์์ฌ ํ์๋ฅผ ๋ถ๋ณํ๊ณ ๋
น๋ด์ฅ ์์ฌ ํ์์ ๋ฐ๋ณ ์ฐ์๋ฅผ ์์ธกํ๋ ๋ฅ ๋ฌ๋ ๋ชจ๋ธ์ ๊ฐ๋ฐํ์๋ค. ์์ ๋ฐ์ดํฐ๋ค์ ๋
น๋ด์ฅ์ ์ง๋จํ๊ฑฐ๋ ์์ธกํ๋๋ฐ ์ ์ฉํ ์ ๋ณด๋ค์ ๊ฐ์ง๊ณ ์๋ค. ํ์ง๋ง, ์ด๋ป๊ฒ ๋ค์ํ ์ ํ์ ์์์ ๋ณด๋ค์ ์กฐํฉํ๋ ๊ฒ์ด ๊ฐ๊ฐ์ ํ์๋ค์ ๋ํด ์ ์ฌ์ ์ธ ๋
น๋ด์ฅ์ ์์ธกํ๋๋ฐ ์ด๋ค ์ํฅ์ ์ฃผ๋์ง์ ๋ํ ์ฐ๊ตฌ๊ฐ ์งํ ๋ ์ ์ด ์๋ค. ๋
น๋ด์ฅ ์ ์ฌ์ ๋ถ๋ฅ์ ๋ฐ๋ณ ๋
์ ์์ธก์ ํฉ์ฑ ๊ณฑ ์๋ ์ธ์ฝ๋(CAE)๋ฅผ ๋น ์ง๋์ ํน์ฑ ์ถ์ถ ๊ธฐ๋ก ์ฌ์ฉํ๊ณ , ๊ธฐ๊ณํ์ต ๋ถ๋ฅ ๊ธฐ์ ํ๊ท๊ธฐ๋ฅผ ํตํด ์งํํ์๋ค. ์ค๊ณํ ๋ชจ๋ธ์ ์ ํ๋์ ํ๊ท ์ ๊ณฑ์ค์ฐจ(MSE)๋ฅผ ํตํด ํ๊ฐ ๋์์ผ๋ฉฐ, ์ด๋ฏธ์ง ํน์ง๊ณผ ํ์ ํน์ง์ ์กฐํฉํ์ ๋ ๋
น๋ด์ฅ ์์ฌ ํ์ ๋ถ๋ฅ์ ๋ฐ๋ณ ๋
์ ์์ธก์ ์ฑ๋ฅ์ด ์ด๋ฏธ์ง ํน์ง๊ณผ ํ์ ํน์ง์ ๊ฐ๊ฐ ์ผ์ ๋๋ณด๋ค ์ฑ๋ฅ์ด ์ข์๋ค. ์ ๋ต๊ณผ์ MSE๋ 2.613์ผ๋ก ๋ํ๋ฌ๋ค.
๋ณธ ์ฐ๊ตฌ์์๋ ๋ฅ ๋ฌ๋์ ์ด์ฉํด ๋
น๋ด์ฅ ๊ด๋ จ ์์ ๋ฐ์ดํฐ ์ค ๋ง๋ง์ ๊ฒฝ์ฌ์ ์ธต, ์ ๊ฒฝ์ ์ธํฌ์ธต ์ฌ์ง์ ๋
น๋ด์ฅ ์ง๋จ์ ์ด์ฉ๋์๊ณ , ์์ ๊ฒฝ์ ๋ ์ฌ์ง์ ์์ ๊ฒฝ์ ๋ณ๋ฆฌํ์ ์ธ ์ง๋จ ์ ํ๋๋ฅผ ๋์๊ณ , ํ์ ์ ๋ณด๋ ๋ณด๋ค ์ ํํ ๋
น๋ด์ฅ ์์ฌ ํ์ ๋ถ๋ฅ์ ๋ฐ๋ณ ๋
์ ์์ธก์ ์ด์ฉ๋์๋ค. ํฅ์๋ ๋
น๋ด์ฅ ์ง๋จ ์ฑ๋ฅ์ ๊ธฐ์ ์ ์ด๊ณ ์์์ ์ธ ์งํ๋ค์ ํตํด ๊ฒ์ฆ๋์๋ค.This paper presents deep learning-based methods for improving glaucoma diagnosis support systems. Novel methods were applied to glaucoma clinical cases and the results were evaluated.
In the first study, a deep learning classifier for glaucoma diagnosis based on spectral-domain optical coherence tomography (SD-OCT) images was proposed and evaluated. Spectral-domain optical coherence tomography (SD-OCT) is commonly employed as an imaging modality for the evaluation of glaucomatous structural damage. The classification model was developed using convolutional neural network (CNN) as a base, and was trained with SD-OCT retinal nerve fiber layer (RNFL) and macular ganglion cell-inner plexiform layer (GCIPL) images. The proposed network architecture, termed Dual-Input Convolutional Neural Network (DICNN), showed great potential as an effective classification algorithm based on two input images. DICNN was trained with both RNFL and GCIPL thickness maps that enabled it to discriminate between normal and glaucomatous eyes. The performance of the proposed DICNN was evaluated with accuracy and area under the receiver operating characteristic curve (AUC), and was compared to other methods using these metrics. Compared to other methods, the proposed DICNN model demonstrated high diagnostic ability for the discrimination of early-stage glaucoma patients in normal subjects. AUC, sensitivity and specificity was 0.869, 0.921, 0.756 respectively.
In the second study, a deep-learning method for increasing the resolution and improving the legibility of Optic-disc Photography(ODP) was proposed. ODP has been proven to be useful for optic nerve evaluation in glaucoma. But in clinical practice, limited patient cooperation, small pupil or media opacities can limit the performance of ODP. A model to enhance the resolution of ODP images, termed super-resolution, was developed using Super Resolution Generative Adversarial Network(SR-GAN). To train this model, high-resolution original ODP images were transformed into two counterparts: (1) down-scaled low-resolution ODPs, and (2) compensated high-resolution ODPs with enhanced visibility of the optic disc margin and surrounding retinal vessels which were produced using a customized image post-processing algorithm. The SR-GAN was trained to learn and recognize the differences between these two counterparts. The performance of the network was evaluated using Peak Signal to Noise Ratio (PSNR), Structural Similarity (SSIM), and Mean Opinion Score (MOS). The proposed study demonstrated that deep learning can be applied to create a generative model that is capable of producing enhanced ophthalmic images with 4x resolution and with improved structural details. The proposed method can be used to enhance ODPs and thereby significantly increase the detection accuracy of optic disc pathology. The average PSNR, SSIM and MOS was 25.01, 0.75, 4.33 respectively
In the third study, a deep-learning model was used to classify suspected glaucoma and to predict subsequent glaucoma onset-year in glaucoma suspects using clinical data and retinal images (ODP & Red-free Fundus RNFL Photo). Clinical data contains useful information about glaucoma diagnosis and prediction. However, no study has been undertaken to investigate how combining different types of clinical information would be helpful for predicting the subsequent course of glaucoma in an individual patient. For this study, image features extracted using Convolutional Auto Encoder (CAE) along with clinical features were used for glaucoma suspect classification and onset-year prediction. The performance of the proposed model was evaluated using accuracy and Mean Squared Error (MSE). Combing the CAE extracted image features and clinical features improved glaucoma suspect classification and on-set year prediction performance as compared to using the image features and patient features separately. The average MSE between onset-year and predicted onset year was 2.613
In this study, deep learning methodology was applied to clinical images related to glaucoma. DICNN with RNFL and GCIPL images were used for classification of glaucoma, SR-GAN with ODP images were used to increase detection accuracy of optic disc pathology, and CAE & machine learning algorithm with clinical data and retinal images was used for glaucoma suspect classification and onset-year predication. The improved glaucoma diagnosis performance was validated using both technical and clinical parameters. The proposed methods as a whole can significantly improve outcomes of glaucoma patients by early detection, prediction and enhancing detection accuracy.Contents
Abstract i
Contents iv
List of Tables vii
List of Figures viii
Chapter 1 General Introduction 1
1.1 Glaucoma 1
1.2 Deep Learning for Glaucoma Diagnosis 3
1.4 Thesis Objectives 3
Chapter 2 Dual-Input Convolutional Neural Network for Glaucoma Diagnosis using Spectral-Domain Optical Coherence Tomography 6
2.1 Introduction 6
2.1.1 Background 6
2.1.2 Related Work 7
2.2 Methods 8
2.2.1 Study Design 8
2.2.2 Dataset 9
2.2.3 Dual-Input Convolutional Neural Network (DICNN) 15
2.2.4 Training Environment 18
2.2.5 Statistical Analysis 19
2.3 Results 20
2.3.1 DICNN Performance 20
2.3.1 Grad-CAM for DICNN 34
2.4 Discussion 37
2.4.1 Research Significance 37
2.4.2 Limitations 40
2.5 Conclusion 42
Chapter 3 Deep-learning-based enhanced optic-disc photography 43
3.1 Introduction 43
3.1.1 Background 43
3.1.2 Needs 44
3.1.3 Related Work 45
3.2 Methods 46
3.2.1 Study Design 46
3.2.2 Dataset 46
3.2.2.1 Details on Customized Image Post-Processing Algorithm 47
3.2.3 SR-GAN Network 50
3.2.3.1 Design of Generative Adversarial Network 50
3.2.3.2 Loss Functions 55
3.2.4 Assessment of Clinical Implications of Enhanced ODPs 58
3.2.5 Statistical Analysis 60
3.2.6 Hardware Specifications & Software Specifications 60
3.3 Results 62
3.3.1 Training Loss of Modified SR-GAN 62
3.3.2 Performance of Final Network 66
3.3.3 Clinical Validation of Enhanced ODP by MOS comparison 77
3.3.4 Comparison of DH-Detection Accuracy 79
3.4 Discussion 80
3.4.1 Research Significance 80
3.4.2 Limitations 85
3.5 Conclusion 88
Chapter 4 Deep Learning Based Prediction of Glaucoma Onset Using Retinal Image and Patient Data 89
4.1 Introduction 89
4.1.1 Background 89
4.1.2 Related Work 90
4.2 Methods 90
4.2.1 Study Design 90
4.2.2 Dataset 91
4.2.3 Design of Overall System 94
4.2.4 Design of Convolutional Auto Encoder 95
4.2.5 Glaucoma Suspect Classification 97
4.2.6 Glaucoma Onset-Year Prediction 97
4.3 Result 99
4.3.1 Performance of Designed CAE 99
4.3.2 Performance of Designed Glaucoma Suspect Classification 101
4.3.3 Performance of Designed Glaucoma Onset-Year Prediction 105
4.4 Discussion 110
4.4.1 Research Significance 110
4.4.2 Limitations 110
4.5 Conclusion 111
Chapter 5 Summary and Future Works 112
5.1 Thesis Summary 112
5.2 Limitations and Future Works 113
Bibliography 115
Abstract in Korean 127
Acknowledgement 130Docto
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