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Tadayoni R, Massin P, Bonnin S, Magazzeni S, Lay B, Le Guilcher A, Vicaut E, Couturier A, Quellec G, Investigators E. Artificial intelligence-based prediction of diabetic retinopathy evolution (EviRed): protocol for a prospective cohort. BMJ Open 2024; 14:e084574. [PMID: 38626974 PMCID: PMC11029320 DOI: 10.1136/bmjopen-2024-084574] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 01/22/2024] [Accepted: 02/19/2024] [Indexed: 04/19/2024] Open
Abstract
INTRODUCTION An important obstacle in the fight against diabetic retinopathy (DR) is the use of a classification system based on old imaging techniques and insufficient data to accurately predict its evolution. New imaging techniques generate new valuable data, but we lack an adapted classification based on these data. The main objective of the Evaluation Intelligente de la Rétinopathie Diabétique, Intelligent evaluation of DR (EviRed) project is to develop and validate a system assisting the ophthalmologist in decision-making during DR follow-up by improving the prediction of its evolution. METHODS AND ANALYSIS A cohort of up to 5000 patients with diabetes will be recruited from 18 diabetology departments and 14 ophthalmology departments, in public or private hospitals in France and followed for an average of 2 years. Each year, systemic health data as well as ophthalmological data will be collected. Both eyes will be imaged by using different imaging modalities including widefield photography, optical coherence tomography (OCT) and OCT-angiography. The EviRed cohort will be divided into two groups: one group will be randomly selected in each stratum during the inclusion period to be representative of the general diabetic population. Their data will be used for validating the algorithms (validation cohort). The data for the remaining patients (training cohort) will be used to train the algorithms. ETHICS AND DISSEMINATION The study protocol was approved by the French South-West and Overseas Ethics Committee 4 on 28 August 2020 (CPP2020-07-060b/2020-A01725-34/20.06.16.41433). Prior to the start of the study, each patient will provide a written informed consent documenting his or her agreement to participate in the clinical trial. Results of this research will be disseminated in peer-reviewed publications and conference presentations. The database will also be available for further study or development that could benefit patients. TRIAL REGISTRATION NUMBER NCT04624737.
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Affiliation(s)
- Ramin Tadayoni
- Ophthalmology Department, Université Paris Cité, AP-HP, Lariboisiere Hospital, Paris, France
- Ophthalmology Departement, Adolphe de Rothschild Ophthalmological Foundation, Paris, France
| | - Pascale Massin
- Ophthalmology Department, Université Paris Cité, AP-HP, Lariboisiere Hospital, Paris, France
| | - Sophie Bonnin
- Ophthalmology, Adolphe de Rothschild Ophthalmological Foundation, Paris, France
| | | | | | | | - Eric Vicaut
- Assistance Publique-Hopitaux de Paris, Paris, France
| | - Aude Couturier
- Ophthalmology Department, Université Paris Cité, AP-HP, Lariboisiere Hospital, Paris, France
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Daich Varela M, Sen S, De Guimaraes TAC, Kabiri N, Pontikos N, Balaskas K, Michaelides M. Artificial intelligence in retinal disease: clinical application, challenges, and future directions. Graefes Arch Clin Exp Ophthalmol 2023; 261:3283-3297. [PMID: 37160501 PMCID: PMC10169139 DOI: 10.1007/s00417-023-06052-x] [Citation(s) in RCA: 13] [Impact Index Per Article: 13.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/26/2023] [Revised: 03/20/2023] [Accepted: 03/24/2023] [Indexed: 05/11/2023] Open
Abstract
Retinal diseases are a leading cause of blindness in developed countries, accounting for the largest share of visually impaired children, working-age adults (inherited retinal disease), and elderly individuals (age-related macular degeneration). These conditions need specialised clinicians to interpret multimodal retinal imaging, with diagnosis and intervention potentially delayed. With an increasing and ageing population, this is becoming a global health priority. One solution is the development of artificial intelligence (AI) software to facilitate rapid data processing. Herein, we review research offering decision support for the diagnosis, classification, monitoring, and treatment of retinal disease using AI. We have prioritised diabetic retinopathy, age-related macular degeneration, inherited retinal disease, and retinopathy of prematurity. There is cautious optimism that these algorithms will be integrated into routine clinical practice to facilitate access to vision-saving treatments, improve efficiency of healthcare systems, and assist clinicians in processing the ever-increasing volume of multimodal data, thereby also liberating time for doctor-patient interaction and co-development of personalised management plans.
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Affiliation(s)
- Malena Daich Varela
- UCL Institute of Ophthalmology, London, UK
- Moorfields Eye Hospital, London, UK
| | | | | | | | - Nikolas Pontikos
- UCL Institute of Ophthalmology, London, UK
- Moorfields Eye Hospital, London, UK
| | | | - Michel Michaelides
- UCL Institute of Ophthalmology, London, UK.
- Moorfields Eye Hospital, London, UK.
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Zhang T, Wei Q, Li Z, Meng W, Zhang M, Zhang Z. Segmentation of paracentral acute middle maculopathy lesions in spectral-domain optical coherence tomography images through weakly supervised deep convolutional networks. COMPUTER METHODS AND PROGRAMS IN BIOMEDICINE 2023; 240:107632. [PMID: 37329802 DOI: 10.1016/j.cmpb.2023.107632] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 08/25/2022] [Revised: 05/23/2023] [Accepted: 05/28/2023] [Indexed: 06/19/2023]
Abstract
BACKGROUND AND OBJECTIVES Spectral-domain optical coherence tomography (SD-OCT) is a valuable tool for non-invasive imaging of the retina, allowing the discovery and visualization of localized lesions, the presence of which is associated with eye diseases. The present study introduces X-Net, a weakly supervised deep-learning framework for automated segmentation of paracentral acute middle maculopathy (PAMM) lesions in retinal SD-OCT images. Despite recent advances in the development of automatic methods for clinical analysis of OCT scans, there remains a scarcity of studies focusing on the automated detection of small retinal focal lesions. Additionally, most existing solutions depend on supervised learning, which can be time-consuming and require extensive image labeling, whereas X-Net offers a solution to these challenges. As far as we can determine, no prior study has addressed the segmentation of PAMM lesions in SD-OCT images. METHODS This study leverages 133 SD-OCT retinal images, each containing instances of paracentral acute middle maculopathy lesions. A team of eye experts annotated the PAMM lesions in these images using bounding boxes. Then, labeled data were used to train a U-Net that performs pre-segmentation, producing region labels of pixel-level accuracy. To attain a highly-accurate final segmentation, we introduced X-Net, a novel neural network made up of a master and a slave U-Net. During training, it takes the expert annotated, and pixel-level pre-segment annotated images and employs sophisticated strategies to ensure the highest segmentation accuracy. RESULTS The proposed method was rigorously evaluated on clinical retinal images excluded from training and achieved an accuracy of 99% with a high level of similarity between the automatic segmentation and expert annotation, as demonstrated by a mean Intersection-over-Union of 0.8. Alternative methods were tested on the same data. Single-stage neural networks proved insufficient for achieving satisfactory results, confirming that more advanced solutions, such as the proposed method, are necessary. We also found that X-Net using Attention U-net for both the pre-segmentation and X-Net arms for the final segmentation shows comparable performance to the proposed method, suggesting that the proposed approach remains a viable solution even when implemented with variants of the classic U-Net. CONCLUSIONS The proposed method exhibits reasonably high performance, validated through quantitative and qualitative evaluations. Medical eye specialists have also verified its validity and accuracy. Thus, it could be a viable tool in the clinical assessment of the retina. Additionally, the demonstrated approach for annotating the training set has proven to be effective in reducing the expert workload.
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Affiliation(s)
- Tianqiao Zhang
- School of Life and Environmental Sciences, Guilin University of Electronic Technology, Guilin, China
| | - Qiaoqian Wei
- School of Life and Environmental Sciences, Guilin University of Electronic Technology, Guilin, China
| | - Zhenzhen Li
- School of Information Engineering, Nanchang Institute of Technology, Nanchang, China
| | - Wenjing Meng
- Department of Library Services, Guilin University of Electronic Technology, Guilin, China
| | - Mengjiao Zhang
- School of Life and Environmental Sciences, Guilin University of Electronic Technology, Guilin, China
| | - Zhengwei Zhang
- Department of Ophthalmology, Jiangnan University Medical Center, Wuxi, China; Department of Ophthalmology, Wuxi No.2 People's Hospital, Affiliated Wuxi Clinical College of Nantong University, Wuxi, China.
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Karn PK, Abdulla WH. On Machine Learning in Clinical Interpretation of Retinal Diseases Using OCT Images. Bioengineering (Basel) 2023; 10:bioengineering10040407. [PMID: 37106594 PMCID: PMC10135895 DOI: 10.3390/bioengineering10040407] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/17/2023] [Revised: 03/21/2023] [Accepted: 03/22/2023] [Indexed: 03/29/2023] Open
Abstract
Optical coherence tomography (OCT) is a noninvasive imaging technique that provides high-resolution cross-sectional retina images, enabling ophthalmologists to gather crucial information for diagnosing various retinal diseases. Despite its benefits, manual analysis of OCT images is time-consuming and heavily dependent on the personal experience of the analyst. This paper focuses on using machine learning to analyse OCT images in the clinical interpretation of retinal diseases. The complexity of understanding the biomarkers present in OCT images has been a challenge for many researchers, particularly those from nonclinical disciplines. This paper aims to provide an overview of the current state-of-the-art OCT image processing techniques, including image denoising and layer segmentation. It also highlights the potential of machine learning algorithms to automate the analysis of OCT images, reducing time consumption and improving diagnostic accuracy. Using machine learning in OCT image analysis can mitigate the limitations of manual analysis methods and provide a more reliable and objective approach to diagnosing retinal diseases. This paper will be of interest to ophthalmologists, researchers, and data scientists working in the field of retinal disease diagnosis and machine learning. By presenting the latest advancements in OCT image analysis using machine learning, this paper will contribute to the ongoing efforts to improve the diagnostic accuracy of retinal diseases.
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Wei X, Sui R. A Review of Machine Learning Algorithms for Retinal Cyst Segmentation on Optical Coherence Tomography. SENSORS (BASEL, SWITZERLAND) 2023; 23:3144. [PMID: 36991857 PMCID: PMC10054815 DOI: 10.3390/s23063144] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 02/11/2023] [Revised: 03/02/2023] [Accepted: 03/07/2023] [Indexed: 06/19/2023]
Abstract
Optical coherence tomography (OCT) is an emerging imaging technique for diagnosing ophthalmic diseases and the visual analysis of retinal structure changes, such as exudates, cysts, and fluid. In recent years, researchers have increasingly focused on applying machine learning algorithms, including classical machine learning and deep learning methods, to automate retinal cysts/fluid segmentation. These automated techniques can provide ophthalmologists with valuable tools for improved interpretation and quantification of retinal features, leading to more accurate diagnosis and informed treatment decisions for retinal diseases. This review summarized the state-of-the-art algorithms for the three essential steps of cyst/fluid segmentation: image denoising, layer segmentation, and cyst/fluid segmentation, while emphasizing the significance of machine learning techniques. Additionally, we provided a summary of the publicly available OCT datasets for cyst/fluid segmentation. Furthermore, the challenges, opportunities, and future directions of artificial intelligence (AI) in OCT cyst segmentation are discussed. This review is intended to summarize the key parameters for the development of a cyst/fluid segmentation system and the design of novel segmentation algorithms and has the potential to serve as a valuable resource for imaging researchers in the development of assessment systems related to ocular diseases exhibiting cyst/fluid in OCT imaging.
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Mousavi N, Monemian M, Ghaderi Daneshmand P, Mirmohammadsadeghi M, Zekri M, Rabbani H. Cyst identification in retinal optical coherence tomography images using hidden Markov model. Sci Rep 2023; 13:12. [PMID: 36593300 PMCID: PMC9807649 DOI: 10.1038/s41598-022-27243-2] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/11/2022] [Accepted: 12/28/2022] [Indexed: 01/03/2023] Open
Abstract
Optical Coherence Tomography (OCT) is a useful imaging modality facilitating the capturing process from retinal layers. In the salient diseases of retina, cysts are formed in retinal layers. Therefore, the identification of cysts in the retinal layers is of great importance. In this paper, a new method is proposed for the rapid detection of cystic OCT B-scans. In the proposed method, a Hidden Markov Model (HMM) is used for mathematically modelling the existence of cyst. In fact, the existence of cyst in the image can be considered as a hidden state. Since the existence of cyst in an OCT B-scan depends on the existence of cyst in the previous B-scans, HMM is an appropriate tool for modelling this process. In the first phase, a number of features are extracted which are Harris, KAZE, HOG, SURF, FAST, Min-Eigen and feature extracted by deep AlexNet. It is shown that the feature with the best discriminating power is the feature extracted by AlexNet. The features extracted in the first phase are used as observation vectors to estimate the HMM parameters. The evaluation results show the improved performance of HMM in terms of accuracy.
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Affiliation(s)
- Niloofarsadat Mousavi
- grid.411751.70000 0000 9908 3264Department of Electrical and Computer Engineering, Isfahan University of Technology, Isfahan, Iran
| | - Maryam Monemian
- grid.411036.10000 0001 1498 685XMedical Image and Signal Processing Research Center, Isfahan University of Medical Sciences, Isfahan, Iran
| | - Parisa Ghaderi Daneshmand
- grid.411036.10000 0001 1498 685XMedical Image and Signal Processing Research Center, Isfahan University of Medical Sciences, Isfahan, Iran
| | | | - Maryam Zekri
- grid.411751.70000 0000 9908 3264Department of Electrical and Computer Engineering, Isfahan University of Technology, Isfahan, Iran
| | - Hossein Rabbani
- grid.411036.10000 0001 1498 685XMedical Image and Signal Processing Research Center, Isfahan University of Medical Sciences, Isfahan, Iran
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Wang X, Tang F, Chen H, Cheung CY, Heng PA. Deep semi-supervised multiple instance learning with self-correction for DME classification from OCT images. Med Image Anal 2023; 83:102673. [PMID: 36403310 DOI: 10.1016/j.media.2022.102673] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/06/2021] [Revised: 07/03/2022] [Accepted: 10/20/2022] [Indexed: 11/18/2022]
Abstract
Supervised deep learning has achieved prominent success in various diabetic macular edema (DME) recognition tasks from optical coherence tomography (OCT) volumetric images. A common problematic issue that frequently occurs in this field is the shortage of labeled data due to the expensive fine-grained annotations, which increases substantial difficulty in accurate analysis by supervised learning. The morphological changes in the retina caused by DME might be distributed sparsely in B-scan images of the OCT volume, and OCT data is often coarsely labeled at the volume level. Hence, the DME identification task can be formulated as a multiple instance classification problem that could be addressed by multiple instance learning (MIL) techniques. Nevertheless, none of previous studies utilize unlabeled data simultaneously to promote the classification accuracy, which is particularly significant for a high quality of analysis at the minimum annotation cost. To this end, we present a novel deep semi-supervised multiple instance learning framework to explore the feasibility of leveraging a small amount of coarsely labeled data and a large amount of unlabeled data to tackle this problem. Specifically, we come up with several modules to further improve the performance according to the availability and granularity of their labels. To warm up the training, we propagate the bag labels to the corresponding instances as the supervision of training, and propose a self-correction strategy to handle the label noise in the positive bags. This strategy is based on confidence-based pseudo-labeling with consistency regularization. The model uses its prediction to generate the pseudo-label for each weakly augmented input only if it is highly confident about the prediction, which is subsequently used to supervise the same input in a strongly augmented version. This learning scheme is also applicable to unlabeled data. To enhance the discrimination capability of the model, we introduce the Student-Teacher architecture and impose consistency constraints between two models. For demonstration, the proposed approach was evaluated on two large-scale DME OCT image datasets. Extensive results indicate that the proposed method improves DME classification with the incorporation of unlabeled data and outperforms competing MIL methods significantly, which confirm the feasibility of deep semi-supervised multiple instance learning at a low annotation cost.
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Affiliation(s)
- Xi Wang
- Zhejiang Lab, Hangzhou, China; Department of Computer Science and Engineering, The Chinese University of Hong Kong, Hong Kong, China; Department of Radiation Oncology, Stanford University School of Medicine, Palo Alto, CA, USA
| | - Fangyao Tang
- Department of Ophthalmology and Visual Sciences, The Chinese University of Hong Kong, Hong Kong, China
| | - Hao Chen
- Department of Computer Science and Engineering, The Hong Kong University of Science and Technology, Hong Kong, China.
| | - Carol Y Cheung
- Department of Ophthalmology and Visual Sciences, The Chinese University of Hong Kong, Hong Kong, China
| | - Pheng-Ann Heng
- Department of Computer Science and Engineering, The Chinese University of Hong Kong, Hong Kong, China; Guangdong-Hong Kong-Macao Joint Laboratory of Human-Machine Intelligence-Synergy Systems, Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences, China
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Ibragimova RR, Gilmanov II, Lopukhova EA, Lakman IA, Bilyalov AR, Mukhamadeev TR, Kutluyarov RV, Idrisova GM. Algorithm of segmentation of OCT macular images to analyze the results in patients with age-related macular degeneration. BULLETIN OF RUSSIAN STATE MEDICAL UNIVERSITY 2022. [DOI: 10.24075/brsmu.2022.062] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/31/2022]
Abstract
Age-related macular degeneration (AMD) is one of the main causes of loss of sight and hypovision in people over working age. Results of optical coherence tomography (OCT) are essential for diagnostics of the disease. Developing the recommendation system to analyze OCT images will reduce the time to process visual data and decrease the probability of errors while working as a doctor. The purpose of the study was to develop an algorithm of segmentation to analyze the results of macular OCT in patients with AMD. It allows to provide a correct prediction of an AMD stage based on the form of discovered pathologies. A program has been developed in the Python programming language using the Pytorch and TensorFlow libraries. Its quality was estimated using OCT macular images of 51 patients with early, intermediate, late AMD. A segmentation algorithm of OCT images was developed based on convolutional neural network. UNet network was selected as architecture of high-accuracy neural net. The neural net is trained on macular OCT images of 125 patients (197 eyes). The author algorithm displayed 98.1% of properly segmented areas on OCT images, which are the most essential for diagnostics and determination of an AMD stage. Weighted sensitivity and specificity of AMD stage classifier amounted to 83.8% and 84.9% respectively. The developed algorithm is promising as a recommendation system that implements the AMD classification based on data that promote taking decisions regarding the treatment strategy.
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Affiliation(s)
| | - II Gilmanov
- Ufa State Aviation Technical University, Ufa, Russia
| | - EA Lopukhova
- Ufa State Aviation Technical University, Ufa, Russia
| | - IA Lakman
- Bashkir State Medical University, Ufa, Russia
| | - AR Bilyalov
- Bashkir State Medical University, Ufa, Russia
| | | | - RV Kutluyarov
- Ufa State Aviation Technical University, Ufa, Russia
| | - GM Idrisova
- Bashkir State Medical University, Ufa, Russia
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Zhang B, Ma L, Zhao H, Hao Y, Fu S, Wang H, Li Y, Han H. Automatic segmentation of hyperreflective dots via focal priors and visual saliency. Med Phys 2022; 49:7025-7037. [PMID: 35838240 DOI: 10.1002/mp.15848] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/04/2022] [Revised: 06/20/2022] [Accepted: 06/27/2022] [Indexed: 12/13/2022] Open
Abstract
PURPOSE Hyperreflective dots (HRDs) can be observed in spectral domain optical coherence tomography (SD-OCT), which can provide a sensitive marker in the treatment decision process. Quantitative analyses of HRDs are the key to make appropriate decisions on observation, treatment, and retreatment. The purpose of this study is to automatically and accurately segment HRDs in SD-OCT B-scans with diabetic retinopathy (DR). METHODS The authors propose an automatic segmentation algorithm of HRDs via focal priors and visual saliency. The algorithm is divided into three stages: segmentation of retinal layers, calculation of the multiscale local contrast saliency map, and adaptive threshold segmentation. First, a method based on improved graph search is used to segment retinal layers to obtain the region of interest (ROI) and the reflectivity estimation of the retinal pigment epithelium (RPE) layer; then, the multiscale local contrast saliency map is obtained by using a local contrast measure, which measures the dissimilarity between the current pixels and corresponding neighborhoods; finally, an adaptive threshold is applied to segment HRDs. RESULTS Experimental results on 20 SD-OCT B-scans demonstrate that our method is effective for HRDs segmentation. The average dice similarity coefficient (DSC) and detection accuracy are 71.12% and 85.07%, respectively. CONCLUSIONS The proposed method can accurately segment HRDs in SD-OCT B-scans with DR and outperforms current state-of-the-art methods. Our method can provide reliable HRDs segmentation to assist ophthalmologists in clinical diagnosis, treatment, disease monitoring, and progression.
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Affiliation(s)
- Bo Zhang
- School of Mathematics, Shandong University, Jinan, China
| | - Lin Ma
- Office of Human Resources, Peking University Health Science, Beijing, China
| | - Hui Zhao
- Department of Ophthalmology, Qilu Hospital of Shandong University, Jinan, China
| | - Yanlei Hao
- Department of Ophthalmology, Jinan Central Hospital of Shandong University, Jinan, China.,The Cheeloo College of Medicine, Shandong University, Jinan, China
| | - Shujun Fu
- School of Mathematics, Shandong University, Jinan, China
| | - Hong Wang
- Department of Ophthalmology, Qilu Hospital of Shandong University, Jinan, China
| | - Yuliang Li
- Department of Intervention Medicine, The Second Hospital of Shandong University, Jinan, China
| | - Hongbin Han
- Department of Radiology, Peking University Third Hospital, Beijing, China.,The Beijing Key Laboratory of Magnetic Resonance Imaging Equipment and Technique, Beijing, China
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López-Varela E, Vidal PL, Pascual NO, Novo J, Ortega M. Fully-Automatic 3D Intuitive Visualization of Age-Related Macular Degeneration Fluid Accumulations in OCT Cubes. J Digit Imaging 2022; 35:1271-1282. [PMID: 35513586 PMCID: PMC9582110 DOI: 10.1007/s10278-022-00643-6] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/26/2021] [Revised: 04/06/2022] [Accepted: 04/13/2022] [Indexed: 11/16/2022] Open
Abstract
Age-related macular degeneration is the leading cause of vision loss in developed countries, and wet-type AMD requires urgent treatment and rapid diagnosis because it causes rapid irreversible vision loss. Currently, AMD diagnosis is mainly carried out using images obtained by optical coherence tomography. This diagnostic process is performed by human clinicians, so human error may occur in some cases. Therefore, fully automatic methodologies are highly desirable adding a layer of robustness to the diagnosis. In this work, a novel computer-aided diagnosis and visualization methodology is proposed for the rapid identification and visualization of wet AMD. We adapted a convolutional neural network for segmentation of a similar domain of medical images to the problem of wet AMD segmentation, taking advantage of transfer learning, which allows us to work with and exploit a reduced number of samples. We generate a 3D intuitive visualization where the existence, position and severity of the fluid were represented in a clear and intuitive way to facilitate the analysis of the clinicians. The 3D visualization is robust and accurate, obtaining satisfactory 0.949 and 0.960 Dice coefficients in the different evaluated OCT cube configurations, allowing to quickly assess the presence and extension of the fluid associated to wet AMD.
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Affiliation(s)
- Emilio López-Varela
- Grupo VARPA, Instituto de investigación Biomédica de A Coruña (INIBIC), Xubias de Arriba, 84, A Coruña, 15006 Spain
- Centro de investigación CITIC, Universidade da Coruña, Campus de Elviña, s/n, A Coruña, 15071 Spain
| | - Plácido L. Vidal
- Grupo VARPA, Instituto de investigación Biomédica de A Coruña (INIBIC), Xubias de Arriba, 84, A Coruña, 15006 Spain
- Centro de investigación CITIC, Universidade da Coruña, Campus de Elviña, s/n, A Coruña, 15071 Spain
| | - Nuria Olivier Pascual
- Servizo de Oftalmoloxía, Complexo Hospitalario Universitario de Ferrol, CHUF, Av. da Residencia, S/N, Ferrol, 15405 Spain
| | - Jorge Novo
- Grupo VARPA, Instituto de investigación Biomédica de A Coruña (INIBIC), Xubias de Arriba, 84, A Coruña, 15006 Spain
- Centro de investigación CITIC, Universidade da Coruña, Campus de Elviña, s/n, A Coruña, 15071 Spain
| | - Marcos Ortega
- Grupo VARPA, Instituto de investigación Biomédica de A Coruña (INIBIC), Xubias de Arriba, 84, A Coruña, 15006 Spain
- Centro de investigación CITIC, Universidade da Coruña, Campus de Elviña, s/n, A Coruña, 15071 Spain
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Directional analysis of intensity changes for determining the existence of cyst in optical coherence tomography images. Sci Rep 2022; 12:2105. [PMID: 35136133 PMCID: PMC8825816 DOI: 10.1038/s41598-022-06099-6] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/01/2021] [Accepted: 01/24/2022] [Indexed: 11/23/2022] Open
Abstract
Diabetic retinopathy (DR) is an important cause of blindness in people with the long history of diabetes. DR is caused due to the damage to blood vessels in the retina. One of the most important manifestations of DR is the formation of fluid-filled regions between retinal layers. The evaluation of stage and transcribed drugs can be possible through the analysis of retinal Optical Coherence Tomography (OCT) images. Therefore, the detection of cysts in OCT images and the is of considerable importance. In this paper, a fast method is proposed to determine the status of OCT images as cystic or non-cystic. The method consists of three phases which are pre-processing, boundary pixel determination and post-processing. After applying a noise reduction method in the pre-processing step, the method finds the pixels which are the boundary pixels of cysts. This process is performed by finding the significant intensity changes in the vertical direction and considering rectangular patches around the candidate pixels. The patches are verified whether or not they contain enough pixels making considerable diagonal intensity changes. Then, a shadow omission method is proposed in the post-processing phase to extract the shadow regions which can be mistakenly considered as cystic areas. Then, the pixels extracted in the previous phase that are near the shadow regions are removed to prevent the production of false positive cases. The performance of the proposed method is evaluated in terms of sensitivity and specificity on real datasets. The experimental results show that the proposed method produces outstanding results from both accuracy and speed points of view.
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Elsharkawy M, Elrazzaz M, Ghazal M, Alhalabi M, Soliman A, Mahmoud A, El-Daydamony E, Atwan A, Thanos A, Sandhu HS, Giridharan G, El-Baz A. Role of Optical Coherence Tomography Imaging in Predicting Progression of Age-Related Macular Disease: A Survey. Diagnostics (Basel) 2021; 11:2313. [PMID: 34943550 PMCID: PMC8699887 DOI: 10.3390/diagnostics11122313] [Citation(s) in RCA: 14] [Impact Index Per Article: 4.7] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/05/2021] [Revised: 12/03/2021] [Accepted: 12/07/2021] [Indexed: 11/16/2022] Open
Abstract
In developed countries, age-related macular degeneration (AMD), a retinal disease, is the main cause of vision loss in the elderly. Optical Coherence Tomography (OCT) is currently the gold standard for assessing individuals for initial AMD diagnosis. In this paper, we look at how OCT imaging can be used to diagnose AMD. Our main aim is to examine and compare automated computer-aided diagnostic (CAD) systems for diagnosing and grading of AMD. We provide a brief summary, outlining the main aspects of performance assessment and providing a basis for current research in AMD diagnosis. As a result, the only viable alternative is to prevent AMD and stop both this devastating eye condition and unwanted visual impairment. On the other hand, the grading of AMD is very important in order to detect early AMD and prevent patients from reaching advanced AMD disease. In light of this, we explore the remaining issues with automated systems for AMD detection based on OCT imaging, as well as potential directions for diagnosis and monitoring systems based on OCT imaging and telemedicine applications.
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Affiliation(s)
- Mohamed Elsharkawy
- Bioengineering Department, University of Louisville, Louisville, KY 40292, USA; (M.E.); (M.E.); (A.S.); (A.M.); (H.S.S.); (G.G.)
| | - Mostafa Elrazzaz
- Bioengineering Department, University of Louisville, Louisville, KY 40292, USA; (M.E.); (M.E.); (A.S.); (A.M.); (H.S.S.); (G.G.)
| | - Mohammed Ghazal
- Electrical and Computer Engineering Department, College of Engineering, Abu Dhabi University, Abu Dhabi 59911, United Arab Emirates; (M.G.); (M.A.)
| | - Marah Alhalabi
- Electrical and Computer Engineering Department, College of Engineering, Abu Dhabi University, Abu Dhabi 59911, United Arab Emirates; (M.G.); (M.A.)
| | - Ahmed Soliman
- Bioengineering Department, University of Louisville, Louisville, KY 40292, USA; (M.E.); (M.E.); (A.S.); (A.M.); (H.S.S.); (G.G.)
| | - Ali Mahmoud
- Bioengineering Department, University of Louisville, Louisville, KY 40292, USA; (M.E.); (M.E.); (A.S.); (A.M.); (H.S.S.); (G.G.)
| | - Eman El-Daydamony
- Faculty of Computers and Information, Mansoura University, Mansoura 35516, Egypt; (E.E.-D.); (A.A.)
| | - Ahmed Atwan
- Faculty of Computers and Information, Mansoura University, Mansoura 35516, Egypt; (E.E.-D.); (A.A.)
| | | | - Harpal Singh Sandhu
- Bioengineering Department, University of Louisville, Louisville, KY 40292, USA; (M.E.); (M.E.); (A.S.); (A.M.); (H.S.S.); (G.G.)
| | - Guruprasad Giridharan
- Bioengineering Department, University of Louisville, Louisville, KY 40292, USA; (M.E.); (M.E.); (A.S.); (A.M.); (H.S.S.); (G.G.)
| | - Ayman El-Baz
- Bioengineering Department, University of Louisville, Louisville, KY 40292, USA; (M.E.); (M.E.); (A.S.); (A.M.); (H.S.S.); (G.G.)
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Automatic Segmentation of the Retinal Nerve Fiber Layer by Means of Mathematical Morphology and Deformable Models in 2D Optical Coherence Tomography Imaging. SENSORS 2021; 21:s21238027. [PMID: 34884031 PMCID: PMC8659929 DOI: 10.3390/s21238027] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 09/29/2021] [Revised: 11/17/2021] [Accepted: 11/25/2021] [Indexed: 11/17/2022]
Abstract
Glaucoma is a neurodegenerative disease process that leads to progressive damage of the optic nerve to produce visual impairment and blindness. Spectral-domain OCT technology enables peripapillary circular scans of the retina and the measurement of the thickness of the retinal nerve fiber layer (RNFL) for the assessment of the disease status or progression in glaucoma patients. This paper describes a new approach to segment and measure the retinal nerve fiber layer in peripapillary OCT images. The proposed method consists of two stages. In the first one, morphological operators robustly detect the coarse location of the layer boundaries, despite the speckle noise and diverse artifacts in the OCT image. In the second stage, deformable models are initialized with the results of the previous stage to perform a fine segmentation of the boundaries, providing an accurate measurement of the entire RNFL. The results of the RNFL segmentation were qualitatively assessed by ophthalmologists, and the measurements of the thickness of the RNFL were quantitatively compared with those provided by the OCT inbuilt software as well as the state-of-the-art methods.
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15
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Stankiewicz A, Marciniak T, Dabrowski A, Stopa M, Marciniak E, Obara B. Segmentation of Preretinal Space in Optical Coherence Tomography Images Using Deep Neural Networks. SENSORS 2021; 21:s21227521. [PMID: 34833597 PMCID: PMC8623441 DOI: 10.3390/s21227521] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 09/28/2021] [Revised: 11/08/2021] [Accepted: 11/09/2021] [Indexed: 02/01/2023]
Abstract
This paper proposes an efficient segmentation of the preretinal area between the inner limiting membrane (ILM) and posterior cortical vitreous (PCV) of the human eye in an image obtained with the use of optical coherence tomography (OCT). The research was carried out using a database of three-dimensional OCT imaging scans obtained with the Optovue RTVue XR Avanti device. Various types of neural networks (UNet, Attention UNet, ReLayNet, LFUNet) were tested for semantic segmentation, their effectiveness was assessed using the Dice coefficient and compared to the graph theory techniques. Improvement in segmentation efficiency was achieved through the use of relative distance maps. We also show that selecting a larger kernel size for convolutional layers can improve segmentation quality depending on the neural network model. In the case of PVC, we obtain the effectiveness reaching up to 96.35%. The proposed solution can be widely used to diagnose vitreomacular traction changes, which is not yet available in scientific or commercial OCT imaging solutions.
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Affiliation(s)
- Agnieszka Stankiewicz
- Division of Electronic Systems and Signal Processing, Institute of Automatic Control and Robotics, Poznan University of Technology, 60-965 Poznan, Poland; (A.S.); (A.D.)
| | - Tomasz Marciniak
- Division of Electronic Systems and Signal Processing, Institute of Automatic Control and Robotics, Poznan University of Technology, 60-965 Poznan, Poland; (A.S.); (A.D.)
- Correspondence:
| | - Adam Dabrowski
- Division of Electronic Systems and Signal Processing, Institute of Automatic Control and Robotics, Poznan University of Technology, 60-965 Poznan, Poland; (A.S.); (A.D.)
| | - Marcin Stopa
- Department of Ophthalmology, Chair of Ophthalmology and Optometry, Heliodor Swiecicki University Hospital, Poznan University of Medical Sciences, 60-780 Poznan, Poland; (M.S.); (E.M.)
| | - Elzbieta Marciniak
- Department of Ophthalmology, Chair of Ophthalmology and Optometry, Heliodor Swiecicki University Hospital, Poznan University of Medical Sciences, 60-780 Poznan, Poland; (M.S.); (E.M.)
| | - Boguslaw Obara
- School of Computing, Newcastle University, Newcastle upon Tyne NE4 5TG, UK;
- Biosciences Institute, Newcastle University, Newcastle upon Tyne NE2 4HH, UK
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16
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Assignment Flow for Order-Constrained OCT Segmentation. Int J Comput Vis 2021. [DOI: 10.1007/s11263-021-01520-5] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/20/2022]
Abstract
AbstractAt the present time optical coherence tomography (OCT) is among the most commonly used non-invasive imaging methods for the acquisition of large volumetric scans of human retinal tissues and vasculature. The substantial increase of accessible highly resolved 3D samples at the optic nerve head and the macula is directly linked to medical advancements in early detection of eye diseases. To resolve decisive information from extracted OCT volumes and to make it applicable for further diagnostic analysis, the exact measurement of retinal layer thicknesses serves as an essential task be done for each patient separately. However, manual examination of OCT scans is a demanding and time consuming task, which is typically made difficult by the presence of tissue-dependent speckle noise. Therefore, the elaboration of automated segmentation models has become an important task in the field of medical image processing. We propose a novel, purely data driven geometric approach to order-constrained 3D OCT retinal cell layer segmentation which takes as input data in any metric space and can be implemented using only simple, highly parallelizable operations. As opposed to many established retinal layer segmentation methods, we use only locally extracted features as input and do not employ any global shape prior. The physiological order of retinal cell layers and membranes is achieved through the introduction of a smoothed energy term. This is combined with additional regularization of local smoothness to yield highly accurate 3D segmentations. The approach thereby systematically avoid bias pertaining to global shape and is hence suited for the detection of anatomical changes of retinal tissue structure. To demonstrate its robustness, we compare two different choices of features on a data set of manually annotated 3D OCT volumes of healthy human retina. The quality of computed segmentations is compared to the state of the art in automatic retinal layer segmention as well as to manually annotated ground truth data in terms of mean absolute error and Dice similarity coefficient. Visualizations of segmented volumes are also provided.
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17
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Ma R, Liu Y, Tao Y, Alawa KA, Shyu ML, Lee RK. Deep Learning-Based Retinal Nerve Fiber Layer Thickness Measurement of Murine Eyes. Transl Vis Sci Technol 2021; 10:21. [PMID: 34297789 PMCID: PMC8300062 DOI: 10.1167/tvst.10.8.21] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/24/2022] Open
Abstract
Purpose To design a robust and automated estimation method for measuring the retinal nerve fiber layer (RNFL) thickness using spectral domain optical coherence tomography (SD-OCT). Methods We developed a deep learning-based image segmentation network for automated segmentation of the RNFL in SD-OCT B-scans of mouse eyes. In total, 5500 SD-OCT B-scans (5200 B-scans were used as training data with the remaining 300 B-scans used as testing data) were used to develop this segmentation network. Postprocessing operations were then applied on the segmentation results to fill any discontinuities or remove any speckles in the RNFL. Subsequently, a three-dimensional retina thickness map was generated by z-stacking 100 segmentation processed thickness B-scan images together. Finally, the average absolute difference between algorithm predicted RNFL thickness compared to the ground truth manual human segmentation was calculated. Results The proposed method achieves an average dice similarity coefficient of 0.929 in the SD-OCT segmentation task and an average absolute difference of 0.0009 mm in thickness estimation task on the basis of the testing dataset. We also evaluated our segmentation algorithm on another biological dataset with SD-OCT volumes for RNFL thickness after the optic nerve crush injury. Results were shown to be comparable between the predicted and manually measured retina thickness values. Conclusions Experimental results demonstrate that our automated segmentation algorithm reliably predicts the RNFL thickness in SD-OCT volumes of mouse eyes compared to laborious and more subjective manual SD-OCT RNFL segmentation. Translational Relevance Automated segmentation using a deep learning-based algorithm for murine eye OCT effectively and rapidly produced nerve fiber layer thicknesses comparable to manual segmentation.
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Affiliation(s)
- Rui Ma
- Department of Electrical and Computer Engineering, University of Miami, Coral Gables, FL, USA
| | - Yuan Liu
- Bascom Palmer Eye Institute, University of Miami Miller School of Medicine, Miami, FL, USA
| | - Yudong Tao
- Department of Electrical and Computer Engineering, University of Miami, Coral Gables, FL, USA
| | - Karam A Alawa
- Bascom Palmer Eye Institute, University of Miami Miller School of Medicine, Miami, FL, USA
| | - Mei-Ling Shyu
- Department of Electrical and Computer Engineering, University of Miami, Coral Gables, FL, USA
| | - Richard K Lee
- Department of Electrical and Computer Engineering, University of Miami, Coral Gables, FL, USA.,Bascom Palmer Eye Institute, University of Miami Miller School of Medicine, Miami, FL, USA
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18
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Sappa LB, Okuwobi IP, Li M, Zhang Y, Xie S, Yuan S, Chen Q. RetFluidNet: Retinal Fluid Segmentation for SD-OCT Images Using Convolutional Neural Network. J Digit Imaging 2021; 34:691-704. [PMID: 34080105 PMCID: PMC8329142 DOI: 10.1007/s10278-021-00459-w] [Citation(s) in RCA: 7] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/08/2020] [Revised: 12/03/2020] [Accepted: 04/29/2021] [Indexed: 11/25/2022] Open
Abstract
Age-related macular degeneration (AMD) is one of the leading causes of irreversible blindness and is characterized by fluid-related accumulations such as intra-retinal fluid (IRF), subretinal fluid (SRF), and pigment epithelial detachment (PED). Spectral-domain optical coherence tomography (SD-OCT) is the primary modality used to diagnose AMD, yet it does not have algorithms that directly detect and quantify the fluid. This work presents an improved convolutional neural network (CNN)-based architecture called RetFluidNet to segment three types of fluid abnormalities from SD-OCT images. The model assimilates different skip-connect operations and atrous spatial pyramid pooling (ASPP) to integrate multi-scale contextual information; thus, achieving the best performance. This work also investigates between consequential and comparatively inconsequential hyperparameters and skip-connect techniques for fluid segmentation from the SD-OCT image to indicate the starting choice for future related researches. RetFluidNet was trained and tested on SD-OCT images from 124 patients and achieved an accuracy of 80.05%, 92.74%, and 95.53% for IRF, PED, and SRF, respectively. RetFluidNet showed significant improvement over competitive works to be clinically applicable in reasonable accuracy and time efficiency. RetFluidNet is a fully automated method that can support early detection and follow-up of AMD.
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Affiliation(s)
- Loza Bekalo Sappa
- School of Computer Science and Engineering, Nanjing University of Science and Technology, 200 Xiaolingwei, Nanjing, 210094, China
| | - Idowu Paul Okuwobi
- School of Computer Science and Engineering, Nanjing University of Science and Technology, 200 Xiaolingwei, Nanjing, 210094, China
| | - Mingchao Li
- School of Computer Science and Engineering, Nanjing University of Science and Technology, 200 Xiaolingwei, Nanjing, 210094, China
| | - Yuhan Zhang
- School of Computer Science and Engineering, Nanjing University of Science and Technology, 200 Xiaolingwei, Nanjing, 210094, China
| | - Sha Xie
- School of Computer Science and Engineering, Nanjing University of Science and Technology, 200 Xiaolingwei, Nanjing, 210094, China
| | - Songtao Yuan
- Department of Ophthalmology, The First Affiliated Hospital With Nanjing Medical University, 300 Guangzhou Road, Nanjing, 210029, China
| | - Qiang Chen
- School of Computer Science and Engineering, Nanjing University of Science and Technology, 200 Xiaolingwei, Nanjing, 210094, China.
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19
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Xing R, Niu S, Gao X, Liu T, Fan W, Chen Y. Weakly supervised serous retinal detachment segmentation in SD-OCT images by two-stage learning. BIOMEDICAL OPTICS EXPRESS 2021; 12:2312-2327. [PMID: 33996231 PMCID: PMC8086451 DOI: 10.1364/boe.416167] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Received: 11/30/2020] [Revised: 03/12/2021] [Accepted: 03/16/2021] [Indexed: 06/12/2023]
Abstract
Automated lesion segmentation is one of the important tasks for the quantitative assessment of retinal diseases in SD-OCT images. Recently, deep convolutional neural networks (CNN) have shown promising advancements in the field of automated image segmentation, whereas they always benefit from large-scale datasets with high-quality pixel-wise annotations. Unfortunately, obtaining accurate annotations is expensive in both human effort and finance. In this paper, we propose a weakly supervised two-stage learning architecture to detect and further segment central serous chorioretinopathy (CSC) retinal detachment with only image-level annotations. Specifically, in the first stage, a Located-CNN is designed to detect the location of lesion regions in the whole SD-OCT retinal images, and highlight the distinguishing regions. To generate available a pseudo pixel-level label, the conventional level set method is employed to refine the distinguishing regions. In the second stage, we customize the active-contour loss function in deep networks to achieve the effective segmentation of the lesion area. A challenging dataset is used to evaluate our proposed method, and the results demonstrate that the proposed method consistently outperforms some current models trained with a different level of supervision, and is even as competitive as those relying on stronger supervision. To our best knowledge, we are the first to achieve CSC segmentation in SD-OCT images using weakly supervised learning, which can greatly reduce the labeling efforts.
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Affiliation(s)
- Ruiwen Xing
- School of Information Science and Engineering, University of Jinan, Jinan 250022, China
- Shandong Provincial Key Laboratory of Network-based Intelligent Computing, Jinan 250022, China
| | - Sijie Niu
- School of Information Science and Engineering, University of Jinan, Jinan 250022, China
- Shandong Provincial Key Laboratory of Network-based Intelligent Computing, Jinan 250022, China
| | - Xizhan Gao
- School of Information Science and Engineering, University of Jinan, Jinan 250022, China
- Shandong Provincial Key Laboratory of Network-based Intelligent Computing, Jinan 250022, China
| | - Tingting Liu
- Shandong Eye Hospital, State Key Laboratory Cultivation Base, Shandong Provincial Key Laboratory of Ophthalmology, Shandong Eye Institute, Shandong First Medical University and Shandong Academy of Medical Sciences, Jinan 250014, Jinan 250014, China
| | - Wen Fan
- Department of Ophthalmology, The First Affiliated Hospital with Nanjing Medical University, Nanjing 210094, China
| | - Yuehui Chen
- School of Information Science and Engineering, University of Jinan, Jinan 250022, China
- Shandong Provincial Key Laboratory of Network-based Intelligent Computing, Jinan 250022, China
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20
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Development of Decision Support Software for Deep Learning-Based Automated Retinal Disease Screening Using Relatively Limited Fundus Photograph Data. ELECTRONICS 2021. [DOI: 10.3390/electronics10020163] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/16/2022]
Abstract
Purpose—This study was conducted to develop an automated detection algorithm for screening fundus abnormalities, including age-related macular degeneration (AMD), diabetic retinopathy (DR), epiretinal membrane (ERM), retinal vascular occlusion (RVO), and suspected glaucoma among health screening program participants. Methods—The development dataset consisted of 43,221 retinal fundus photographs (from 25,564 participants, mean age 53.38 ± 10.97 years, female 39.0%) from a health screening program and patients of the Kangbuk Samsung Hospital Ophthalmology Department from 2006 to 2017. We evaluated our screening algorithm on independent validation datasets. Five separate one-versus-rest (OVR) classification algorithms based on deep convolutional neural networks (CNNs) were trained to detect AMD, ERM, DR, RVO, and suspected glaucoma. The ground truth for both development and validation datasets was graded at least two times by three ophthalmologists. The area under the receiver operating characteristic curve (AUC), sensitivity, and specificity were calculated for each disease, as well as their macro-averages. Results—For the internal validation dataset, the average sensitivity was 0.9098 (95% confidence interval (CI), 0.8660–0.9536), the average specificity was 0.9079 (95% CI, 0.8576–0.9582), and the overall accuracy was 0.9092 (95% CI, 0.8769–0.9415). For the external validation dataset consisting of 1698 images, the average of the AUCs was 0.9025 (95% CI, 0.8671–0.9379). Conclusions—Our algorithm had high sensitivity and specificity for detecting major fundus abnormalities. Our study will facilitate expansion of the applications of deep learning-based computer-aided diagnostic decision support tools in actual clinical settings. Further research is needed to improved generalization for this algorithm.
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21
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Mahmudi T, Kafieh R, Rabbani H, Mehri A, Akhlaghi MR. Evaluation of Asymmetry in Right and Left Eyes of Normal Individuals Using Extracted Features from Optical Coherence Tomography and Fundus Images. JOURNAL OF MEDICAL SIGNALS & SENSORS 2021; 11:12-23. [PMID: 34026586 PMCID: PMC8043121 DOI: 10.4103/jmss.jmss_67_19] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/14/2019] [Revised: 02/14/2020] [Accepted: 03/09/2020] [Indexed: 11/21/2022]
Abstract
BACKGROUND Asymmetry analysis of retinal layers in right and left eyes can be a valuable tool for early diagnoses of retinal diseases. To determine the limits of the normal interocular asymmetry in retinal layers around macula, thickness measurements are obtained with optical coherence tomography (OCT). METHODS For this purpose, after segmentation of intraretinal layer in threedimensional OCT data and calculating the midmacular point, the TM of each layer is obtained in 9 sectors in concentric circles around the macula. To compare corresponding sectors in the right and left eyes, the TMs of the left and right images are registered by alignment of retinal raphe (i.e. diskfovea axes). Since the retinal raphe of macular OCTs is not calculable due to limited region size, the TMs are registered by first aligning corresponding retinal raphe of fundus images and then registration of the OCTs to aligned fundus images. To analyze the asymmetry in each retinal layer, the mean and standard deviation of thickness in 9 sectors of 11 layers are calculated in 50 normal individuals. RESULTS The results demonstrate that some sectors of retinal layers have signifcant asymmetry with P < 0.05 in normal population. In this base, the tolerance limits for normal individuals are calculated. CONCLUSION This article shows that normal population does not have identical retinal information in both eyes, and without considering this reality, normal asymmetry in information gathered from both eyes might be interpreted as retinal disorders.
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Affiliation(s)
- Tahereh Mahmudi
- Department of Medical Physics and Biomedical Engineering, School of Medicine, Tehran University of Medical Sciences, Tehran, Iran
- Medical Image and Signal Processing Research Center, School of Advanced Technologies in Medicine, Isfahan University of Medical Sciences, Isfahan, Iran
| | - Raheleh Kafieh
- Department of Medical Physics and Biomedical Engineering, School of Medicine, Tehran University of Medical Sciences, Tehran, Iran
| | - Hossein Rabbani
- Department of Medical Physics and Biomedical Engineering, School of Medicine, Tehran University of Medical Sciences, Tehran, Iran
| | - Alireza Mehri
- Department of Medical Physics and Biomedical Engineering, School of Medicine, Tehran University of Medical Sciences, Tehran, Iran
| | - Mohammad-Reza Akhlaghi
- Department of Ophthalmology, School of Medicine, Isfahan University of Medical Sciences, Isfahan, Iran
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22
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Anoop B, Pavan R, Girish G, Kothari AR, Rajan J. Stack generalized deep ensemble learning for retinal layer segmentation in Optical Coherence Tomography images. Biocybern Biomed Eng 2020. [DOI: 10.1016/j.bbe.2020.07.010] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/16/2022]
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23
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Automatic Segmentation of Macular Edema in Retinal OCT Images Using Improved U-Net++. APPLIED SCIENCES-BASEL 2020. [DOI: 10.3390/app10165701] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/16/2022]
Abstract
The number and volume of retinal macular edemas are important indicators for screening and diagnosing retinopathy. Aiming at the problem that the segmentation method of macular edemas in a retinal optical coherence tomography (OCT) image is not ideal in segmentation of diverse edemas, this paper proposes a new method of automatic segmentation of macular edema regions in retinal OCT images using the improved U-Net++. The proposed method makes full use of the U-Net++ re-designed skip pathways and dense convolution block; reduces the semantic gap of the feature maps in the encoder/decoder sub-network; and adds the improved Resnet network as the backbone, which make the extraction of features in the edema regions more accurate and improves the segmentation effect. The proposed method was trained and validated on the public dataset of Duke University, and the experiments demonstrated the proposed method can not only improve the overall segmentation effect, but also can significantly improve the segmented precision for diverse edema in multi-regions, as well as reducing the error of the number of edema regions.
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24
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Ganjee R, Ebrahimi Moghaddam M, Nourinia R. An unsupervised hierarchical approach for automatic intra-retinal cyst segmentation in spectral-domain optical coherence tomography images. Med Phys 2020; 47:4872-4884. [PMID: 32609378 DOI: 10.1002/mp.14361] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/25/2019] [Revised: 03/16/2020] [Accepted: 06/17/2020] [Indexed: 11/06/2022] Open
Abstract
PURPOSE Intra-retinal cyst (IRC) is a symptom of macular disorders that occurs due to retinal blood vessel damage and fluid leakage to the macula area. These abnormalities are efficiently visualized using optical coherence tomography (OCT) imaging. These patients need to be regularly monitored for the presence and changes of IRC regions. Thus, automatic segmentation of IRCs can be beneficial to investigate disease progression. METHODS In this study, automatic IRC segmentation is accomplished by building three different masks in three unsupervised segmentation levels of a hierarchical framework. In the first level, the ROI-mask (R-mask) is built, and the retina area is cropped based on this mask. In the second level, the prune-mask (P-mask) is built, and the searching space is significantly reduced toward the target objects using this mask; and finally in the third level, by applying the Markov random field (MRF) model and employing intensity and contextual information, the cyst mask (C-mask) is extracted. RESULTS The proposed method is evaluated on three datasets including OPTIMA, UMN, and KERMANY datasets. The experimental results showed that the proposed method is effective with a mean dice coefficient rate of 0.74, 0.75 and 0.79 by the intersection of ground truths on the OPTIMA, UMN and KERMANY datasets, respectively. CONCLUSION The proposed method outperforms the state-of-the-art methods on the OPTIMA and UMN datasets while achieving comparable results to the most recently proposed method on the KERMANY dataset.
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Affiliation(s)
- Razieh Ganjee
- The Faculty of Computer Science and Engineering, Shahid Beheshti University G.C, Tehran, Iran
| | | | - Ramin Nourinia
- Ophthalmic Research Center, Shahid Beheshti University of Medical Sciences, Tehran, Iran
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25
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Chen Y, Shi C, Zhou L, Huang S, Shen M, He Z. The Detection of Retina Microvascular Density in Subclinical Aquaporin-4 Antibody Seropositive Neuromyelitis Optica Spectrum Disorders. Front Neurol 2020; 11:35. [PMID: 32117008 PMCID: PMC7026479 DOI: 10.3389/fneur.2020.00035] [Citation(s) in RCA: 22] [Impact Index Per Article: 5.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/29/2019] [Accepted: 01/10/2020] [Indexed: 01/07/2023] Open
Abstract
Purpose: To use optical coherence tomography (OCT) and OCT angiography (OCT-A) to measure changes in the retinal structure and microvasculature of patients with aquaporin-4 antibody-positive, neuromyelitis optica spectrum disorder (NMOSD) with a history of optic neuritis (NMOSD+ON) and those without it (NMOSD–ON). Methods: A total of 27 aquaporin-4 antibody-positive NMOSD patients and 31 age- and gender-matched healthy control (HC) participants were included. In 27 NMOSD patients, 19 of them had a history of optic neuritis (ON) and 8 of them had no history of ON. Peripapillary retinal nerve fiber layer (pRNFL) thickness and macular ganglion cell and inner plexiform layer (GCIPL) thickness were measured by OCT. Radial peripapillary capillary density (RPCD) and macular superficial vessel density (MSVD) were measured by OCT-A. Comparisons of retinal structural and microvascular parameters between the cohorts were performed using generalized estimating equation (GEE) models. Diagnostic accuracy was evaluated by the area under the receiver operating characteristics curve (AROC). Results: In NMOSD+ON eyes, the GCIPL and pRNFL thicknesses, 48.6 ± 7.1 and 61.7 ± 25.1 μm, respectively, were significantly thinner than in HC eyes (P < 0.001 for both). However, in NMOSD–ON eyes, the GCIPL and pRNFL thicknesses were not significantly thinner than in HC eyes (P > 0.05 for both). In NMOSD+ON eyes, the RPCD and MSVD, 37.8 ± 7.1 and 36.7 ± 5.0%, respectively, were significantly less dense than HC eyes (P < 0.001 for both). Similarly, the RPCD and MSVD in NMOSD–ON eyes, 49.0 ± 2.8 and 43.9 ± 4.2%, respectively, were also less dense than in HC eyes (P < 0.029 for RPCD, P < 0.023 for MSVD). The highest AROC, 0.845 (sensitivity = 88.5%, specificity = 78.0%), was achieved by the logistic regression combination of all of the variables, i.e., pRNFL, GCIPL, RPCD, and MSVD. Conclusions: Retinal microvascular changes were present in NMOSD–ON eyes. The combination of retinal structural and microvascular parameters might be helpful to discriminate NMOSD–ON eyes from HC eyes.
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Affiliation(s)
- Yihong Chen
- School of Ophthalmology and Optometry, Wenzhou Medical University, Wenzhou, China.,Department of Neurology, The Second Affiliated Hospital and Yuying Children's Hospital of Wenzhou Medical University, Wenzhou, China
| | - Ce Shi
- School of Ophthalmology and Optometry, Wenzhou Medical University, Wenzhou, China
| | - Lili Zhou
- Department of Neurology, The Second Affiliated Hospital and Yuying Children's Hospital of Wenzhou Medical University, Wenzhou, China
| | - Shenghai Huang
- School of Ophthalmology and Optometry, Wenzhou Medical University, Wenzhou, China
| | - Meixiao Shen
- School of Ophthalmology and Optometry, Wenzhou Medical University, Wenzhou, China
| | - Zhiyong He
- Department of Neurology, The Second Affiliated Hospital and Yuying Children's Hospital of Wenzhou Medical University, Wenzhou, China
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Yang J, Zhu X, Liu Y, Jiang X, Fu J, Ren X, Li K, Qiu W, Li X, Yao J. TMIS: a new image-based software application for the measurement of tear meniscus height. Acta Ophthalmol 2019; 97:e973-e980. [PMID: 31044537 DOI: 10.1111/aos.14107] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/15/2018] [Accepted: 03/14/2019] [Indexed: 12/25/2022]
Abstract
PURPOSE To present a new automated image recognition software for the measurement of tear meniscus height (TMH) and investigate its correlation and efficacy compared with an open-source software (NIH ImageJ) and manual evaluation. METHODS A total of 520 slit lamp photographs, among which 276 were in ×16 magnification and 244 were ×40 magnified, captured from 138 eyes of 69 healthy subjects were assessed for TMH by the new automated Tear Meniscus Identification Software (TMIS), ImageJ and human graders. Images processing of TMIS included filtration, recognition and measurement of slit lamp photographs under certain algorithm, which output two measurement patterns, TMISM ax and TMISM ean . TMH measured by ImageJ software, considered as the reference value, was conducted by a masked observer while four masked ophthalmologists performed the manual evaluation. RESULTS In both magnifications, TMH measured by TMISM ean showed similar values with ImageJ while manual evaluation demonstrated underestimated results, and a strong correlation was detected between TMIS and ImageJ. In ×16 magnified photographs, manually obtained TMH revealed a higher correlation with ImageJ, whereas a notably stronger correlation of TMIS with ImageJ was observed in ×40 photographs. Correspondingly, the accuracy for both TMISM ax and TMISM ean appeared to be lower than most doctors in ×16 slit lamp images, in contrast to a better precision of TMISM ean in ×40 ones. CONCLUSION The new software displayed high accuracy and efficacy in ×40 magnification and TMISM ean pattern, suggesting the possibility of this automated TMH measurement platform to be a valid tool in dry eye screening and follow-up practice.
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Affiliation(s)
- Jiarui Yang
- Department of Ophthalmology Peking University Third Hospital Beijing China
| | - Xingyu Zhu
- Research Centre of Multiphase Flow in Porous Media China University of Petroleum (East China) Qingdao China
| | - Yushi Liu
- Department of Ophthalmology Peking University Third Hospital Beijing China
| | - Xiaodan Jiang
- Department of Ophthalmology Peking University Third Hospital Beijing China
- Beijing key laboratory of restoration of damaged ocular nerve Beijing China
| | - Jiayu Fu
- Department of Ophthalmology Peking University Third Hospital Beijing China
| | - Xiaotong Ren
- Department of Ophthalmology Peking University Third Hospital Beijing China
| | - Kaixiu Li
- Burns and Plastic Department Miyun Teaching Hospital of Capital Medical University Beijing China
| | - Weiqiang Qiu
- Department of Ophthalmology Peking University Third Hospital Beijing China
| | - Xuemin Li
- Department of Ophthalmology Peking University Third Hospital Beijing China
- Beijing key laboratory of restoration of damaged ocular nerve Beijing China
| | - Jun Yao
- Research Centre of Multiphase Flow in Porous Media China University of Petroleum (East China) Qingdao China
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27
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Ruan Y, Xue J, Li T, Liu D, Lu H, Chen M, Liu T, Niu S, Li D. Multi-phase level set algorithm based on fully convolutional networks (FCN-MLS) for retinal layer segmentation in SD-OCT images with central serous chorioretinopathy (CSC). BIOMEDICAL OPTICS EXPRESS 2019; 10:3987-4002. [PMID: 31452990 PMCID: PMC6701532 DOI: 10.1364/boe.10.003987] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.2] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Received: 04/18/2019] [Revised: 06/13/2019] [Accepted: 06/13/2019] [Indexed: 06/10/2023]
Abstract
As a function of the spatial position of the optical coherence tomography (OCT) image, retinal layer thickness is an important diagnostic indicator for many retinal diseases. Reliable segmentation of the retinal layer is necessary for extracting useful clinical information. However, manual segmentation of these layers is time-consuming and prone to bias. Furthermore, due to speckle noise, low image contrast, retinal detachment, and also irregular morphological features make the automatic segmentation task challenging. To alleviate these challenges, in this paper, we propose a new coarse-fine framework combining the full convolutional network (FCN) with a multiphase level set (named FCN-MLS) for automatic segmentation of nine boundaries in retinal spectral OCT images. In the coarse stage, FCN is used to learn the characteristics of specific retinal layer boundaries and achieve classification of four retinal layers. The boundaries are then extracted and the remaining boundaries are initialized based on a priori information about the thickness of the retinal layer. In order to prevent the overlapping of the segmentation interfaces, a regional restriction technique is used in the multi-phase level to evolve the boundaries to achieve fine nine retinal layers segmentation. Experimental results on 1280 B-scans show that the proposed method can segment nine retinal boundaries accurately. Compared with the manual delineation, the overall mean absolute boundary location difference and the overall mean absolute thickness difference were 5.88 ± 2.38μm and 5.81 ± 2.19μm, which showed a good consistency with manual segmentation by the physicians. Our experimental results also outperform state-of-the-art methods.
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Affiliation(s)
- Yanan Ruan
- Shandong Key Laboratory of Medical Physics and Image Processing, Shandong Provincial Engineering and Technical Center of Light Manipulation, School of Physics and Electronics, Shandong Normal University, Jinan, 250014, China
- These authors have contributed equally to this work
| | - Jie Xue
- Shandong Key Laboratory of Medical Physics and Image Processing, Shandong Provincial Engineering and Technical Center of Light Manipulation, School of Physics and Electronics, Shandong Normal University, Jinan, 250014, China
- Business School, Shandong Normal University, Jinan, Shandong, 250014, China
- These authors have contributed equally to this work
| | - Tianlai Li
- Shandong Key Laboratory of Medical Physics and Image Processing, Shandong Provincial Engineering and Technical Center of Light Manipulation, School of Physics and Electronics, Shandong Normal University, Jinan, 250014, China
| | - Danhua Liu
- Shandong Key Laboratory of Medical Physics and Image Processing, Shandong Provincial Engineering and Technical Center of Light Manipulation, School of Physics and Electronics, Shandong Normal University, Jinan, 250014, China
| | - Hua Lu
- Shandong Key Laboratory of Medical Physics and Image Processing, Shandong Provincial Engineering and Technical Center of Light Manipulation, School of Physics and Electronics, Shandong Normal University, Jinan, 250014, China
| | - Meirong Chen
- Affiliated Hospital of Shandong University of Traditional Chinese Medicine, Jinan, Shandong 250014, P. R. China
| | - Tingting Liu
- Shandong Eye Hospital, Shandong Eye Institute, Shandong Academy of Medical Science, Jinan, Shandong 250014, China
| | - Sijie Niu
- School of Information Science and Engineering, University of Jinan, Jinan, 250022, China
| | - Dengwang Li
- Shandong Key Laboratory of Medical Physics and Image Processing, Shandong Provincial Engineering and Technical Center of Light Manipulation, School of Physics and Electronics, Shandong Normal University, Jinan, 250014, China
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28
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Bogunovic H, Venhuizen F, Klimscha S, Apostolopoulos S, Bab-Hadiashar A, Bagci U, Beg MF, Bekalo L, Chen Q, Ciller C, Gopinath K, Gostar AK, Jeon K, Ji Z, Kang SH, Koozekanani DD, Lu D, Morley D, Parhi KK, Park HS, Rashno A, Sarunic M, Shaikh S, Sivaswamy J, Tennakoon R, Yadav S, De Zanet S, Waldstein SM, Gerendas BS, Klaver C, Sanchez CI, Schmidt-Erfurth U. RETOUCH: The Retinal OCT Fluid Detection and Segmentation Benchmark and Challenge. IEEE TRANSACTIONS ON MEDICAL IMAGING 2019; 38:1858-1874. [PMID: 30835214 DOI: 10.1109/tmi.2019.2901398] [Citation(s) in RCA: 75] [Impact Index Per Article: 15.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/23/2023]
Abstract
Retinal swelling due to the accumulation of fluid is associated with the most vision-threatening retinal diseases. Optical coherence tomography (OCT) is the current standard of care in assessing the presence and quantity of retinal fluid and image-guided treatment management. Deep learning methods have made their impact across medical imaging, and many retinal OCT analysis methods have been proposed. However, it is currently not clear how successful they are in interpreting the retinal fluid on OCT, which is due to the lack of standardized benchmarks. To address this, we organized a challenge RETOUCH in conjunction with MICCAI 2017, with eight teams participating. The challenge consisted of two tasks: fluid detection and fluid segmentation. It featured for the first time: all three retinal fluid types, with annotated images provided by two clinical centers, which were acquired with the three most common OCT device vendors from patients with two different retinal diseases. The analysis revealed that in the detection task, the performance on the automated fluid detection was within the inter-grader variability. However, in the segmentation task, fusing the automated methods produced segmentations that were superior to all individual methods, indicating the need for further improvements in the segmentation performance.
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Gao K, Niu S, Ji Z, Wu M, Chen Q, Xu R, Yuan S, Fan W, Chen Y, Dong J. Double-branched and area-constraint fully convolutional networks for automated serous retinal detachment segmentation in SD-OCT images. COMPUTER METHODS AND PROGRAMS IN BIOMEDICINE 2019; 176:69-80. [PMID: 31200913 DOI: 10.1016/j.cmpb.2019.04.027] [Citation(s) in RCA: 16] [Impact Index Per Article: 3.2] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 12/19/2018] [Revised: 04/17/2019] [Accepted: 04/23/2019] [Indexed: 06/09/2023]
Abstract
BACKGROUND AND OBJECTIVE Quantitative assessment of subretinal fluid in spectral domain optical coherence tomography (SD-OCT) images is crucial for the diagnosis of central serous chorioretinopathy. For the subretinal fluid segmentation, the traditional methods need to segment retinal layers and then segment subretinal fluid. The layer segmentation has a high influence on subretinal fluid segmentation, so we aim to develop a deep learning model to segment subretinal fluid automatically without layer segmentation. METHODS In this paper, we propose a novel image-to-image double-branched and area-constraint fully convolutional networks (DA-FCN) for segmenting subretinal fluid in SD-OCT images. Firstly, the dataset is extended by mirroring image, which helps to overcome the over-fitting problem in the training stage. Then, double-branched structures are designed to learn the shallow coarse and deep representations from the SD-OCT images. DA-FCN model is directly trained using the image and corresponding pixel-based ground truth. Finally, we introduce a novel supervision mechanism by jointing the area loss LA with the softmax loss LS to learn more representative features. RESULTS The testing dataset with 52 SD-OCT volumes from 35 eyes of 35 patients is used for the evaluation of the proposed algorithm based on the cross-validation method. For the three criterions, including the true positive volume fraction, dice similarity coefficient, and positive predicative value, our method can obtain the results of (1) 94.3, 95.3, and 96.4 for dataset 1; (2) 97.3, 95.3, and 93.4 for dataset 2; (3) 93.0, 92.8, and 92.8 for dataset 3; (4) 89.7, 90.1, and 92.6 for dataset 4. CONCLUSION In this work, we propose a novel fully convolutional network for the automatic segmentation of the subretinal fluid. By constructing the double branched structures and area constraint term, our method shows higher segmentation accuracy without layer segmentation compared with other methods.
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Affiliation(s)
- Kun Gao
- Shandong Provincial Key Laboratory of Network based Intelligent Computing, School of Information Science and Engineering, University of Jinan, Jinan 250022, China
| | - Sijie Niu
- Shandong Provincial Key Laboratory of Network based Intelligent Computing, School of Information Science and Engineering, University of Jinan, Jinan 250022, China.
| | - Zexuan Ji
- School of Computer Science and Engineering, Nanjing University of Science and Technology, Nanjing 210094, China
| | - Menglin Wu
- School of Computer Science and Technology, Nanjing Tech University, Nanjing 210094, China
| | - Qiang Chen
- School of Computer Science and Engineering, Nanjing University of Science and Technology, Nanjing 210094, China
| | - Rongbin Xu
- Shandong Provincial Key Laboratory of Network based Intelligent Computing, School of Information Science and Engineering, University of Jinan, Jinan 250022, China
| | - Songtao Yuan
- Department of Ophthalmology, The First Affiliated Hospital with Nanjing Medical University, Nanjing 210094, China
| | - Wen Fan
- Department of Ophthalmology, The First Affiliated Hospital with Nanjing Medical University, Nanjing 210094, China
| | - Yuehui Chen
- Shandong Provincial Key Laboratory of Network based Intelligent Computing, School of Information Science and Engineering, University of Jinan, Jinan 250022, China
| | - Jiwen Dong
- Shandong Provincial Key Laboratory of Network based Intelligent Computing, School of Information Science and Engineering, University of Jinan, Jinan 250022, China
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30
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Verticchio Vercellin AC, Jassim F, Poon LYC, Tsikata E, Braaf B, Shah S, Ben-David G, Shieh E, Lee R, Simavli H, Que CJ, Papadogeorgou G, Guo R, Vakoc BJ, Bouma BE, de Boer JF, Chen TC. Diagnostic Capability of Three-Dimensional Macular Parameters for Glaucoma Using Optical Coherence Tomography Volume Scans. Invest Ophthalmol Vis Sci 2019; 59:4998-5010. [PMID: 30326067 PMCID: PMC6188465 DOI: 10.1167/iovs.18-23813] [Citation(s) in RCA: 11] [Impact Index Per Article: 2.2] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/24/2022] Open
Abstract
Purpose To compare the diagnostic capability of three-dimensional (3D) macular parameters against traditional two-dimensional (2D) retinal nerve fiber layer (RNFL) thickness using spectral domain optical coherence tomography. To determine if manual correction and interpolation of B-scans improve the ability of 3D macular parameters to diagnose glaucoma. Methods A total of 101 open angle glaucoma patients (29 with early glaucoma) and 57 healthy subjects had peripapillary 2D RNFL thickness and 3D macular volume scans. Four parameters were calculated for six different-sized annuli: total macular thickness (M-thickness), total macular volume (M-volume), ganglion cell complex (GCC) thickness, and GCC volume of the innermost 3 macular layers (retinal nerve fiber layer + ganglion cell layer + inner plexiform layer). All macular parameters were calculated with and without correction and interpolation of frames with artifacts. The areas under the receiver operating characteristic curves (AUROC) were calculated for all the parameters. Results The 3D macular parameter with the best diagnostic performance was GCC-volume-34, with an inner diameter of 3 mm and an outer of 4 mm. The AUROC for RNFL thickness and GCC-volume-34 were statistically similar for all regions (global: RNFL thickness 0.956, GCC-volume-34 0.939, P value = 0.3827), except for the temporal GCC-volume-34, which was significantly better than temporal RNFL thickness (P value = 0.0067). Correction of artifacts did not significantly change the AUROC of macular parameters (P values between 0.8452 and 1.0000). Conclusions The diagnostic performance of best macular parameters (GCC-volume-34 and GCC-thickness-34) were similar to or better than 2D RNFL thickness. Manual correction of artifacts with data interpolation is unnecessary in the clinical setting.
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Affiliation(s)
- Alice C Verticchio Vercellin
- University Eye Clinic, Istituto di Ricovero e Cura a Carattere Scientifico (IRCCS), Policlinico San Matteo, Pavia, Italy.,IRCCS-Fondazione Bietti, Rome, Italy.,Massachusetts Eye and Ear Infirmary, Department of Ophthalmology, Glaucoma Service, Boston, Massachusetts, United States.,Harvard Medical School, Boston, Massachusetts, United States
| | - Firas Jassim
- Massachusetts Eye and Ear Infirmary, Department of Ophthalmology, Glaucoma Service, Boston, Massachusetts, United States.,Harvard Medical School, Boston, Massachusetts, United States
| | - Linda Yi-Chieh Poon
- Massachusetts Eye and Ear Infirmary, Department of Ophthalmology, Glaucoma Service, Boston, Massachusetts, United States.,Harvard Medical School, Boston, Massachusetts, United States.,Kaohsiung Chang Gung Memorial Hospital, Chang Gung University College of Medicine, Department of Ophthalmology, Kaohsiung, Taiwan
| | - Edem Tsikata
- Massachusetts Eye and Ear Infirmary, Department of Ophthalmology, Glaucoma Service, Boston, Massachusetts, United States.,Harvard Medical School, Boston, Massachusetts, United States
| | - Boy Braaf
- Harvard Medical School, Boston, Massachusetts, United States.,Wellman Center for Photomedicine, Massachusetts General Hospital, Boston, Massachusetts, United States
| | - Sneha Shah
- Massachusetts Eye and Ear Infirmary, Department of Ophthalmology, Glaucoma Service, Boston, Massachusetts, United States.,Johns Hopkins School of Medicine, Baltimore, Maryland, United States
| | - Geulah Ben-David
- Massachusetts Eye and Ear Infirmary, Department of Ophthalmology, Glaucoma Service, Boston, Massachusetts, United States.,Harvard Medical School, Boston, Massachusetts, United States.,Sackler School of Medicine, Tel Aviv University, Tel Aviv, Israel
| | - Eric Shieh
- Massachusetts Eye and Ear Infirmary, Department of Ophthalmology, Glaucoma Service, Boston, Massachusetts, United States.,Harvard Medical School, Boston, Massachusetts, United States.,Jules Stein Eye Institute, David Geffen School of Medicine, University of California, Los Angeles, California, United States
| | - Ramon Lee
- Massachusetts Eye and Ear Infirmary, Department of Ophthalmology, Glaucoma Service, Boston, Massachusetts, United States.,Harvard Medical School, Boston, Massachusetts, United States.,University of Southern California Roski Eye Institute, Department of Ophthalmology, Keck School of Medicine, Los Angeles, California, United States
| | - Huseyin Simavli
- Massachusetts Eye and Ear Infirmary, Department of Ophthalmology, Glaucoma Service, Boston, Massachusetts, United States.,Harvard Medical School, Boston, Massachusetts, United States.,Kudret Eye Hospital, Kadikoy, Istanbul, Turkey
| | - Christian J Que
- Massachusetts Eye and Ear Infirmary, Department of Ophthalmology, Glaucoma Service, Boston, Massachusetts, United States.,Harvard Medical School, Boston, Massachusetts, United States.,University of the East Ramon Magsaysay Memorial Medical Center, Quezon City, Philippines.,Romblon Provincial Hospital, Liwanag, Odiongan, Romblon, Philippines
| | - Georgia Papadogeorgou
- Harvard School of Public Health, Department of Biostatistics, Boston, Massachusetts, United States
| | - Rong Guo
- Harvard Medical School, Boston, Massachusetts, United States.,University of California, Los Angeles, Department of Internal Medicine, Los Angeles, California, United States
| | - Benjamin J Vakoc
- Harvard Medical School, Boston, Massachusetts, United States.,Wellman Center for Photomedicine, Massachusetts General Hospital, Boston, Massachusetts, United States
| | - Brett E Bouma
- Harvard Medical School, Boston, Massachusetts, United States.,Wellman Center for Photomedicine, Massachusetts General Hospital, Boston, Massachusetts, United States
| | - Johannes F de Boer
- LaserLaB Amsterdam, Department of Physics and Astronomy, Vrije Universiteit, The Netherlands.,Department of Ophthalmology, VU Medical Center, The Netherlands
| | - Teresa C Chen
- Massachusetts Eye and Ear Infirmary, Department of Ophthalmology, Glaucoma Service, Boston, Massachusetts, United States.,Harvard Medical School, Boston, Massachusetts, United States
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31
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Xiang D, Chen G, Shi F, Zhu W, Liu Q, Yuan S, Chen X. Automatic Retinal Layer Segmentation of OCT Images With Central Serous Retinopathy. IEEE J Biomed Health Inform 2019; 23:283-295. [DOI: 10.1109/jbhi.2018.2803063] [Citation(s) in RCA: 24] [Impact Index Per Article: 4.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/08/2022]
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32
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Rong Y, Xiang D, Zhu W, Yu K, Shi F, Fan Z, Chen X. Surrogate-Assisted Retinal OCT Image Classification Based on Convolutional Neural Networks. IEEE J Biomed Health Inform 2019; 23:253-263. [DOI: 10.1109/jbhi.2018.2795545] [Citation(s) in RCA: 52] [Impact Index Per Article: 10.4] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/10/2022]
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33
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Sawyer TW, Chandra S, Rice PFS, Koevary JW, Barton JK. Three-dimensional texture analysis of optical coherence tomography images of ovarian tissue. Phys Med Biol 2018; 63:235020. [PMID: 30511664 PMCID: PMC6934175 DOI: 10.1088/1361-6560/aaefd2] [Citation(s) in RCA: 16] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/13/2023]
Abstract
Ovarian cancer has the lowest survival rate among all gynecologic cancers due to predominantly late diagnosis. Optical coherence tomography (OCT) has been applied successfully to experimentally image the ovaries in vivo; however, a robust method for analysis is still required to provide quantitative diagnostic information. Recently, texture analysis has proved to be a useful tool for tissue characterization; unfortunately, existing work in the scope of OCT ovarian imaging is limited to only analyzing 2D sub-regions of the image data, discarding information encoded in the full image area, as well as in the depth dimension. Here we address these challenges by testing three implementations of texture analysis for the ability to classify tissue type. First, we test the traditional case of extracted 2D regions of interest; then we extend this to include the entire image area by segmenting the organ from the background. Finally, we conduct a full volumetric analysis of the image volume using 3D segmented data. For each case, we compute features based on the Grey-Level Co-occurence Matrix and also by introducing a new approach that evaluates the frequency distribution in the image by computing the energy density. We test these methods on a mouse model of ovarian cancer to differentiate between age, genotype, and treatment. The results show that the 3D application of texture analysis is most effective for differentiating tissue types, yielding an average classification accuracy of 78.6%. This is followed by the analysis in 2D with the segmented image volume, yielding an average accuracy of 71.5%. Both of these improve on the traditional approach of extracting square regions of interest, which yield an average classification accuracy of 67.7%. Thus, applying texture analysis in 3D with a fully segmented image volume is the most robust approach to quantitatively characterizing ovarian tissue.
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Affiliation(s)
- Travis W Sawyer
- College of Optical Sciences, The University of Arizona, Tucson 85721, AZ, United States of America
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34
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Gan M, Wang C, Yang T, Yang N, Zhang M, Yuan W, Li X, Wang L. Robust layer segmentation of esophageal OCT images based on graph search using edge-enhanced weights. BIOMEDICAL OPTICS EXPRESS 2018; 9:4481-4495. [PMID: 30615715 PMCID: PMC6157790 DOI: 10.1364/boe.9.004481] [Citation(s) in RCA: 5] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 06/01/2018] [Revised: 08/17/2018] [Accepted: 08/20/2018] [Indexed: 05/18/2023]
Abstract
Automatic segmentation of esophageal layers in OCT images is crucial for studying esophageal diseases and computer-assisted diagnosis. This work aims to improve the current techniques to increase the accuracy and robustness for esophageal OCT image segmentation. A two-step edge-enhanced graph search (EEGS) framework is proposed in this study. Firstly, a preprocessing scheme is applied to suppress speckle noise and remove the disturbance in the esophageal structure. Secondly, the image is formulated into a graph and layer boundaries are located by graph search. In this process, we propose an edge-enhanced weight matrix for the graph by combining the vertical gradients with a Canny edge map. Experiments on esophageal OCT images from guinea pigs demonstrate that the EEGS framework is more robust and more accurate than the current segmentation method. It can be potentially useful for the early detection of esophageal diseases.
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Affiliation(s)
- Meng Gan
- Department of Electronic and Information Engineering, Soochow University, Suzhou 215006,
China
- Suzhou Institute of Biomedical Engineering and Technology, Chinese Academy of Sciences, Suzhou 215163,
China
| | - Cong Wang
- Suzhou Institute of Biomedical Engineering and Technology, Chinese Academy of Sciences, Suzhou 215163,
China
| | - Ting Yang
- Department of Electronic and Information Engineering, Soochow University, Suzhou 215006,
China
| | - Na Yang
- Department of Electronic and Information Engineering, Soochow University, Suzhou 215006,
China
| | - Miao Zhang
- Department of Electronic and Information Engineering, Soochow University, Suzhou 215006,
China
| | - Wu Yuan
- Department of Biomedical Engineering, Johns Hopkins University, Baltimore, MD 21205,
USA
| | - Xingde Li
- Department of Biomedical Engineering, Johns Hopkins University, Baltimore, MD 21205,
USA
| | - Lirong Wang
- Department of Electronic and Information Engineering, Soochow University, Suzhou 215006,
China
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Rasti R, Rabbani H, Mehridehnavi A, Hajizadeh F. Macular OCT Classification Using a Multi-Scale Convolutional Neural Network Ensemble. IEEE TRANSACTIONS ON MEDICAL IMAGING 2018; 37:1024-1034. [PMID: 29610079 DOI: 10.1109/tmi.2017.2780115] [Citation(s) in RCA: 88] [Impact Index Per Article: 14.7] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/20/2023]
Abstract
Computer-aided diagnosis (CAD) of retinal pathologies is a current active area in medical image analysis. Due to the increasing use of retinal optical coherence tomography (OCT) imaging technique, a CAD system in retinal OCT is essential to assist ophthalmologist in the early detection of ocular diseases and treatment monitoring. This paper presents a novel CAD system based on a multi-scale convolutional mixture of expert (MCME) ensemble model to identify normal retina, and two common types of macular pathologies, namely, dry age-related macular degeneration, and diabetic macular edema. The proposed MCME modular model is a data-driven neural structure, which employs a new cost function for discriminative and fast learning of image features by applying convolutional neural networks on multiple-scale sub-images. MCME maximizes the likelihood function of the training data set and ground truth by considering a mixture model, which tries also to model the joint interaction between individual experts by using a correlated multivariate component for each expert module instead of only modeling the marginal distributions by independent Gaussian components. Two different macular OCT data sets from Heidelberg devices were considered for the evaluation of the method, i.e., a local data set of OCT images of 148 subjects and a public data set of 45 OCT acquisitions. For comparison purpose, we performed a wide range of classification measures to compare the results with the best configurations of the MCME method. With the MCME model of four scale-dependent experts, the precision rate of 98.86%, and the area under the receiver operating characteristic curve (AUC) of 0.9985 were obtained on average.
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36
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Clinical Analysis of Macular Edema with New Software for SD-OCT Imaging. Eur J Ophthalmol 2018; 23:899-904. [DOI: 10.5301/ejo.5000329] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 06/03/2013] [Indexed: 11/20/2022]
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Girish GN, Thakur B, Chowdhury SR, Kothari AR, Rajan J. Segmentation of Intra-Retinal Cysts From Optical Coherence Tomography Images Using a Fully Convolutional Neural Network Model. IEEE J Biomed Health Inform 2018; 23:296-304. [PMID: 29994161 DOI: 10.1109/jbhi.2018.2810379] [Citation(s) in RCA: 57] [Impact Index Per Article: 9.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/09/2022]
Abstract
Optical coherence tomography (OCT) is an imaging modality that is used extensively for ophthalmic diagnosis, near-histological visualization, and quantification of retinal abnormalities such as cysts, exudates, retinal layer disorganization, etc. Intra-retinal cysts (IRCs) occur in several macular disorders such as, diabetic macular edema, retinal vascular disorders, age-related macular degeneration, and inflammatory disorders. Automated segmentation of IRCs poses challenges owing to variations in the acquisition system scan intensities, speckle noise, and imaging artifacts. Several segmentation methods have been proposed in the literature for IRC segmentation on vendor-specific OCT images that lack generalizability across imaging systems. In this paper, we propose a fully convolutional network (FCN) model for vendor-independent IRC segmentation. The proposed method counteracts image noise variabilities and trains FCN models on OCT sub-images from the OPTIMA cyst segmentation challenge dataset (with four different vendor-specific images, namely, Cirrus, Nidek, Spectralis, and Topcon). Further, optimal data augmentation and model hyperparametrization are shown to prevent over-fitting for IRC area segmentation. The proposed method is evaluated on the test dataset with a recall/precision rate of 0.66/0.79 across imaging vendors. The Dice correlation coefficient of the proposed method outperforms that of the published algorithms in the OPTIMA cyst segmentation challenge with a Dice rate of 0.71 across the vendors.
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Abstract
Medical image segmentation is a fundamental and challenging problem for analyzing medical images. Among different existing medical image segmentation methods, graph-based approaches are relatively new and show good features in clinical applications. In the graph-based method, pixels or regions in the original image are interpreted into nodes in a graph. By considering Markov random field to model the contexture information of the image, the medical image segmentation problem can be transformed into a graph-based energy minimization problem. This problem can be solved by the use of minimum s-t cut/ maximum flow algorithm. This review is devoted to cut-based medical segmentation methods, including graph cuts and graph search for region and surface segmentation. Different varieties of cut-based methods, including graph-cuts-based methods, model integrated graph cuts methods, graph-search-based methods, and graph search/graph cuts based methods, are systematically reviewed. Graph cuts and graph search with deep learning technique are also discussed.
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Girish GN, Anima VA, Kothari AR, Sudeep PV, Roychowdhury S, Rajan J. A benchmark study of automated intra-retinal cyst segmentation algorithms using optical coherence tomography B-scans. COMPUTER METHODS AND PROGRAMS IN BIOMEDICINE 2018; 153:105-114. [PMID: 29157443 DOI: 10.1016/j.cmpb.2017.10.010] [Citation(s) in RCA: 10] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 03/03/2017] [Revised: 09/25/2017] [Accepted: 10/10/2017] [Indexed: 05/25/2023]
Abstract
(BACKGROUND AND OBJECTIVES) Retinal cysts are formed by accumulation of fluid in the retina caused by leakages from inflammation or vitreous fractures. Analysis of the retinal cystic spaces holds significance in detection and treatment of several ocular diseases like age-related macular degeneration, diabetic macular edema etc. Thus, segmentation of intra-retinal cysts and quantification of cystic spaces are vital for retinal pathology and severity detection. In the recent years, automated segmentation of intra-retinal cysts using optical coherence tomography B-scans has gained significant importance in the field of retinal image analysis. The objective of this paper is to compare different intra-retinal cyst segmentation algorithms for comparative analysis and benchmarking purposes. (METHODS) In this work, we employ a modular approach for standardizing the different segmentation algorithms. Further, we analyze the variations in automated cyst segmentation performances and method scalability across image acquisition systems by using the publicly available cyst segmentation challenge dataset (OPTIMA cyst segmentation challenge). (RESULTS) Several key automated methods are comparatively analyzed using quantitative and qualitative experiments. Our analysis demonstrates the significance of variations in signal-to-noise ratio (SNR), retinal layer morphology and post-processing steps on the automated cyst segmentation processes. (CONCLUSION) This benchmarking study provides insights towards the scalability of automated processes across vendor-specific imaging modalities to provide guidance for retinal pathology diagnostics and treatment processes.
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Affiliation(s)
- G N Girish
- Department of Computer Science and Engineering, National Institute of Technology Karnataka, Surathkal, India.
| | - V A Anima
- Department of Computer Science and Engineering, National Institute of Technology Karnataka, Surathkal, India
| | | | - P V Sudeep
- Department of Electronics and Communication Engineering, National Institute of Technology Karnataka, Surathkal, India; Department of Electronics and Communication Engineering, Manipal Institute of Technology, Manipal, India
| | - Sohini Roychowdhury
- Department of Electrical and Computer Engineering, University of Washington, Bothell, USA
| | - Jeny Rajan
- Department of Computer Science and Engineering, National Institute of Technology Karnataka, Surathkal, India
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Rashno A, Nazari B, Koozekanani DD, Drayna PM, Sadri S, Rabbani H, Parhi KK. Fully-automated segmentation of fluid regions in exudative age-related macular degeneration subjects: Kernel graph cut in neutrosophic domain. PLoS One 2017; 12:e0186949. [PMID: 29059257 PMCID: PMC5653365 DOI: 10.1371/journal.pone.0186949] [Citation(s) in RCA: 26] [Impact Index Per Article: 3.7] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/20/2017] [Accepted: 10/10/2017] [Indexed: 11/19/2022] Open
Abstract
A fully-automated method based on graph shortest path, graph cut and neutrosophic (NS) sets is presented for fluid segmentation in OCT volumes for exudative age related macular degeneration (EAMD) subjects. The proposed method includes three main steps: 1) The inner limiting membrane (ILM) and the retinal pigment epithelium (RPE) layers are segmented using proposed methods based on graph shortest path in NS domain. A flattened RPE boundary is calculated such that all three types of fluid regions, intra-retinal, sub-retinal and sub-RPE, are located above it. 2) Seed points for fluid (object) and tissue (background) are initialized for graph cut by the proposed automated method. 3) A new cost function is proposed in kernel space, and is minimized with max-flow/min-cut algorithms, leading to a binary segmentation. Important properties of the proposed steps are proven and quantitative performance of each step is analyzed separately. The proposed method is evaluated using a publicly available dataset referred as Optima and a local dataset from the UMN clinic. For fluid segmentation in 2D individual slices, the proposed method outperforms the previously proposed methods by 18%, 21% with respect to the dice coefficient and sensitivity, respectively, on the Optima dataset, and by 16%, 11% and 12% with respect to the dice coefficient, sensitivity and precision, respectively, on the local UMN dataset. Finally, for 3D fluid volume segmentation, the proposed method achieves true positive rate (TPR) and false positive rate (FPR) of 90% and 0.74%, respectively, with a correlation of 95% between automated and expert manual segmentations using linear regression analysis.
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Affiliation(s)
- Abdolreza Rashno
- Department of Electrical and Computer Engineering, Isfahan University of Technology, Isfahan, Iran
- Department of Electrical and Computer Engineering, University of Minnesota, Minneapolis, MN, United States of America
| | - Behzad Nazari
- Department of Electrical and Computer Engineering, Isfahan University of Technology, Isfahan, Iran
| | - Dara D. Koozekanani
- Department of Ophthalmology and Visual Neurosciences, University of Minnesota, Minneapolis, MN, United States of America
| | - Paul M. Drayna
- Department of Ophthalmology and Visual Neurosciences, University of Minnesota, Minneapolis, MN, United States of America
| | - Saeed Sadri
- Department of Electrical and Computer Engineering, Isfahan University of Technology, Isfahan, Iran
| | - Hossein Rabbani
- Department of Biomedical Engineering, Medical Image and Signal Processing Research Center, Isfahan University of Medical Sciences, Isfahan, Iran
| | - Keshab K. Parhi
- Department of Electrical and Computer Engineering, University of Minnesota, Minneapolis, MN, United States of America
- * E-mail:
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Wu M, Fan W, Chen Q, Du Z, Li X, Yuan S, Park H. Three-dimensional continuous max flow optimization-based serous retinal detachment segmentation in SD-OCT for central serous chorioretinopathy. BIOMEDICAL OPTICS EXPRESS 2017; 8:4257-4274. [PMID: 28966863 PMCID: PMC5611939 DOI: 10.1364/boe.8.004257] [Citation(s) in RCA: 10] [Impact Index Per Article: 1.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 06/21/2017] [Revised: 07/29/2017] [Accepted: 08/22/2017] [Indexed: 05/28/2023]
Abstract
Assessment of serous retinal detachment plays an important role in the diagnosis of central serous chorioretinopathy (CSC). In this paper, we propose an automatic, three-dimensional segmentation method to detect both neurosensory retinal detachment (NRD) and pigment epithelial detachment (PED) in spectral domain optical coherence tomography (SD-OCT) images. The proposed method involves constructing a probability map from training samples using random forest classification. The probability map is constructed from a linear combination of structural texture, intensity, and layer thickness information. Then, a continuous max flow optimization algorithm is applied to the probability map to segment the retinal detachment-associated fluid regions. Experimental results from 37 retinal SD-OCT volumes from cases of CSC demonstrate the proposed method can achieve a true positive volume fraction (TPVF), false positive volume fraction (FPVF), positive predicative value (PPV), and dice similarity coefficient (DSC) of 92.1%, 0.53%, 94.7%, and 93.3%, respectively, for NRD segmentation and 92.5%, 0.14%, 80.9%, and 84.6%, respectively, for PED segmentation. The proposed method can be an automatic tool to evaluate serous retinal detachment and has the potential to improve the clinical evaluation of CSC.
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Affiliation(s)
- Menglin Wu
- School of Computer Science and Technology, Nanjing Tech University, Nanjing, China
- These authors contributed equally to this manuscript
| | - Wen Fan
- Department of Ophthalmology, First Affiliated Hospital with Nanjing Medical University, Nanjing, China
- These authors contributed equally to this manuscript
| | - Qiang Chen
- School of Computer Science and Engineering, Nanjing University of Science and Technology, Nanjing, China
| | - Zhenlong Du
- School of Computer Science and Technology, Nanjing Tech University, Nanjing, China
| | - Xiaoli Li
- School of Computer Science and Technology, Nanjing Tech University, Nanjing, China
| | - Songtao Yuan
- Department of Ophthalmology, First Affiliated Hospital with Nanjing Medical University, Nanjing, China
| | - Hyunjin Park
- School of Electronic and Electrical Engineering, Sungkyunkwan University, South Korea
- Center for Neuroscience Imaging Research, Institute for Basic Science (IBS), South Korea
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Guo Z, Kwon YH, Lee K, Wang K, Wahle A, Alward WLM, Fingert JH, Bettis DI, Johnson CA, Garvin MK, Sonka M, Abràmoff MD. Optical Coherence Tomography Analysis Based Prediction of Humphrey 24-2 Visual Field Thresholds in Patients With Glaucoma. Invest Ophthalmol Vis Sci 2017; 58:3975-3985. [PMID: 28796875 PMCID: PMC5552000 DOI: 10.1167/iovs.17-21832] [Citation(s) in RCA: 34] [Impact Index Per Article: 4.9] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/24/2022] Open
Abstract
Purpose A pilot study showed that prediction of individual Humphrey 24-2 visual field (HVF 24-2) sensitivity thresholds from optical coherence tomography (OCT) image analysis is possible. We evaluate performance of an improved approach as well as 3 other predictive algorithms on a new, fully independent set of glaucoma subjects. Methods Subjects underwent HVF 24-2 and 9-field OCT (Heidelberg Spectralis) testing. Nerve fiber (NFL), and ganglion cell and inner plexiform (GCL+IPL) layers were cosegmented and partitioned into 52 sectors matching HVF 24-2 test locations. The Wilcoxon rank sum test was applied to test correlation R, root mean square error (RMSE), and limits of agreement (LoA) between actual and predicted thresholds for four prediction models. The training data consisted of the 9-field OCT and HVF 24-2 thresholds of 111 glaucoma patients from our pilot study. Results We studied 112 subjects (112 eyes) with early, moderate, or advanced primary and secondary open angle glaucoma. Subjects with less than 9 scans (15/112) or insufficient quality segmentations (11/97) were excluded. Retinal ganglion cell axonal complex (RGC-AC) optimized had superior average R = 0.74 (95% confidence interval [CI], 0.67-0.76) and RMSE = 5.42 (95% CI, 5.1-5.7) dB, which was significantly better (P < 0.05/3) than the other three models: Naïve (R = 0.49; 95% CI, 0.44-0.54; RMSE = 7.24 dB; 95% CI, 6.6-7.8 dB), Garway-Heath (R = 0.66; 95% CI, 0.60-0.68; RMSE = 6.07 dB; 95% CI, 5.7-6.5 dB), and Donut (R = 0.67; 95% CI, 0.61-0.69; RMSE = 6.08 dB, 95% CI, 5.8-6.4 dB). Conclusions The proposed RGC-AC optimized predictive algorithm based on 9-field OCT image analysis and the RGC-AC concept is superior to previous methods and its performance is close to the reproducibility of HVF 24-2.
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Affiliation(s)
- Zhihui Guo
- Department of Biomedical Engineering, University of Iowa, Iowa City, Iowa, United States
| | - Young H Kwon
- Stephen A. Wynn Institute for Vision Research, University of Iowa, Iowa City, Iowa, United States.,Department of Ophthalmology and Visual Sciences, University of Iowa, Iowa City, Iowa, United States
| | - Kyungmoo Lee
- Department of Electrical and Computer Engineering, University of Iowa, Iowa City, Iowa, United States
| | - Kai Wang
- Department of Biostatistics, College of Public Health, University of Iowa, Iowa City, Iowa, United States
| | - Andreas Wahle
- Department of Electrical and Computer Engineering, University of Iowa, Iowa City, Iowa, United States
| | - Wallace L M Alward
- Stephen A. Wynn Institute for Vision Research, University of Iowa, Iowa City, Iowa, United States.,Department of Ophthalmology and Visual Sciences, University of Iowa, Iowa City, Iowa, United States
| | - John H Fingert
- Stephen A. Wynn Institute for Vision Research, University of Iowa, Iowa City, Iowa, United States.,Department of Ophthalmology and Visual Sciences, University of Iowa, Iowa City, Iowa, United States
| | - Daniel I Bettis
- Department of Ophthalmology and Visual Sciences, University of Iowa, Iowa City, Iowa, United States
| | - Chris A Johnson
- Department of Ophthalmology and Visual Sciences, University of Iowa, Iowa City, Iowa, United States
| | - Mona K Garvin
- Department of Electrical and Computer Engineering, University of Iowa, Iowa City, Iowa, United States.,Iowa City VA Health Care System, Iowa City, Iowa, United States
| | - Milan Sonka
- Department of Ophthalmology and Visual Sciences, University of Iowa, Iowa City, Iowa, United States.,Department of Electrical and Computer Engineering, University of Iowa, Iowa City, Iowa, United States
| | - Michael D Abràmoff
- Department of Biomedical Engineering, University of Iowa, Iowa City, Iowa, United States.,Stephen A. Wynn Institute for Vision Research, University of Iowa, Iowa City, Iowa, United States.,Department of Ophthalmology and Visual Sciences, University of Iowa, Iowa City, Iowa, United States.,Department of Electrical and Computer Engineering, University of Iowa, Iowa City, Iowa, United States.,Iowa City VA Health Care System, Iowa City, Iowa, United States
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The Edge Detectors Suitable for Retinal OCT Image Segmentation. JOURNAL OF HEALTHCARE ENGINEERING 2017; 2017:3978410. [PMID: 29065594 PMCID: PMC5585566 DOI: 10.1155/2017/3978410] [Citation(s) in RCA: 5] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 05/04/2017] [Accepted: 07/19/2017] [Indexed: 11/18/2022]
Abstract
Retinal layer thickness measurement offers important information for reliable diagnosis of retinal diseases and for the evaluation of disease development and medical treatment responses. This task critically depends on the accurate edge detection of the retinal layers in OCT images. Here, we intended to search for the most suitable edge detectors for the retinal OCT image segmentation task. The three most promising edge detection algorithms were identified in the related literature: Canny edge detector, the two-pass method, and the EdgeFlow technique. The quantitative evaluation results show that the two-pass method outperforms consistently the Canny detector and the EdgeFlow technique in delineating the retinal layer boundaries in the OCT images. In addition, the mean localization deviation metrics show that the two-pass method caused the smallest edge shifting problem. These findings suggest that the two-pass method is the best among the three algorithms for detecting retinal layer boundaries. The overall better performance of Canny and two-pass methods over EdgeFlow technique implies that the OCT images contain more intensity gradient information than texture changes along the retinal layer boundaries. The results will guide our future efforts in the quantitative analysis of retinal OCT images for the effective use of OCT technologies in the field of ophthalmology.
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Rashno A, Koozekanani DD, Drayna PM, Nazari B, Sadri S, Rabbani H, Parhi KK. Fully Automated Segmentation of Fluid/Cyst Regions in Optical Coherence Tomography Images With Diabetic Macular Edema Using Neutrosophic Sets and Graph Algorithms. IEEE Trans Biomed Eng 2017; 65:989-1001. [PMID: 28783619 DOI: 10.1109/tbme.2017.2734058] [Citation(s) in RCA: 37] [Impact Index Per Article: 5.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/06/2022]
Abstract
This paper presents a fully automated algorithm to segment fluid-associated (fluid-filled) and cyst regions in optical coherence tomography (OCT) retina images of subjects with diabetic macular edema. The OCT image is segmented using a novel neutrosophic transformation and a graph-based shortest path method. In neutrosophic domain, an image is transformed into three sets: (true), (indeterminate) that represents noise, and (false). This paper makes four key contributions. First, a new method is introduced to compute the indeterminacy set , and a new -correction operation is introduced to compute the set in neutrosophic domain. Second, a graph shortest-path method is applied in neutrosophic domain to segment the inner limiting membrane and the retinal pigment epithelium as regions of interest (ROI) and outer plexiform layer and inner segment myeloid as middle layers using a novel definition of the edge weights . Third, a new cost function for cluster-based fluid/cyst segmentation in ROI is presented which also includes a novel approach in estimating the number of clusters in an automated manner. Fourth, the final fluid regions are achieved by ignoring very small regions and the regions between middle layers. The proposed method is evaluated using two publicly available datasets: Duke, Optima, and a third local dataset from the UMN clinic which is available online. The proposed algorithm outperforms the previously proposed Duke algorithm by 8% with respect to the dice coefficient and by 5% with respect to precision on the Duke dataset, while achieving about the same sensitivity. Also, the proposed algorithm outperforms a prior method for Optima dataset by 6%, 22%, and 23% with respect to the dice coefficient, sensitivity, and precision, respectively. Finally, the proposed algorithm also achieves sensitivity of 67.3%, 88.8%, and 76.7%, for the Duke, Optima, and the university of minnesota (UMN) datasets, respectively.
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Wintergerst MWM, Schultz T, Birtel J, Schuster AK, Pfeiffer N, Schmitz-Valckenberg S, Holz FG, Finger RP. Algorithms for the Automated Analysis of Age-Related Macular Degeneration Biomarkers on Optical Coherence Tomography: A Systematic Review. Transl Vis Sci Technol 2017; 6:10. [PMID: 28729948 PMCID: PMC5516568 DOI: 10.1167/tvst.6.4.10] [Citation(s) in RCA: 26] [Impact Index Per Article: 3.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/04/2017] [Accepted: 05/30/2017] [Indexed: 11/24/2022] Open
Abstract
PURPOSE To assess the quality of optical coherence tomography (OCT) grading algorithms for retinal biomarkers of age-related macular degeneration (AMD). METHODS Following a systematic review of the literature data on detection and quantification of AMD retinal biomarkers by available algorithms were extracted and descriptively synthesized. Algorithm quality was assessed using a modified version of the Quality Assessment of Diagnostic Accuracy Studies 2 checklist with a focus on accuracy against established reference standards and risk of bias. RESULTS Thirty five studies reporting computer-aided diagnosis (CAD) tools for qualitative analysis or algorithms for quantitative analysis were identified. Compared with manual assessment in reference standards correlation coefficients ranged from 0.54 to 0.97 for drusen, 0.80 to 0.98 for geographic atrophy (GA), and 0.30 to 0.98 for intra- or subretinal fluid and pigment epithelial detachment (PED) detection by automated algorithms. CAD tools achieved area under the curve (AUC) values of 0.94 to 0.99, sensitivity of 0.90 to 1.00, and specificity of 0.89 to 0.92. CONCLUSIONS Automated analysis of AMD biomarkers on OCT is promising. However, most of the algorithm validation was performed in preselected patients, exhibiting the targeted biomarker only. In addition, type and quality of reported algorithm validation varied substantially. TRANSLATIONAL RELEVANCE The development of algorithms for combined, simultaneous analysis of multiple AMD biomarkers including AMD staging and the agreement on standardized validation procedures would be of considerable translational value for the clinician and the clinical researcher.
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Affiliation(s)
| | - Thomas Schultz
- Department of Computer Science, University of Bonn, Bonn, Germany
| | - Johannes Birtel
- Department of Ophthalmology, University of Bonn, Bonn, Germany
| | | | - Norbert Pfeiffer
- Department of Ophthalmology, University Medical Center Mainz, Mainz, Germany
| | | | - Frank G Holz
- Department of Ophthalmology, University of Bonn, Bonn, Germany
| | - Robert P Finger
- Department of Ophthalmology, University of Bonn, Bonn, Germany
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Zhu W, Zhang L, Shi F, Xiang D, Wang L, Guo J, Yang X, Chen H, Chen X. Automated framework for intraretinal cystoid macular edema segmentation in three-dimensional optical coherence tomography images with macular hole. JOURNAL OF BIOMEDICAL OPTICS 2017; 22:76014. [PMID: 28732095 DOI: 10.1117/1.jbo.22.7.076014] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 03/11/2017] [Accepted: 07/05/2017] [Indexed: 06/07/2023]
Abstract
Cystoid macular edema (CME) and macular hole (MH) are the leading causes for visual loss in retinal diseases. The volume of the CMEs can be an accurate predictor for visual prognosis. This paper presents an automatic method to segment the CMEs from the abnormal retina with coexistence of MH in three-dimensional-optical coherence tomography images. The proposed framework consists of preprocessing and CMEs segmentation. The preprocessing part includes denoising, intraretinal layers segmentation and flattening, and MH and vessel silhouettes exclusion. In the CMEs segmentation, a three-step strategy is applied. First, an AdaBoost classifier trained with 57 features is employed to generate the initialization results. Second, an automated shape-constrained graph cut algorithm is applied to obtain the refined results. Finally, cyst area information is used to remove false positives (FPs). The method was evaluated on 19 eyes with coexistence of CMEs and MH from 18 subjects. The true positive volume fraction, FP volume fraction, dice similarity coefficient, and accuracy rate for CMEs segmentation were 81.0%±7.8%, 0.80%±0.63%, 80.9%±5.7%, and 99.7%±0.1%, respectively.
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Affiliation(s)
- Weifang Zhu
- Soochow University, School of Electronics and Information Engineering, Suzhou, China
| | - Li Zhang
- Soochow University, School of Electronics and Information Engineering, Suzhou, China
| | - Fei Shi
- Soochow University, School of Electronics and Information Engineering, Suzhou, China
| | - Dehui Xiang
- Soochow University, School of Electronics and Information Engineering, Suzhou, China
| | - Lirong Wang
- Soochow University, School of Electronics and Information Engineering, Suzhou, China
| | - Jingyun Guo
- Soochow University, School of Electronics and Information Engineering, Suzhou, China
| | - Xiaoling Yang
- Soochow University, School of Electronics and Information Engineering, Suzhou, China
| | - Haoyu Chen
- Shantou University and the Chinese University of Hong Kong, Joint Shantou International Eye Center, Shantou, ChinacThe Chinese University of Hong Kong, Department of Ophthalmology and Visual Sciences, Hong Kong, China
| | - Xinjian Chen
- Soochow University, School of Electronics and Information Engineering, Suzhou, China
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Guo J, Zhu W, Shi F, Xiang D, Chen H, Chen X. A Framework for Classification and Segmentation of Branch Retinal Artery Occlusion in SD-OCT. IEEE TRANSACTIONS ON IMAGE PROCESSING : A PUBLICATION OF THE IEEE SIGNAL PROCESSING SOCIETY 2017; 26:3518-3527. [PMID: 28459688 DOI: 10.1109/tip.2017.2697762] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/07/2023]
Abstract
Branch retinal artery occlusion (BRAO) is an ocular emergency, which could lead to blindness. Quantitative analysis of the BRAO region in the retina is necessary for the assessment of the severity of retinal ischemia. In this paper, a fully automatic framework was proposed to segment BRAO regions based on 3D spectral-domain optical coherence tomography (SD-OCT) images. To the best of our knowledge, this is the first automatic 3D BRAO segmentation framework. First, the input 3D image is automatically classified into BRAO of acute phase and BRAO of chronic phase or normal retina using an AdaBoost classifier based on combining local structural, intensity, textural features with our new feature distribution analyzing strategy. Then, BRAO regions of acute phase and chronic phase are segmented separately. A thickness model is built to segment BRAO in the chronic phase. While for segmenting BRAO in the acute phase, a two-step segmentation strategy is performed: rough initialization and refine segmentation. The proposed method was tested on SD-OCT images of 35 patients (12 BRAO acute phase, 11 BRAO chronic phase, and 12 normal eyes) using the leave-one-out strategy. The classification accuracy for BRAO acute phase, BRAO chronic phase, and normal retina were 100%, 90.9%, and 91.7%, respectively. The overall true positive volume fraction (TPVF) and false positive volume fraction (FPVF) for the acute phase were 91.1% and 5.5% and for the chronic phase were 92.7% and 8.4%, respectively.
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Oguz I, Abramoff MD, Zhang L, Lee K, Zhang EZ, Sonka M. 4D Graph-Based Segmentation for Reproducible and Sensitive Choroid Quantification From Longitudinal OCT Scans. Invest Ophthalmol Vis Sci 2017; 57:OCT621-OCT630. [PMID: 27936264 PMCID: PMC5215413 DOI: 10.1167/iovs.15-18924] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/02/2022] Open
Abstract
Purpose Longitudinal imaging is becoming more commonplace for studies of disease progression, response to treatment, and healthy maturation. Accurate and reproducible quantification methods are desirable to fully mine the wealth of data in such datasets. However, most current retinal OCT segmentation methods are cross-sectional and fail to leverage the inherent context present in longitudinal sequences of images. Methods We propose a novel graph-based method for segmentation of multiple three-dimensional (3D) scans over time (termed 3D + time or 4D). The usefulness of this approach in retinal imaging is illustrated in the segmentation of the choroidal surfaces from longitudinal optical coherence tomography (OCT) scans. A total of 3219 synthetic (3070) and patient (149) OCT images were segmented for validation of our approach. Results The results show that the proposed 4D segmentation method is significantly more reproducible (P < 0.001) than the 3D approach and is significantly more sensitive to temporal changes (P < 0.0001) achieved by the substantial increase of measurement robustness. Conclusions This is the first automated 4D method for jointly quantifying choroidal thickness in longitudinal OCT studies. Our method is robust to image noise and produces more reproducible choroidal thickness measurements than a sequence of independent 3D segmentations, without sacrificing sensitivity to temporal changes.
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Affiliation(s)
- Ipek Oguz
- Department of Ophthalmology and Visual Sciences, The University of Iowa, Iowa City, Iowa, United States 2Iowa Institute for Biomedical Imaging, The University of Iowa, Iowa City, Iowa, United States 3Department of Radiology, University of Pennsylvania, Philadelphia, Pennsylvania, United States
| | - Michael D Abramoff
- Department of Ophthalmology and Visual Sciences, The University of Iowa, Iowa City, Iowa, United States 2Iowa Institute for Biomedical Imaging, The University of Iowa, Iowa City, Iowa, United States 4Veterans Affairs Medical Center, Iowa City, Iowa, United States
| | - Li Zhang
- Iowa Institute for Biomedical Imaging, The University of Iowa, Iowa City, Iowa, United States
| | - Kyungmoo Lee
- Iowa Institute for Biomedical Imaging, The University of Iowa, Iowa City, Iowa, United States
| | - Ellen Ziyi Zhang
- Wellman Center for Photomedicine, Massachusetts General Hospital and Harvard Medical School, Boston, Massachusetts, United States
| | - Milan Sonka
- Department of Ophthalmology and Visual Sciences, The University of Iowa, Iowa City, Iowa, United States 2Iowa Institute for Biomedical Imaging, The University of Iowa, Iowa City, Iowa, United States
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Alsaih K, Lemaitre G, Rastgoo M, Massich J, Sidibé D, Meriaudeau F. Machine learning techniques for diabetic macular edema (DME) classification on SD-OCT images. Biomed Eng Online 2017; 16:68. [PMID: 28592309 PMCID: PMC5463338 DOI: 10.1186/s12938-017-0352-9] [Citation(s) in RCA: 54] [Impact Index Per Article: 7.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/31/2016] [Accepted: 05/16/2017] [Indexed: 11/12/2022] Open
Abstract
BACKGROUND Spectral domain optical coherence tomography (OCT) (SD-OCT) is most widely imaging equipment used in ophthalmology to detect diabetic macular edema (DME). Indeed, it offers an accurate visualization of the morphology of the retina as well as the retina layers. METHODS The dataset used in this study has been acquired by the Singapore Eye Research Institute (SERI), using CIRRUS TM (Carl Zeiss Meditec, Inc., Dublin, CA, USA) SD-OCT device. The dataset consists of 32 OCT volumes (16 DME and 16 normal cases). Each volume contains 128 B-scans with resolution of 1024 px × 512 px, resulting in more than 3800 images being processed. All SD-OCT volumes are read and assessed by trained graders and identified as normal or DME cases based on evaluation of retinal thickening, hard exudates, intraretinal cystoid space formation, and subretinal fluid. Within the DME sub-set, a large number of lesions has been selected to create a rather complete and diverse DME dataset. This paper presents an automatic classification framework for SD-OCT volumes in order to identify DME versus normal volumes. In this regard, a generic pipeline including pre-processing, feature detection, feature representation, and classification was investigated. More precisely, extraction of histogram of oriented gradients and local binary pattern (LBP) features within a multiresolution approach is used as well as principal component analysis (PCA) and bag of words (BoW) representations. RESULTS AND CONCLUSION Besides comparing individual and combined features, different representation approaches and different classifiers are evaluated. The best results are obtained for LBP[Formula: see text] vectors while represented and classified using PCA and a linear-support vector machine (SVM), leading to a sensitivity(SE) and specificity (SP) of 87.5 and 87.5%, respectively.
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Affiliation(s)
- Khaled Alsaih
- LE2I, CNRS, Arts et Métiers, Université Bourgogne Franche-Comté, 12 rue de la Fonderie, Le Creusot, France
- Centre for Intelligent Signal and Imaging Research (CISIR), Electrical & Electronic Engineering Department, Universiti Teknologi PETRONAS, 32610 Seri Iskandar, Malaysia
| | - Guillaume Lemaitre
- LE2I, CNRS, Arts et Métiers, Université Bourgogne Franche-Comté, 12 rue de la Fonderie, Le Creusot, France
| | - Mojdeh Rastgoo
- LE2I, CNRS, Arts et Métiers, Université Bourgogne Franche-Comté, 12 rue de la Fonderie, Le Creusot, France
| | - Joan Massich
- LE2I, CNRS, Arts et Métiers, Université Bourgogne Franche-Comté, 12 rue de la Fonderie, Le Creusot, France
| | - Désiré Sidibé
- LE2I, CNRS, Arts et Métiers, Université Bourgogne Franche-Comté, 12 rue de la Fonderie, Le Creusot, France
| | - Fabrice Meriaudeau
- LE2I, CNRS, Arts et Métiers, Université Bourgogne Franche-Comté, 12 rue de la Fonderie, Le Creusot, France
- Centre for Intelligent Signal and Imaging Research (CISIR), Electrical & Electronic Engineering Department, Universiti Teknologi PETRONAS, 32610 Seri Iskandar, Malaysia
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Fatehi N, Nowroozizadeh S, Henry S, Coleman AL, Caprioli J, Nouri-Mahdavi K. Association of Structural and Functional Measures With Contrast Sensitivity in Glaucoma. Am J Ophthalmol 2017; 178:129-139. [PMID: 28342719 DOI: 10.1016/j.ajo.2017.03.019] [Citation(s) in RCA: 30] [Impact Index Per Article: 4.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/27/2016] [Revised: 03/15/2017] [Accepted: 03/15/2017] [Indexed: 11/25/2022]
Abstract
PURPOSE To test the hypothesis that structural and functional measures predict contrast sensitivity (CS) outcomes in glaucomatous eyes. DESIGN Cross-sectional prospective study. METHODS One hundred five eyes of 65 patients who underwent macular spectral-domain optical coherence tomography imaging, 24-2 standard achromatic visual fields (VF), and CS measurement on the same day were enrolled. Association of CS at 4 spatial frequencies (3, 6, 12, and 18 cycles per degree, cpd) with structural and functional outcomes was explored with correlation and regression analyses. RESULTS The median (IQR) 24-2 visual field mean deviation was -7.6 (-11.1 to -3.0). Significant correlations were found between CS at 6 cpd and ganglion cell/inner plexiform layer thickness at inferotemporal and inferonasal macular sectors (ρ = 0.222, P = .023 and ρ = 0.209, P = .032, respectively). CS at 6 cpd demonstrated higher correlations with full macular thickness measurements, the strongest of which was with the central macular thickness in the superior 6 × 3-degree region (ρ = 0.311, P = .001). Contrast sensitivity at 6 cpd also had the strongest correlation with mean deviation of the 4 central VF points (ρ = -0.420; P < .001). There was a significant correlation between logMAR visual acuity and contrast sensitivity at 6, 12, and 18 cpd (ρ = -0.306, ρ = -0.348 and ρ = -0.241, P < .013, respectively). CONCLUSIONS Structural and functional measures showed a fair relationship with contrast sensitivity. This association was most prominent between full-thickness macular measures or central VF parameters and CS at 6 cpd. Contrast sensitivity was not a reliable surrogate for glaucoma severity in this cross-sectional study.
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