Three-Dimensional Spectral-Domain Optical Coherence Tomography Data Analysis for Glaucoma Detection

Purpose: To develop a new three-dimensional (3D) spectral-domain optical coherence tomography (SD-OCT) data analysis method using a machine learning technique based on variable-size super pixel segmentation that efficiently utilizes full 3D dataset to improve the discrimination between early glaucomatous and healthy eyes. Methods: 192 eyes of 96 subjects (44 healthy, 59 glaucoma suspect and 89 glaucomatous eyes) were scanned with SD-OCT. Each SD-OCT cube dataset was first converted into 2D feature map based on retinal nerve fiber layer (RNFL) segmentation and then divided into various number of super pixels. Unlike the conventional super pixel having a fixed number of points, this newly developed variable-size super pixel is defined as a cluster of homogeneous adjacent pixels with variable size, shape and number. Features of super pixel map were extracted and used as inputs to machine classifier (LogitBoost adaptive boosting) to automatically identify diseased eyes. For discriminating performance assessment, area under the curve (AUC) of the receiver operating characteristics of the machine classifier outputs were compared with the conventional circumpapillary RNFL (cpRNFL) thickness measurements. Results: The super pixel analysis showed statistically significantly higher AUC than the cpRNFL (0.855 vs. 0.707, respectively, p = 0.031, Jackknife test) when glaucoma suspects were discriminated from healthy, while no significant difference was found when confirmed glaucoma eyes were discriminated from healthy eyes. Conclusions: A novel 3D OCT analysis technique performed at least as well as the cpRNFL in glaucoma discrimination and even better at glaucoma suspect discrimination. This new method has the potential to improve early detection of glaucomatous damage. © 2013 Xu et al

Image database system for glaucoma diagnosis support

Tato práce popisuje přehled standardních a pokročilých metod používaných k diagnose glaukomu v ranném stádiu. Na základě teoretických poznatků je implementován internetově orientovaný informační systém pro oční lékaře, který má tři hlavní cíle. Prvním cílem je možnost sdílení osobních dat konkrétního pacienta bez nutnosti posílat tato data internetem. Druhým cílem je vytvořit účet pacienta založený na kompletním očním vyšetření. Posledním cílem je aplikovat algoritmus pro registraci intenzitního a barevného fundus obrazu a na jeho základě vytvořit internetově orientovanou tři-dimenzionální vizualizaci optického disku. Tato práce je součásti DAAD spolupráce mezi Ústavem Biomedicínského Inženýrství, Vysokého Učení Technického v Brně, Oční klinikou v Erlangenu a Ústavem Informačních Technologií, Friedrich-Alexander University, Erlangen-Nurnberg.This master thesis describes a conception of standard and advanced eye examination methods used for glaucoma diagnosis in its early stage. According to the theoretical knowledge, a web based information system for ophthalmologists with three main aims is implemented. The first aim is the possibility to share medical data of a concrete patient without sending his personal data through the Internet. The second aim is to create a patient account based on a complete eye examination procedure. The last aim is to improve the HRT diagnostic method with an image registration algorithm for the fundus and intensity images and create an optic nerve head web based 3D visualization. This master thesis is a part of project based on DAAD co-operation between Department of Biomedical Engineering, Brno University of Technology, Eye Clinic in Erlangen and Department of Computer Science, Friedrich-Alexander University, Erlangen-Nurnberg.

Frequency-aware optical coherence tomography image super-resolution via conditional generative adversarial neural network

Optical coherence tomography (OCT) has stimulated a wide range of medical image-based diagnosis and treatment in fields such as cardiology and ophthalmology. Such applications can be further facilitated by deep learning-based super-resolution technology, which improves the capability of resolving morphological structures. However, existing deep learning-based method only focuses on spatial distribution and disregard frequency fidelity in image reconstruction, leading to a frequency bias. To overcome this limitation, we propose a frequency-aware super-resolution framework that integrates three critical frequency-based modules (i.e., frequency transformation, frequency skip connection, and frequency alignment) and frequency-based loss function into a conditional generative adversarial network (cGAN). We conducted a large-scale quantitative study from an existing coronary OCT dataset to demonstrate the superiority of our proposed framework over existing deep learning frameworks. In addition, we confirmed the generalizability of our framework by applying it to fish corneal images and rat retinal images, demonstrating its capability to super-resolve morphological details in eye imaging.Comment: 13 pages, 7 figures, submitted to Biomedical Optics Express special issu

Optical Coherence Tomography – Variations on a Theme

This paper was presented at the 4th Micro and Nano Flows Conference (MNF2014), which was held at University College, London, UK. The conference was organised by Brunel University and supported by the Italian Union of Thermofluiddynamics, IPEM, the Process Intensification Network, the Institution of Mechanical Engineers, the Heat Transfer Society, HEXAG - the Heat Exchange Action Group, and the Energy Institute, ASME Press, LCN London Centre for Nanotechnology, UCL University College London, UCL Engineering, the International NanoScience Community, www.nanopaprika.eu.Optical Coherence Tomography (OCT) has developed extensively over the last 23 years. This paper reviews some of the imaging techniques based on OCT with particular reference to the trade-offs between lateral and axial resolution, working distance, imaging depth, acquisition speed (enabling real time observation and 3D imaging), imaged area/volume, contrast enhancement – including velocity measurement, and system complexity – including detectors, light sources and the optical path. The majority of applications of OCT are biomedical, especially ophthalmology, endoscopy and intravascular imaging. However, some industrial applications are emerging particularly for non-destructive testing and quality control, such as in the production of MEMS devices, or the non-destructive detection of sub-surface strain fields in injected moulded polymer parts

An In-Depth Statistical Review of Retinal Image Processing Models from a Clinical Perspective

The burgeoning field of retinal image processing is critical in facilitating early diagnosis and treatment of retinal diseases, which are amongst the leading causes of vision impairment globally. Despite rapid advancements, existing machine learning models for retinal image processing are characterized by significant limitations, including disparities in pre-processing, segmentation, and classification methodologies, as well as inconsistencies in post-processing operations. These limitations hinder the realization of accurate, reliable, and clinically relevant outcomes. This paper provides an in-depth statistical review of extant machine learning models used in retinal image processing, meticulously comparing them based on their internal operating characteristics and performance levels. By adopting a robust analytical approach, our review delineates the strengths and weaknesses of current models, offering comprehensive insights that are instrumental in guiding future research and development in this domain. Furthermore, this review underscores the potential clinical impacts of these models, highlighting their pivotal role in enhancing diagnostic accuracy, prognostic assessments, and therapeutic interventions for retinal disorders. In conclusion, our work not only bridges the existing knowledge gap in the literature but also paves the way for the evolution of more sophisticated and clinically-aligned retinal image processing models, ultimately contributing to improved patient outcomes and advancements in ophthalmic care

딥러닝을 이용한 녹내장 진단 보조 시스템

학위논문 (박사) -- 서울대학교 대학원 : 공과대학 협동과정 바이오엔지니어링전공, 2021. 2. 김희찬.본 논문에서는 딥 러닝 기반의 진단 보조 시스템을 제안하였다. 새로운 방법이 녹내장 데이터에 적용되었고 결과를 평가하였다. 첫번째 연구에서는 스펙트럼영역 빛간섭단층촬영기(SD-OCT)를 딥 러닝 분류 기를 이용해 분석하였다. 스펙트럼영역 빛간섭단층촬영기는 녹내장으로 인한 구조적 손상을 평가하기 위해 사용하는 장비이다. 분류 알고리즘은 합성 곱 신경망을 이용해 개발 되었으며, 스펙트럼영역 빛간섭단층촬영기의 망막신경섬유층(RNFL)과 황반부 신경절세포내망상층 (GCIPL) 사진을 이용해 학습했다. 제안한 방법은 두개의 이미지를 입력으로 받는 이중입력합성곱신경망(DICNN)이며, 딥 러닝 분류에서 효과적인 것으로 알려져 있다. 이중입력합성곱신경망은 망막신경섬유층 과 신경절세포층 의 두께 지도를 이용하여 학습 됐으며, 학습된 네트워크는 녹내장과 정상 군을 구분한다. 이중입력합성곱신경망은 정확도와 수신기동작특성곡선하면적 (AUC)으로 평가 되었다. 망막신경섬유층과 신경절세포층 두께 지도로 학습된 설계한 딥 러닝 모델을 조기 녹내장과 정상 군을 분류하는 성능을 평가하고 비교하였다. 성능평가 결과 이중입력합성곱신경망은 조기 녹내장을 분류하는데 0.869의 수신기동작특성곡선의넓이와 0.921의 민감도, 0.756의 특이도를 보였다. 두번째 연구에서는 딥 러닝을 이용해 시신경유두사진의 해상도와 대비, 색감, 밝기를 보정하는 방법을 제안하였다. 시신경유두사진은 녹내장을 진단하는데 있어 효과적인 것으로 알려져 있다. 하지만, 녹내장의 진단에서 환자의 나, 작은 동공, 매체 불투명성 등으로 인해 평가가 어려운 경우가 있다. 초 해상도와 보정 알고리즘은 초 해상도 적대적생성신경망을 통해 개발되었다. 원본 고해상도의 시신경 유두 사진은 저해상도 사진으로 축소되고, 보정된 고해상도 시신경유두사진으로 보정 되며, 보정된 사진은 시신경여백의 가시성과 근처 혈관을 잘 보이도록 후처리 알고리즘을 이용한다. 저해상도이미지를 보정된 고해상도이미지로 복원하는 과정을 초해상도적대적신경망을 통해 학습한다. 설계한 네트워크는 신호 대 잡음 비(PSNR)과 구조적유사성(SSIM), 평균평가점(MOS)를 이용해 평가 되었다. 현재의 연구는 딥 러닝이 안과 이미지를 4배 해상도와 구조적인 세부 항목이 잘 보이도록 개선할 수 있다는 것을 보여주었다. 향상된 시신경유두 사진은 시신경의 병리학적인 특성의 진단 정확도를 명확히 향상시킨다. 성능평가결과 평균 PSNR은 25.01 SSIM은 0.75 MOS는 4.33으로 나타났다. 세번째 연구에서는 환자 정보와 안과 영상(시신경유두 사진과 붉은색이 없는 망막신경섬유층 사진)을 이용해 녹내장 의심 환자를 분별하고 녹내장 의심 환자의 발병 연수를 예측하는 딥 러닝 모델을 개발하였다. 임상 데이터들은 녹내장을 진단하거나 예측하는데 유용한 정보들을 가지고 있다. 하지만, 어떻게 다양한 유형의 임상정보들을 조합하는 것이 각각의 환자들에 대해 잠재적인 녹내장을 예측하는데 어떤 영향을 주는지에 대한 연구가 진행 된 적이 없다. 녹내장 의 심자 분류와 발병 년 수 예측은 합성 곱 자동 인코더(CAE)를 비 지도적 특성 추출 기로 사용하고, 기계학습 분류 기와 회귀기를 통해 진행하였다. 설계한 모델은 정확도와 평균제곱오차(MSE)를 통해 평가 되었으며, 이미지 특징과 환자 특징은 조합했을 때 녹내장 의심 환자 분류와 발병 년 수 예측의 성능이 이미지 특징과 환자 특징을 각각 썼을 때보다 성능이 좋았다. 정답과의 MSE는 2.613으로 나타났다. 본 연구에서는 딥 러닝을 이용해 녹내장 관련 임상 데이터 중 망막신경섬유층, 신경절세포층 사진을 녹내장 진단에 이용되었고, 시신경유두 사진은 시신경의 병리학적인 진단 정확도를 높였고, 환자 정보는 보다 정확한 녹내장 의심 환자 분류와 발병 년 수 예측에 이용되었다. 향상된 녹내장 진단 성능은 기술적이고 임상적인 지표들을 통해 검증되었다.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

Multidimensional en-face OCT imaging of the retina.

Fast T-scanning (transverse scanning, en-face) was used to build B-scan or C-scan optical coherence tomography (OCT) images of the retina. Several unique signature patterns of en-face (coronal) are reviewed in conjunction with associated confocal images of the fundus and B-scan OCT images. Benefits in combining T-scan OCT with confocal imaging to generate pairs of OCT and confocal images similar to those generated by scanning laser ophthalmoscopy (SLO) are discussed in comparison with the spectral OCT systems. The multichannel potential of the OCT/SLO system is demonstrated with the addition of a third hardware channel which acquires and generates indocyanine green (ICG) fluorescence images. The OCT, confocal SLO and ICG fluorescence images are simultaneously presented in a two or a three screen format. A fourth channel which displays a live mix of frames of the ICG sequence superimposed on the corresponding coronal OCT slices for immediate multidimensional comparison, is also included. OSA ISP software is employed to illustrate the synergy between the simultaneously provided perspectives. This synergy promotes interpretation of information by enhancing diagnostic comparisons and facilitates internal correction of movement artifacts within C-scan and B-scan OCT images using information provided by the SLO channel

Development of real-time dual-display handheld and bench-top hybrid-mode SD-OCTs

Development of a dual-display handheld optical coherence tomography (OCT) system for retina and optic-nerve-head diagnosis beyond the volunteer motion constraints is reported. The developed system is portable and easily movable, containing the compact portable OCT system that includes the handheld probe and computer. Eye posterior chambers were diagnosed using the handheld probe, and the probe could be fixed to the bench-top cradle depending on the volunteers' physical condition. The images obtained using this handheld probe were displayed in real time on the computer monitor and on a small secondary built-in monitor; the displayed images were saved using the handheld probe's built-in button. Large-scale signal-processing procedures such as k-domain linearization, fast Fourier transform (FFT), and log-scaling signal processing can be rapidly applied using graphics-processing-unit (GPU) accelerated processing rather than central-processing-unit (CPU) processing. The Labview-based system resolution is 1,024 ?? 512 pixels, and the frame rate is 56 frames/s, useful for real-time display. The 3D images of the posterior chambers including the retina, optic-nerve head, blood vessels, and optic nerve were composed using real-time displayed images with 500 ?? 500 ?? 500 pixel resolution. A handheld and bench-top hybrid mode with a dual-display handheld OCT was developed to overcome the drawbacks of the conventional method.open0

OCT in Glaucoma Diagnosis, Detection and Screening

Glaucoma is a chronic and progressive optic neuropathy in which increased intraocular pressure is the most important risk factor in the etiopathogenesis. The basic pathology is the progressive loss of retinal ganglion cells (RGCs) especially the death of the axons of ganglion cells initially (apoptosis), followed by peripapillary retinal nerve fiber layer (RNFL) defects. Since optical coherence tomography (OCT)’s first demonstration in 1991 by Huang et al. and introduction commercially in 1996, it began gaining popularity in 2000s for retinal evaluation and the detection, diagnosis, and follow-up of glaucoma. Previously available OCT instruments used a technique referred to as time-domain (TD-) OCT, followed by spectral-domain (SD-) OCT, which has an increased scan acquisition rate, allowing for a more detailed sampling of the area of interest. Recently, swept-source OCT (SS-OCT), a newer generation of OCT, has been introduced. Clinical assessment using multiple parameters, including peripapillary RNFL, ganglion cells, optic nerve head, and macular parameters, has proven useful for managing and diagnosing glaucoma as well as for evaluating risk in glaucoma suspects. In this chapter, we aim to evaluate the use of OCT and its modalities in diagnosis, screening, and progression of glaucoma