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Ήλ΄μ₯ μ§λ¨ μ±λ₯μ κΈ°μ μ μ΄κ³ μμμ μΈ μ§νλ€μ ν΅ν΄ κ²μ¦λμλ€.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