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Wang Y, Chen S, Chen X, Xu Z, Lin K, Shi L, Mu Q, Liu L. Coaxial Bright and Dark Field Optical Coherence Tomography. IEEE Trans Biomed Eng 2024; 71:1879-1888. [PMID: 38231824 DOI: 10.1109/tbme.2024.3355174] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/19/2024]
Abstract
To improve the signal collection efficiency of Optical Coherence Tomography (OCT) for biomedical applications. A novel coaxial optical design was implemented, utilizing a wavefront-division beam splitter in the sample arm with a 45-degree rod mirror. This design allowed for the simultaneous collection of bright and dark field signals. The bright field signal was detected within its circular aperture in a manner similar to standard OCT, while the dark field signal passed through an annular-shaped aperture and was collected by the same spectrometer via a fiber array. This new configuration improved the signal collection efficiency by ∼3 dB for typical biological tissues. Dark-field OCT images were found to provide higher resolution, contrast and distinct information compared to standard bright-field OCT. By compounding bright and dark field images, speckle noise was suppressed by ∼ √2 . These advantages were validated using Teflon phantoms, chicken breast ex vivo, and human skin in vivo. This new OCT configuration significantly enhances signal collection efficiency and image quality, offering great potential for improving OCT technology with better depth, contrast, resolution, speckles, and signal-to-noise ratio. We believe that the bright and dark field signals will enable more comprehensive tissue characterization with the angled scattered light. This advancement will greatly promote the OCT technology in various clinical and biomedical research applications.
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Gende M, Castelo L, de Moura J, Novo J, Ortega M. Intra- and Inter-expert Validation of an Automatic Segmentation Method for Fluid Regions Associated with Central Serous Chorioretinopathy in OCT Images. JOURNAL OF IMAGING INFORMATICS IN MEDICINE 2024; 37:107-122. [PMID: 38343245 DOI: 10.1007/s10278-023-00926-6] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 06/15/2023] [Revised: 10/16/2023] [Accepted: 10/16/2023] [Indexed: 03/02/2024]
Abstract
Central Serous Chorioretinopathy (CSC) is a retinal disorder caused by the accumulation of fluid, resulting in vision distortion. The diagnosis of this disease is typically performed through Optical Coherence Tomography (OCT) imaging, which displays any fluid buildup between the retinal layers. Currently, these fluid regions are manually detected by visual inspection a time-consuming and subjective process that can be prone to errors. A series of six deep learning-based automatic segmentation architectural configurations of different levels of complexity were trained and compared in order to determine the best model intended for the automatic segmentation of CSC-related lesions in OCT images. The best performing models were then evaluated in an external validation study. Furthermore, an intra- and inter-expert analysis was conducted in order to compare the manual segmentation performed by expert ophthalmologists with the automatic segmentation provided by the models. Test results of the best performing configuration achieved a mean Dice of 0.868 ± 0.056 in the internal dataset. In the external validation set, these models achieved a level of agreement with human experts of up to 0.960 in terms of Kappa coefficient, contrasting with a value of 0.951 for agreement between human experts. Overall, the models reached a better agreement with either of the human experts than these experts with each other, suggesting that automatic segmentation models for the detection of CSC-related lesions in OCT imaging can be useful tools for assessing this disease, reducing the workload of manual inspection and leading to a more robust and objective diagnosis method.
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Affiliation(s)
- Mateo Gende
- Grupo, VARPA, Instituto de Investigación Biomédica de A Coruña (INIBIC), Universidade da Coruña, Xubias de Arriba, 84, 15006, A Coruña, Spain
- Centro de investigación, CITIC, Universidade da Coruña, Campus de Elviña s/n, 15071, A Coruña, Spain
| | - Lúa Castelo
- Grupo, VARPA, Instituto de Investigación Biomédica de A Coruña (INIBIC), Universidade da Coruña, Xubias de Arriba, 84, 15006, A Coruña, Spain
- Centro de investigación, CITIC, Universidade da Coruña, Campus de Elviña s/n, 15071, A Coruña, Spain
| | - Joaquim de Moura
- Grupo, VARPA, Instituto de Investigación Biomédica de A Coruña (INIBIC), Universidade da Coruña, Xubias de Arriba, 84, 15006, A Coruña, Spain.
- Centro de investigación, CITIC, Universidade da Coruña, Campus de Elviña s/n, 15071, A Coruña, Spain.
| | - Jorge Novo
- Grupo, VARPA, Instituto de Investigación Biomédica de A Coruña (INIBIC), Universidade da Coruña, Xubias de Arriba, 84, 15006, A Coruña, Spain
- Centro de investigación, CITIC, Universidade da Coruña, Campus de Elviña s/n, 15071, A Coruña, Spain
| | - Marcos Ortega
- Grupo, VARPA, Instituto de Investigación Biomédica de A Coruña (INIBIC), Universidade da Coruña, Xubias de Arriba, 84, 15006, A Coruña, Spain
- Centro de investigación, CITIC, Universidade da Coruña, Campus de Elviña s/n, 15071, A Coruña, Spain
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3
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Yu X, Li M, Ge C, Yuan M, Liu L, Mo J, Shum PP, Chen J. Loss-balanced parallel decoding network for retinal fluid segmentation in OCT. Comput Biol Med 2023; 165:107319. [PMID: 37611427 DOI: 10.1016/j.compbiomed.2023.107319] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/08/2023] [Revised: 07/12/2023] [Accepted: 08/07/2023] [Indexed: 08/25/2023]
Abstract
As a leading cause of blindness worldwide, macular edema (ME) is mainly determined by sub-retinal fluid (SRF), intraretinal fluid (IRF), and pigment epithelial detachment (PED) accumulation, and therefore, the characterization of SRF, IRF, and PED, which is also known as ME segmentation, has become a crucial issue in ophthalmology. Due to the subjective and time-consuming nature of ME segmentation in retinal optical coherence tomography (OCT) images, automatic computer-aided systems are highly desired in clinical practice. This paper proposes a novel loss-balanced parallel decoding network, namely PadNet, for ME segmentation. Specifically, PadNet mainly consists of an encoder and three parallel decoder modules, which serve as segmentation, contour, and diffusion branches, and they are employed to extract the ME's characteristics, the contour area features, and to expand the ME area from the center to edge, respectively. A new loss-balanced joint-loss function with three components corresponding to each of the three parallel decoding branches is also devised for training. Experiments are conducted with three public datasets to verify the effectiveness of PadNet, and the performances of PadNet are compared with those of five state-of-the-art methods. Results show that PadNet improves ME segmentation accuracy by 8.1%, 11.1%, 0.6%, 1.4% and 8.3%, as compared with UNet, sASPP, MsTGANet, YNet, RetiFluidNet, respectively, which convincingly demonstrates that the proposed PadNet is robust and effective in ME segmentation in different cases.
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Affiliation(s)
- Xiaojun Yu
- School of Automation, Northwestern Polytechnical University, Xi'an, 710072, Shaanxi, China; Shenzhen Research Institute of Northwestern Polytechnical University, Shenzhen, 518057, Guangdong, China.
| | - Mingshuai Li
- School of Automation, Northwestern Polytechnical University, Xi'an, 710072, Shaanxi, China.
| | - Chenkun Ge
- School of Automation, Northwestern Polytechnical University, Xi'an, 710072, Shaanxi, China.
| | - Miao Yuan
- School of Automation, Northwestern Polytechnical University, Xi'an, 710072, Shaanxi, China.
| | - Linbo Liu
- School of Electrical and Electronic Engineering, Nanyang Technological University, 639798, Singapore.
| | - Jianhua Mo
- School of Electronics and Information Engineering, Soochow University, Suzhou 215006, China.
| | - Perry Ping Shum
- Department of Electrical and Electronic Engineering, Southern University of Science and Technology, Shenzhen, 518055, China.
| | - Jinna Chen
- Department of Electrical and Electronic Engineering, Southern University of Science and Technology, Shenzhen, 518055, China.
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4
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Oganov AC, Seddon I, Jabbehdari S, Uner OE, Fonoudi H, Yazdanpanah G, Outani O, Arevalo JF. Artificial intelligence in retinal image analysis: Development, advances, and challenges. Surv Ophthalmol 2023; 68:905-919. [PMID: 37116544 DOI: 10.1016/j.survophthal.2023.04.001] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/12/2022] [Revised: 04/20/2023] [Accepted: 04/24/2023] [Indexed: 04/30/2023]
Abstract
Modern advances in diagnostic technologies offer the potential for unprecedented insight into ophthalmic conditions relating to the retina. We discuss the current landscape of artificial intelligence in retina with respect to screening, diagnosis, and monitoring of retinal pathologies such as diabetic retinopathy, diabetic macular edema, central serous chorioretinopathy, and age-related macular degeneration. We review the methods used in these models and evaluate their performance in both research and clinical contexts and discuss potential future directions for investigation, use of multiple imaging modalities in artificial intelligence algorithms, and challenges in the application of artificial intelligence in retinal pathologies.
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Affiliation(s)
- Anthony C Oganov
- Department of Ophthalmology, Renaissance School of Medicine, Stony Brook, NY, USA
| | - Ian Seddon
- College of Osteopathic Medicine, Nova Southeastern University, Fort Lauderdale, FL, USA
| | - Sayena Jabbehdari
- Jones Eye Institute, University of Arkansas for Medical Sciences, Little Rock, AR, USA.
| | - Ogul E Uner
- Casey Eye Institute, Department of Ophthalmology, Oregon Health and Science University, Portland, OR, USA
| | - Hossein Fonoudi
- Eye Research Center, Farabi Eye Hospital, Tehran University of Medical Sciences, Iranshahr University of Medical Sciences, Iranshahr, Sistan and Baluchestan, Iran
| | - Ghasem Yazdanpanah
- Department of Ophthalmology and Visual Sciences, Illinois Eye and Ear Infirmary, University of Illinois at Chicago, Chicago, IL, USA
| | - Oumaima Outani
- Faculty of Medicine and Pharmacy of Rabat, Mohammed 5 University, Rabat, Rabat, Morocco
| | - J Fernando Arevalo
- Wilmer Eye Institute, Johns Hopkins University School of Medicine, Baltimore, MD, USA
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5
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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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Yu X, Ge C, Li M, Aziz MZ, Mo J, Fan Z. Multiscale denoising generative adversarial network for speckle reduction in optical coherence tomography images. J Med Imaging (Bellingham) 2023; 10:024006. [PMID: 37009058 PMCID: PMC10061342 DOI: 10.1117/1.jmi.10.2.024006] [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: 08/04/2022] [Accepted: 03/13/2023] [Indexed: 04/03/2023] Open
Abstract
Purpose Optical coherence tomography (OCT) is a noninvasive, high-resolution imaging modality capable of providing both cross-sectional and three-dimensional images of tissue microstructures. Owing to its low-coherence interferometry nature, however, OCT inevitably suffers from speckles, which diminish image quality and mitigate the precise disease diagnoses, and therefore, despeckling mechanisms are highly desired to alleviate the influences of speckles on OCT images. Approach We propose a multiscale denoising generative adversarial network (MDGAN) for speckle reductions in OCT images. A cascade multiscale module is adopted as MDGAN basic block first to raise the network learning capability and take advantage of the multiscale context, and then a spatial attention mechanism is proposed to refine the denoised images. For enormous feature learning in OCT images, a deep back-projection layer is finally introduced to alternatively upscale and downscale the features map of MDGAN. Results Experiments with two different OCT image datasets are conducted to verify the effectiveness of the proposed MDGAN scheme. Results compared those of the state-of-the-art existing methods show that MDGAN is able to improve both peak-single-to-noise ratio and signal-to-noise ratio by 3 dB at most, with its structural similarity index measurement and contrast-to-noise ratio being 1.4% and 1.3% lower than those of the best existing methods. Conclusions Results demonstrate that MDGAN is effective and robust for OCT image speckle reductions and outperforms the best state-of-the-art denoising methods in different cases. It could help alleviate the influence of speckles in OCT images and improve OCT imaging-based diagnosis.
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Affiliation(s)
- Xiaojun Yu
- Northwestern Polytechnical University, School of Automation, Xi’an, China
| | - Chenkun Ge
- Northwestern Polytechnical University, School of Automation, Xi’an, China
| | - Mingshuai Li
- Northwestern Polytechnical University, School of Automation, Xi’an, China
| | | | - Jianhua Mo
- Soochow University, School of Electronics and Information Engineering, Suzhou, China
| | - Zeming Fan
- Northwestern Polytechnical University, School of Automation, Xi’an, China
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Vidal P, de Moura J, Novo J, Ortega M. Multivendor fully automatic uncertainty management approaches for the intuitive representation of DME fluid accumulations in OCT images. Med Biol Eng Comput 2023; 61:1209-1224. [PMID: 36690902 PMCID: PMC10083163 DOI: 10.1007/s11517-022-02765-z] [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: 03/30/2022] [Accepted: 12/27/2022] [Indexed: 01/25/2023]
Abstract
Diabetes represents one of the main causes of blindness in developed countries, caused by fluid accumulations in the retinal layers. The clinical literature defines the different types of diabetic macular edema (DME) as cystoid macular edema (CME), diffuse retinal thickening (DRT), and serous retinal detachment (SRD), each with its own clinical relevance. These fluid accumulations do not present defined borders that facilitate segmentational approaches (specially the DRT type, usually not taken into account by the state of the art for this reason) so a diffuse paradigm is used for its detection and visualization. In this paper, we propose three novel approaches for the representation and characterization of these types of DME. A baseline proposal, using a convolutional neural network as backbone, another based on transfer learning from a general domain, and a third approach exploiting information of regions without a defined label. Overall, our baseline proposal obtained an AUC of 0.9583 ± 0.0093, the approach pretrained with a general-domain dataset an AUC of 0.9603 ± 0.0087, and the approach pretrained in the domain taking advantage of uncertainty, an AUC of 0.9619 ± 0.0073.
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Affiliation(s)
- Plácido Vidal
- Centro de investigación CITIC, Universidade da Coruña, Campus de Elviña, s/n, A Coruña, 15071, Galicia, Spain.,Grupo VARPA, Instituto de Investigación Biomédica de A Coruña (INIBIC), Universidade da Coruña, Xubias de Arriba, 84, A Coruña, 15006, Galicia, Spain
| | - Joaquim de Moura
- Centro de investigación CITIC, Universidade da Coruña, Campus de Elviña, s/n, A Coruña, 15071, Galicia, Spain. .,Grupo VARPA, Instituto de Investigación Biomédica de A Coruña (INIBIC), Universidade da Coruña, Xubias de Arriba, 84, A Coruña, 15006, Galicia, Spain.
| | - Jorge Novo
- Centro de investigación CITIC, Universidade da Coruña, Campus de Elviña, s/n, A Coruña, 15071, Galicia, Spain.,Grupo VARPA, Instituto de Investigación Biomédica de A Coruña (INIBIC), Universidade da Coruña, Xubias de Arriba, 84, A Coruña, 15006, Galicia, Spain
| | - Marcos Ortega
- Centro de investigación CITIC, Universidade da Coruña, Campus de Elviña, s/n, A Coruña, 15071, Galicia, Spain.,Grupo VARPA, Instituto de Investigación Biomédica de A Coruña (INIBIC), Universidade da Coruña, Xubias de Arriba, 84, A Coruña, 15006, Galicia, Spain
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8
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A deep network embedded with rough fuzzy discretization for OCT fundus image segmentation. Sci Rep 2023; 13:328. [PMID: 36609585 PMCID: PMC9822971 DOI: 10.1038/s41598-023-27479-6] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/10/2022] [Accepted: 01/03/2023] [Indexed: 01/08/2023] Open
Abstract
The noise and redundant information are the main reasons for the performance bottleneck of medical image segmentation algorithms based on the deep learning. To this end, we propose a deep network embedded with rough fuzzy discretization (RFDDN) for OCT fundus image segmentation. Firstly, we establish the information decision table of OCT fundus image segmentation, and regard each category of segmentation region as a fuzzy set. Then, we use the fuzzy c-means clustering to get the membership degrees of pixels to each segmentation region. According to membership functions and the equivalence relation generated by the brightness attribute, we design the individual fitness function based on the rough fuzzy set, and use a genetic algorithm to search for the best breakpoints to discretize the features of OCT fundus images. Finally, we take the feature discretization based on the rough fuzzy set as the pre-module of the deep neural network, and introduce the deep supervised attention mechanism to obtain the important multi-scale information. We compare RFDDN with U-Net, ReLayNet, CE-Net, MultiResUNet, and ISCLNet on the two groups of 3D retinal OCT data. RFDDN is superior to the other five methods on all evaluation indicators. The results obtained by ISCLNet are the second only inferior to those obtained by RFDDN. DSC, sensitivity, and specificity of RFDDN are evenly 3.3%, 2.6%, and 7.1% higher than those of ISCLNet, respectively. HD95 and ASD of RFDDN are evenly 6.6% and 19.7% lower than those of ISCLNet, respectively. The experimental results show that our method can effectively eliminate the noise and redundant information in Oct fundus images, and greatly improve the accuracy of OCT fundus image segmentation while taking into account the interpretability and computational efficiency.
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9
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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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Pavithra K, Kumar P, Geetha M, Bhandary SV. Computer aided diagnosis of diabetic macular edema in retinal fundus and OCT images: A review. Biocybern Biomed Eng 2023. [DOI: 10.1016/j.bbe.2022.12.005] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/04/2023]
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11
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Medhi JP, S.R. N, Choudhury S, Dandapat S. Improved detection and analysis of Macular Edema using modified guided image filtering with modified level set spatial fuzzy clustering on Optical Coherence Tomography images. Biomed Signal Process Control 2023. [DOI: 10.1016/j.bspc.2022.104149] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/14/2022]
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12
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Tang W, Ye Y, Chen X, Shi F, Xiang D, Chen Z, Zhu W. Multi-class retinal fluid joint segmentation based on cascaded convolutional neural networks. Phys Med Biol 2022; 67. [DOI: 10.1088/1361-6560/ac7378] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/12/2022] [Accepted: 05/25/2022] [Indexed: 11/12/2022]
Abstract
Abstract
Objective. Retinal fluid mainly includes intra-retinal fluid (IRF), sub-retinal fluid (SRF) and pigment epithelial detachment (PED), whose accurate segmentation in optical coherence tomography (OCT) image is of great importance to the diagnosis and treatment of the relative fundus diseases. Approach. In this paper, a novel two-stage multi-class retinal fluid joint segmentation framework based on cascaded convolutional neural networks is proposed. In the pre-segmentation stage, a U-shape encoder–decoder network is adopted to acquire the retinal mask and generate a retinal relative distance map, which can provide the spatial prior information for the next fluid segmentation. In the fluid segmentation stage, an improved context attention and fusion network based on context shrinkage encode module and multi-scale and multi-category semantic supervision module (named as ICAF-Net) is proposed to jointly segment IRF, SRF and PED. Main results. the proposed segmentation framework was evaluated on the dataset of RETOUCH challenge. The average Dice similarity coefficient, intersection over union and accuracy (Acc) reach 76.39%, 64.03% and 99.32% respectively. Significance. The proposed framework can achieve good performance in the joint segmentation of multi-class fluid in retinal OCT images and outperforms some state-of-the-art segmentation networks.
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Song W, Kaakour AH, Kalur A, Muste JC, Iyer AI, Valentim CCS, Singh RP. Performance of a Machine-Learning Computational Image Analysis Algorithm in Retinal Fluid Quantification for Patients With Diabetic Macular Edema and Retinal Vein Occlusions. Ophthalmic Surg Lasers Imaging Retina 2022; 53:123-131. [PMID: 35272558 DOI: 10.3928/23258160-20220215-02] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/20/2022]
Abstract
BACKGROUND AND OBJECTIVE The objective is to validate an automated artificial intelligence model in detecting and quantifying fluid in diabetic macular edema (DME) and retinal vein occlusion (RVO) optical coherence tomography images. PATIENTS AND METHODS DME (n = 100) and RVO (n = 100) images of adult patients were reviewed. The performance of machine-learning (ML) computational image analysis algorithm was evaluated against consensus manual grading. Main outcomes were accuracy and sensitivity for detection and Pearson's correlation coefficients for quantification. RESULTS The ML algorithm had a high accuracy and sensitivity in both DME (intraretinal fluid [IRF]: 0.92, 0.97; subretinal fluid [SRF]: 0.93, 1.00) and RVO (IRF: 0.94, 0.99; SRF: 0.93, 1.00). It had moderate-high correlation in quantifying fluid in DME (total retinal fluid: 0.88; IRF: 0.88; SRF: 0.97) and RVO (total retinal fluid: 0.83; IRF: 0.76; SRF: 0.64). CONCLUSION The ML algorithm is highly accurate and sensitive in detecting fluid in DME and RVO optical coherence tomography images and effectively quantifies IRF and SRF in both disease states, particularly in images with low to moderate fluid burden. [Ophthalmic Surg Lasers Imaging Retina. 2022;53:123-131.].
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He X, Fang L, Tan M, Chen X. Intra- and Inter-Slice Contrastive Learning for Point Supervised OCT Fluid Segmentation. IEEE TRANSACTIONS ON IMAGE PROCESSING : A PUBLICATION OF THE IEEE SIGNAL PROCESSING SOCIETY 2022; 31:1870-1881. [PMID: 35139015 DOI: 10.1109/tip.2022.3148814] [Citation(s) in RCA: 13] [Impact Index Per Article: 6.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/14/2023]
Abstract
OCT fluid segmentation is a crucial task for diagnosis and therapy in ophthalmology. The current convolutional neural networks (CNNs) supervised by pixel-wise annotated masks achieve great success in OCT fluid segmentation. However, requiring pixel-wise masks from OCT images is time-consuming, expensive and expertise needed. This paper proposes an Intra- and inter-Slice Contrastive Learning Network (ISCLNet) for OCT fluid segmentation with only point supervision. Our ISCLNet learns visual representation by designing contrastive tasks that exploit the inherent similarity or dissimilarity from unlabeled OCT data. Specifically, we propose an intra-slice contrastive learning strategy to leverage the fluid-background similarity and the retinal layer-background dissimilarity. Moreover, we construct an inter-slice contrastive learning architecture to learn the similarity of adjacent OCT slices from one OCT volume. Finally, an end-to-end model combining intra- and inter-slice contrastive learning processes learns to segment fluid under the point supervision. The experimental results on two public OCT fluid segmentation datasets (i.e., AI Challenger and RETOUCH) demonstrate that the ISCLNet bridges the gap between fully-supervised and weakly-supervised OCT fluid segmentation and outperforms other well-known point-supervised segmentation methods.
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15
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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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Macular Hole Detection Using a New Hybrid Method: Using Multilevel Thresholding and Derivation on Optical Coherence Tomographic Images. COMPUTATIONAL INTELLIGENCE AND NEUROSCIENCE 2022; 2021:6904217. [PMID: 34976042 PMCID: PMC8716210 DOI: 10.1155/2021/6904217] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 07/15/2021] [Revised: 10/08/2021] [Accepted: 11/24/2021] [Indexed: 11/18/2022]
Abstract
Optical coherence tomography (OCT) is a noninvasive imaging test. OCT imaging is analogous to ultrasound imaging, except that it uses light instead of sound. In this type of image, microscopic quality intratissue images are provided. In addition, fast and direct imaging of tissue morphology and reproducibility of results are the advantages of this imaging. Macular holes are a common eye disease that leads to visual impairment. The macular perforation is a rupture in the central part of the retina that, if left untreated, can lead to vision loss. A novel method for detecting macular holes using OCT images based on multilevel thresholding and derivation is proposed in this paper. This is a multistep method, which consists of segmentation, feature extraction, and feature selection. A combination of thresholding and derivation is used to diagnose the macular hole. After feature extraction, the features with useful information are selected and finally the output image of the macular hole is obtained. An open-access data set of 200 images with the size of 224 × 224 pixels from Sankara Nethralaya (SN) Eye Hospital, Chennai, India, is used in the experiments. Experimental results show better-diagnosing results than some recent diagnosing methods.
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Correlation of Volume of Macular Edema with Retinal Tomography Features in Diabetic Retinopathy Eyes. J Pers Med 2021; 11:jpm11121337. [PMID: 34945810 PMCID: PMC8708057 DOI: 10.3390/jpm11121337] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/18/2021] [Revised: 11/25/2021] [Accepted: 12/01/2021] [Indexed: 11/17/2022] Open
Abstract
Optical coherence tomography (OCT) enables the detection of macular edema, a significant pathological outcome of diabetic retinopathy (DR). The aim of the study was to correlate edema volume with the severity of diabetic retinopathy and response to treatment with intravitreal injections (compared to baseline). Diabetic retinopathy (DR; n = 181) eyes were imaged with OCT (Heidelberg Engineering, Germany). They were grouped as responders (a decrease in thickness after intravitreal injection of Bevacizumab), non-responders (persistent edema or reduced decrease in thickness), recurrent (recurrence of edema after injection), and treatment naïve (no change in edema at follow-up without any injection). The post-treatment imaging of eyes was included for all groups, except for the treatment naïve group. All eyes underwent a 9 × 6 mm raster scan to measure the edema volume (EV). Central foveal thickness (CFT), central foveal volume (CFV), and total retinal volume (TRV) were obtained from the early treatment diabetic retinopathy study (ETDRS) map. The median EV increased with DR severity, with PDR having the greatest EV (4.01 mm3). This correlated positively with TRV (p < 0.001). Median CFV and CFT were the greatest in severe NPDR. Median EV was the greatest in the recurrent eyes (4.675 mm3) and lowest (1.6 mm3) in the treatment naïve group. Responders and non-responders groups had median values of 3.65 and 3.93 mm3, respectively. This trend was not observed with CFV, CFT, and TRV. A linear regression yielded threshold values of CFV (~0.3 mm3), CFT (~386 µm), and TRV (~9.06 mm3), above which EV may be detected by the current scanner. In this study, EV provided a better distinction between the response groups when compared to retinal tomography parameters. The EV increased with disease severity. Thus, EV can be a more precise parameter to identify subclinical edema and aid in better treatment planning.
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Feature definition and comprehensive analysis on the robust identification of intraretinal cystoid regions using optical coherence tomography images. Pattern Anal Appl 2021. [DOI: 10.1007/s10044-021-01028-1] [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
AbstractCurrently, optical coherence tomography is one of the most used medical imaging modalities, offering cross-sectional representations of the studied tissues. This image modality is specially relevant for the analysis of the retina, since it is the internal part of the human body that allows an almost direct examination without invasive techniques. One of the most representative cases of use of this medical imaging modality is for the identification and characterization of intraretinal fluid accumulations, critical for the diagnosis of one of the main causes of blindness in developed countries: the Diabetic Macular Edema. The study of these fluid accumulations is particularly interesting, both from the point of view of pattern recognition and from the different branches of health sciences. As these fluid accumulations are intermingled with retinal tissues, they present numerous variants according to their severity, and change their appearance depending on the configuration of the device; they are a perfect subject for an in-depth research, as they are considered to be a problem without a strict solution. In this work, we propose a comprehensive and detailed analysis of the patterns that characterize them. We employed a pool of 11 different texture and intensity feature families (giving a total of 510 markers) which we have analyzed using three different feature selection strategies and seven complementary classification algorithms. By doing so, we have been able to narrow down and explain the factors affecting this kind of accumulations and tissue lesions by means of machine learning techniques with a pipeline specially designed for this purpose.
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Liu X, Wang S, Zhang Y, Liu D, Hu W. Automatic fluid segmentation in retinal optical coherence tomography images using attention based deep learning. Neurocomputing 2021. [DOI: 10.1016/j.neucom.2020.07.143] [Citation(s) in RCA: 12] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/21/2022]
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Keenan TDL, Chakravarthy U, Loewenstein A, Chew EY, Schmidt-Erfurth U. Automated Quantitative Assessment of Retinal Fluid Volumes as Important Biomarkers in Neovascular Age-Related Macular Degeneration. Am J Ophthalmol 2021; 224:267-281. [PMID: 33359681 PMCID: PMC8058226 DOI: 10.1016/j.ajo.2020.12.012] [Citation(s) in RCA: 31] [Impact Index Per Article: 10.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/06/2020] [Revised: 12/09/2020] [Accepted: 12/10/2020] [Indexed: 02/07/2023]
Abstract
PURPOSE To evaluate retinal fluid volume data extracted from optical coherence tomography (OCT) scans by artificial intelligence algorithms in the treatment of neovascular age-related macular degeneration (NV-AMD). DESIGN Perspective. METHODS A review was performed of retinal image repository datasets from diverse clinical settings. SETTINGS Clinical trial (HARBOR) and trial follow-on (Age-Related Eye Disease Study 2 10-year Follow-On); real-world (Belfast and Tel-Aviv tertiary centers). PATIENTS 24,362 scans of 1,095 eyes (HARBOR); 4,673 of 880 (Belfast); 1,470 of 132 (Tel-Aviv); 511 of 511 (Age-Related Eye Disease Study 2 10-year Follow-On). ObservationProcedures: Vienna Fluid Monitor or Notal OCT Analyzer applied to macular cube scans. OutcomeMeasures: Intraretinal fluid (IRF), subretinal fluid (SRF), and pigment epithelial detachment (PED) volumes. RESULTS The fluid volumes measured in neovascular AMD were expressed efficiently in nanoliters. Large ranges that differed by population were observed at the treatment-naïve stage: 0-3,435 nL (IRF), 0-5,018 nL (SRF), and 0-10,022 nL (PED). Mean volumes decreased rapidly and consistently with anti-vascular endothelial growth factor therapy. During maintenance therapy, mean IRF volumes were highest in Tel-Aviv (100 nL), lower in Belfast and HARBOR-Pro Re Nata, and lowest in HARBOR-monthly (21 nL). Mean SRF volumes were low in all: 30 nL (HARBOR-monthly) and 48-49 nL (others). CONCLUSIONS Quantitative measures of IRF, SRF, and PED are important biomarkers in NV-AMD. Accurate volumes can be extracted efficiently from OCT scans by artificial intelligence algorithms to guide the treatment of exudative macular diseases. Automated fluid monitoring identifies fluid characteristics in different NV-AMD populations at baseline and during follow-up. For consistency between studies, we propose the nanoliter as a convenient unit. We explore the advantages of using these quantitative metrics in clinical practice and research.
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Affiliation(s)
- Tiarnan D L Keenan
- Division of Epidemiology and Clinical Applications, National Eye Institute, National Institutes of Health, Bethesda, Maryland, USA.
| | - Usha Chakravarthy
- Centre for Experimental Medicine, Dentistry and Biomedical Sciences, Queen's University of Belfast, Belfast, United Kingdom
| | - Anat Loewenstein
- Tel Aviv Medical Center, Sackler Faculty of Medicine, Tel Aviv University, Tel Aviv, Israel
| | - Emily Y Chew
- Division of Epidemiology and Clinical Applications, National Eye Institute, National Institutes of Health, Bethesda, Maryland, USA
| | - Ursula Schmidt-Erfurth
- Department of Ophthalmology and Optometry, Christian Doppler Laboratory for Ophthalmic Image Analyses (OPTIMA), Medical University of Vienna, Vienna, Austria
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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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A Joint Model for Macular Edema Analysis in Optical Coherence Tomography Images Based on Image Enhancement and Segmentation. BIOMED RESEARCH INTERNATIONAL 2021; 2021:6679556. [PMID: 33681374 PMCID: PMC7904365 DOI: 10.1155/2021/6679556] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 10/14/2020] [Revised: 01/23/2021] [Accepted: 01/30/2021] [Indexed: 11/19/2022]
Abstract
Optical coherence tomography (OCT) provides the visualization of macular edema which can assist ophthalmologists in the diagnosis of ocular diseases. Macular edema is a major cause of vision loss in patients with retinal vein occlusion (RVO). However, manual delineation of macular edema is a laborious and time-consuming task. This study proposes a joint model for automatic delineation of macular edema in OCT images. This model consists of two steps: image enhancement using a bioinspired algorithm and macular edema segmentation using a Gaussian-filtering regularized level set (SBGFRLS) algorithm. We then evaluated the delineation efficiency using the following parameters: accuracy, precision, sensitivity, specificity, Dice's similarity coefficient, IOU, and kappa coefficient. Compared with the traditional level set algorithms, including C-V and GAC, the proposed model had higher efficiency in macular edema delineation as shown by reduced processing time and iteration times. Moreover, the accuracy, precision, sensitivity, specificity, Dice's similarity coefficient, IOU, and kappa coefficient for macular edema delineation could reach 99.7%, 97.8%, 96.0%, 99.0%, 96.9%, 94.0%, and 96.8%, respectively. More importantly, the proposed model had comparable precision but shorter processing time compared with manual delineation. Collectively, this study provides a novel model for the delineation of macular edema in OCT images, which can assist the ophthalmologists for the screening and diagnosis of retinal diseases.
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Hassan T, Akram MU, Werghi N, Nazir MN. RAG-FW: A Hybrid Convolutional Framework for the Automated Extraction of Retinal Lesions and Lesion-Influenced Grading of Human Retinal Pathology. IEEE J Biomed Health Inform 2021; 25:108-120. [PMID: 32224467 DOI: 10.1109/jbhi.2020.2982914] [Citation(s) in RCA: 29] [Impact Index Per Article: 9.7] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/09/2022]
Abstract
The identification of retinal lesions plays a vital role in accurately classifying and grading retinopathy. Many researchers have presented studies on optical coherence tomography (OCT) based retinal image analysis over the past. However, to the best of our knowledge, there is no framework yet available that can extract retinal lesions from multi-vendor OCT scans and utilize them for the intuitive severity grading of the human retina. To cater this lack, we propose a deep retinal analysis and grading framework (RAG-FW). RAG-FW is a hybrid convolutional framework that extracts multiple retinal lesions from OCT scans and utilizes them for lesion-influenced grading of retinopathy as per the clinical standards. RAG-FW has been rigorously tested on 43,613 scans from five highly complex publicly available datasets, containing multi-vendor scans, where it achieved the mean intersection-over-union score of 0.8055 for extracting the retinal lesions and the accuracy of 98.70% for the correct severity grading of retinopathy.
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Keenan TDL, Clemons TE, Domalpally A, Elman MJ, Havilio M, Agrón E, Benyamini G, Chew EY. Retinal Specialist versus Artificial Intelligence Detection of Retinal Fluid from OCT: Age-Related Eye Disease Study 2: 10-Year Follow-On Study. Ophthalmology 2021; 128:100-109. [PMID: 32598950 PMCID: PMC8371700 DOI: 10.1016/j.ophtha.2020.06.038] [Citation(s) in RCA: 44] [Impact Index Per Article: 14.7] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/20/2020] [Revised: 06/16/2020] [Accepted: 06/18/2020] [Indexed: 01/14/2023] Open
Abstract
PURPOSE To evaluate the performance of retinal specialists in detecting retinal fluid presence in spectral domain OCT (SD-OCT) scans from eyes with age-related macular degeneration (AMD) and compare performance with an artificial intelligence algorithm. DESIGN Prospective comparison of retinal fluid grades from human retinal specialists and the Notal OCT Analyzer (NOA) on SD-OCT scans from 2 common devices. PARTICIPANTS A total of 1127 eyes of 651 Age-Related Eye Disease Study 2 10-year Follow-On Study (AREDS2-10Y) participants with SD-OCT scans graded by reading center graders (as the ground truth). METHODS The AREDS2-10Y investigators graded each SD-OCT scan for the presence/absence of intraretinal and subretinal fluid. Separately, the same scans were graded by the NOA. MAIN OUTCOME MEASURES Accuracy (primary), sensitivity, specificity, precision, and F1-score. RESULTS Of the 1127 eyes, retinal fluid was present in 32.8%. For detecting retinal fluid, the investigators had an accuracy of 0.805 (95% confidence interval [CI], 0.780-0.828), a sensitivity of 0.468 (95% CI, 0.416-0.520), a specificity of 0.970 (95% CI, 0.955-0.981). The NOA metrics were 0.851 (95% CI, 0.829-0.871), 0.822 (95% CI, 0.779-0.859), 0.865 (95% CI, 0.839-0.889), respectively. For detecting intraretinal fluid, the investigator metrics were 0.815 (95% CI, 0.792-0.837), 0.403 (95% CI, 0.349-0.459), and 0.978 (95% CI, 0.966-0.987); the NOA metrics were 0.877 (95% CI, 0.857-0.896), 0.763 (95% CI, 0.713-0.808), and 0.922 (95% CI, 0.902-0.940), respectively. For detecting subretinal fluid, the investigator metrics were 0.946 (95% CI, 0.931-0.958), 0.583 (95% CI, 0.471-0.690), and 0.973 (95% CI, 0.962-0.982); the NOA metrics were 0.863 (95% CI, 0.842-0.882), 0.940 (95% CI, 0.867-0.980), and 0.857 (95% CI, 0.835-0.877), respectively. CONCLUSIONS In this large and challenging sample of SD-OCT scans obtained with 2 common devices, retinal specialists had imperfect accuracy and low sensitivity in detecting retinal fluid. This was particularly true for intraretinal fluid and difficult cases (with lower fluid volumes appearing on fewer B-scans). Artificial intelligence-based detection achieved a higher level of accuracy. This software tool could assist physicians in detecting retinal fluid, which is important for diagnostic, re-treatment, and prognostic tasks.
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Affiliation(s)
- Tiarnan D L Keenan
- Division of Epidemiology and Clinical Applications, National Eye Institute, National Institutes of Health, Bethesda, Maryland.
| | | | - Amitha Domalpally
- Fundus Photograph Reading Center, University of Wisconsin, Madison, Wisconsin
| | | | | | - Elvira Agrón
- Division of Epidemiology and Clinical Applications, National Eye Institute, National Institutes of Health, Bethesda, Maryland
| | | | - Emily Y Chew
- Division of Epidemiology and Clinical Applications, National Eye Institute, National Institutes of Health, Bethesda, Maryland
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Song Z, Xu L, Wang J, Rasti R, Sastry A, Li JD, Raynor W, Izatt JA, Toth CA, Vajzovic L, Deng B, Farsiu S. Lightweight Learning-Based Automatic Segmentation of Subretinal Blebs on Microscope-Integrated Optical Coherence Tomography Images. Am J Ophthalmol 2021; 221:154-168. [PMID: 32707207 PMCID: PMC8120705 DOI: 10.1016/j.ajo.2020.07.020] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/27/2019] [Revised: 07/08/2020] [Accepted: 07/09/2020] [Indexed: 10/23/2022]
Abstract
PURPOSE Subretinal injections of therapeutics are commonly used to treat ocular diseases. Accurate dosing of therapeutics at target locations is crucial but difficult to achieve using subretinal injections due to leakage, and there is no method available to measure the volume of therapeutics successfully administered to the subretinal location during surgery. Here, we introduce the first automatic method for quantifying the volume of subretinal blebs, using porcine eyes injected with Ringer's lactate solution as samples. DESIGN Ex vivo animal study. METHODS Microscope-integrated optical coherence tomography was used to obtain 3D visualization of subretinal blebs in porcine eyes at Duke Eye Center. Two different injection phases were imaged and analyzed in 15 eyes (30 volumes), selected from a total of 37 eyes. The inclusion/exclusion criteria were set independently from the algorithm-development and testing team. A novel lightweight, deep learning-based algorithm was designed to segment subretinal bleb boundaries. A cross-validation method was used to avoid selection bias. An ensemble-classifier strategy was applied to generate final results for the test dataset. RESULTS The algorithm performs notably better than 4 other state-of-the-art deep learning-based segmentation methods, achieving an F1 score of 93.86 ± 1.17% and 96.90 ± 0.59% on the independent test data for entry and full blebs, respectively. CONCLUSION The proposed algorithm accurately segmented the volumetric boundaries of Ringer's lactate solution delivered into the subretinal space of porcine eyes with robust performance and real-time speed. This is the first step for future applications in computer-guided delivery of therapeutics into the subretinal space in human subjects.
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Affiliation(s)
- Zhenxi Song
- School of Electrical and Information Engineering, Tianjin University, Tianjin, China; Department of Biomedical Engineering, Duke University, Durham, North Carolina, USA
| | - Liangyu Xu
- Department of Biomedical Engineering, Duke University, Durham, North Carolina, USA
| | - Jiang Wang
- School of Electrical and Information Engineering, Tianjin University, Tianjin, China
| | - Reza Rasti
- Department of Biomedical Engineering, Duke University, Durham, North Carolina, USA
| | - Ananth Sastry
- Department of Ophthalmology, Duke University School of Medicine, Durham, North Carolina, USA
| | - Jianwei D Li
- Department of Biomedical Engineering, Duke University, Durham, North Carolina, USA
| | - William Raynor
- Department of Ophthalmology, Duke University School of Medicine, Durham, North Carolina, USA
| | - Joseph A Izatt
- Department of Biomedical Engineering, Duke University, Durham, North Carolina, USA; Department of Ophthalmology, Duke University School of Medicine, Durham, North Carolina, USA
| | - Cynthia A Toth
- Department of Biomedical Engineering, Duke University, Durham, North Carolina, USA; Department of Ophthalmology, Duke University School of Medicine, Durham, North Carolina, USA
| | - Lejla Vajzovic
- Department of Ophthalmology, Duke University School of Medicine, Durham, North Carolina, USA
| | - Bin Deng
- School of Electrical and Information Engineering, Tianjin University, Tianjin, China
| | - Sina Farsiu
- Department of Biomedical Engineering, Duke University, Durham, North Carolina, USA; Department of Ophthalmology, Duke University School of Medicine, Durham, North Carolina, USA.
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Girish G, R. Kothari A, Rajan J. Marker controlled watershed transform for intra-retinal cysts segmentation from optical coherence tomography B-scans. Pattern Recognit Lett 2020. [DOI: 10.1016/j.patrec.2017.12.019] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/22/2023]
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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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de Moura J, Vidal PL, Novo J, Rouco J, Penedo MG, Ortega M. Intraretinal Fluid Pattern Characterization in Optical Coherence Tomography Images. SENSORS 2020; 20:s20072004. [PMID: 32260062 PMCID: PMC7180444 DOI: 10.3390/s20072004] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 03/03/2020] [Revised: 03/27/2020] [Accepted: 03/31/2020] [Indexed: 12/20/2022]
Abstract
Optical Coherence Tomography (OCT) has become a relevant image modality in the ophthalmological clinical practice, as it offers a detailed representation of the eye fundus. This medical imaging modality is currently one of the main means of identification and characterization of intraretinal cystoid regions, a crucial task in the diagnosis of exudative macular disease or macular edema, among the main causes of blindness in developed countries. This work presents an exhaustive analysis of intensity and texture-based descriptors for its identification and classification, using a complete set of 510 texture features, three state-of-the-art feature selection strategies, and seven representative classifier strategies. The methodology validation and the analysis were performed using an image dataset of 83 OCT scans. From these images, 1609 samples were extracted from both cystoid and non-cystoid regions. The different tested configurations provided satisfactory results, reaching a mean cross-validation test accuracy of 92.69%. The most promising feature categories identified for the issue were the Gabor filters, the Histogram of Oriented Gradients (HOG), the Gray-Level Run-Length matrix (GLRL), and the Laws’ texture filters (LAWS), being consistently and considerably selected along all feature selector algorithms in the top positions of different relevance rankings.
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Affiliation(s)
- Joaquim de Moura
- Centro de investigación CITIC, Universidade da Coruña, 15071 A Coruña, Spain; (J.d.M.); (J.N.); (J.R.); (M.G.P.); (M.O.)
- Grupo VARPA, Instituto de Investigación Biomédica de A Coruña (INIBIC), Universidade da Coruña, 15006 A Coruña, Spain
| | - Plácido L. Vidal
- Centro de investigación CITIC, Universidade da Coruña, 15071 A Coruña, Spain; (J.d.M.); (J.N.); (J.R.); (M.G.P.); (M.O.)
- Grupo VARPA, Instituto de Investigación Biomédica de A Coruña (INIBIC), Universidade da Coruña, 15006 A Coruña, Spain
- Correspondence:
| | - Jorge Novo
- Centro de investigación CITIC, Universidade da Coruña, 15071 A Coruña, Spain; (J.d.M.); (J.N.); (J.R.); (M.G.P.); (M.O.)
- Grupo VARPA, Instituto de Investigación Biomédica de A Coruña (INIBIC), Universidade da Coruña, 15006 A Coruña, Spain
| | - José Rouco
- Centro de investigación CITIC, Universidade da Coruña, 15071 A Coruña, Spain; (J.d.M.); (J.N.); (J.R.); (M.G.P.); (M.O.)
- Grupo VARPA, Instituto de Investigación Biomédica de A Coruña (INIBIC), Universidade da Coruña, 15006 A Coruña, Spain
| | - Manuel G. Penedo
- Centro de investigación CITIC, Universidade da Coruña, 15071 A Coruña, Spain; (J.d.M.); (J.N.); (J.R.); (M.G.P.); (M.O.)
- Grupo VARPA, Instituto de Investigación Biomédica de A Coruña (INIBIC), Universidade da Coruña, 15006 A Coruña, Spain
| | - Marcos Ortega
- Centro de investigación CITIC, Universidade da Coruña, 15071 A Coruña, Spain; (J.d.M.); (J.N.); (J.R.); (M.G.P.); (M.O.)
- Grupo VARPA, Instituto de Investigación Biomédica de A Coruña (INIBIC), Universidade da Coruña, 15006 A Coruña, Spain
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Girish GN, Saikumar B, Roychowdhury S, Kothari AR, Rajan J. Depthwise Separable Convolutional Neural Network Model for Intra-Retinal Cyst Segmentation. ANNUAL INTERNATIONAL CONFERENCE OF THE IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. ANNUAL INTERNATIONAL CONFERENCE 2020; 2019:2027-2031. [PMID: 31946299 DOI: 10.1109/embc.2019.8857333] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/09/2022]
Abstract
Intra-retinal cysts (IRCs) are significant in detecting several ocular and retinal pathologies. Segmentation and quantification of IRCs from optical coherence tomography (OCT) scans is a challenging task due to present of speckle noise and scan intensity variations across the vendors. This work proposes a convolutional neural network (CNN) model with an encoder-decoder pair architecture for IRC segmentation across different cross-vendor OCT scans. Since deep CNN models have high computational complexity due to a large number of parameters, the proposed method of depthwise separable convolutional filters aids model generalizability and prevents model over-fitting. Also, the swish activation function is employed to prevent the vanishing gradient problem. The optima cyst segmentation challenge (OCSC) dataset with four different vendor OCT device scans is used to evaluate the proposed model. Our model achieves a mean Dice score of 0.74 and mean recall/precision rate of 0.72/0.82 across different imaging vendors and it outperforms existing algorithms on the OCSC dataset.
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Automated Segmentation and Quantification of Drusen in Fundus and Optical Coherence Tomography Images for Detection of ARMD. J Digit Imaging 2019; 31:464-476. [PMID: 29204763 DOI: 10.1007/s10278-017-0038-7] [Citation(s) in RCA: 8] [Impact Index Per Article: 1.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/18/2022] Open
Abstract
Age-related macular degeneration (ARMD) is one of the most common retinal syndromes that occurs in elderly people. Different eye testing techniques such as fundus photography and optical coherence tomography (OCT) are used to clinically examine the ARMD-affected patients. Many researchers have worked on detecting ARMD from fundus images, few of them also worked on detecting ARMD from OCT images. However, there are only few systems that establish the correspondence between fundus and OCT images to give an accurate prediction of ARMD pathology. In this paper, we present fully automated decision support system that can automatically detect ARMD by establishing correspondence between OCT and fundus imagery. The proposed system also distinguishes between early, suspect and confirmed ARMD by correlating OCT B-scans with respective region of the fundus image. In first phase, proposed system uses different B-scan based features along with support vector machine (SVM) to detect the presence of drusens and classify it as ARMD or normal case. In case input OCT scan is classified as ARMD, region of interest from corresponding fundus image is considered for further evaluation. The analysis of fundus image is performed using contrast enhancement and adaptive thresholding to detect possible drusens from fundus image and proposed system finally classified it as early stage ARMD or advance stage ARMD. The proposed system is tested on local data set of 100 patients with100 fundus images and 6800 OCT B-scans. Proposed system detects ARMD with the accuracy, sensitivity, and specificity ratings of 98.0, 100, and 97.14%, respectively.
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Maloca PM, Lee AY, de Carvalho ER, Okada M, Fasler K, Leung I, Hörmann B, Kaiser P, Suter S, Hasler PW, Zarranz-Ventura J, Egan C, Heeren TFC, Balaskas K, Tufail A, Scholl HPN. Validation of automated artificial intelligence segmentation of optical coherence tomography images. PLoS One 2019; 14:e0220063. [PMID: 31419240 PMCID: PMC6697318 DOI: 10.1371/journal.pone.0220063] [Citation(s) in RCA: 35] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/12/2019] [Accepted: 07/08/2019] [Indexed: 12/15/2022] Open
Abstract
PURPOSE To benchmark the human and machine performance of spectral-domain (SD) and swept-source (SS) optical coherence tomography (OCT) image segmentation, i.e., pixel-wise classification, for the compartments vitreous, retina, choroid, sclera. METHODS A convolutional neural network (CNN) was trained on OCT B-scan images annotated by a senior ground truth expert retina specialist to segment the posterior eye compartments. Independent benchmark data sets (30 SDOCT and 30 SSOCT) were manually segmented by three classes of graders with varying levels of ophthalmic proficiencies. Nine graders contributed to benchmark an additional 60 images in three consecutive runs. Inter-human and intra-human class agreement was measured and compared to the CNN results. RESULTS The CNN training data consisted of a total of 6210 manually segmented images derived from 2070 B-scans (1046 SDOCT and 1024 SSOCT; 630 C-Scans). The CNN segmentation revealed a high agreement with all grader groups. For all compartments and groups, the mean Intersection over Union (IOU) score of CNN compartmentalization versus group graders' compartmentalization was higher than the mean score for intra-grader group comparison. CONCLUSION The proposed deep learning segmentation algorithm (CNN) for automated eye compartment segmentation in OCT B-scans (SDOCT and SSOCT) is on par with manual segmentations by human graders.
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Affiliation(s)
- Peter M. Maloca
- Institute of Molecular and Clinical Ophthalmology Basel (IOB), Basel, Switzerland
- OCTlab, Department of Ophthalmology, University Hospital Basel, Basel, Switzerland
- Department of Ophthalmology, University of Basel, Basel, Switzerland
- Moorfields Eye Hospital NHS Foundation Trust, London, United Kingdom
| | - Aaron Y. Lee
- Department of Ophthalmology, Puget Sound Veteran Affairs, Seattle, Washington, United States of America
- eScience Institute, University of Washington, Seattle, Washington, United States of America
- Department of Ophthalmology, University of Washington, Seattle, Washington, United States of America
| | | | - Mali Okada
- Royal Victorian Eye and Ear Hospital, Melbourne, Victoria, Australia
| | - Katrin Fasler
- Moorfields Eye Hospital NHS Foundation Trust, London, United Kingdom
| | - Irene Leung
- Moorfields Ophthalmic Reading Centre, London, United Kingdom
| | | | | | | | - Pascal W. Hasler
- OCTlab, Department of Ophthalmology, University Hospital Basel, Basel, Switzerland
- Department of Ophthalmology, University of Basel, Basel, Switzerland
| | | | - Catherine Egan
- Moorfields Eye Hospital NHS Foundation Trust, London, United Kingdom
| | - Tjebo F. C. Heeren
- Moorfields Eye Hospital NHS Foundation Trust, London, United Kingdom
- Institute of Ophthalmology, University College London, London, United Kingdom
| | - Konstantinos Balaskas
- Moorfields Eye Hospital NHS Foundation Trust, London, United Kingdom
- Moorfields Ophthalmic Reading Centre, London, United Kingdom
| | - Adnan Tufail
- Moorfields Eye Hospital NHS Foundation Trust, London, United Kingdom
| | - Hendrik P. N. Scholl
- Institute of Molecular and Clinical Ophthalmology Basel (IOB), Basel, Switzerland
- OCTlab, Department of Ophthalmology, University Hospital Basel, Basel, Switzerland
- Department of Ophthalmology, University of Basel, Basel, Switzerland
- Wilmer Eye Institute, Johns Hopkins University, Baltimore, Maryland, United States of America
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Feng-Ping A, Zhi-Wen L. Medical image segmentation algorithm based on feedback mechanism convolutional neural network. Biomed Signal Process Control 2019. [DOI: 10.1016/j.bspc.2019.101589] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.4] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/21/2022]
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Medical Image Segmentation Algorithm Based on Feedback Mechanism CNN. CONTRAST MEDIA & MOLECULAR IMAGING 2019; 2019:6134942. [PMID: 31481851 PMCID: PMC6701432 DOI: 10.1155/2019/6134942] [Citation(s) in RCA: 11] [Impact Index Per Article: 2.2] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 12/22/2018] [Revised: 05/18/2019] [Accepted: 06/16/2019] [Indexed: 01/17/2023]
Abstract
With the development of computer vision and image segmentation technology, medical image segmentation and recognition technology has become an important part of computer-aided diagnosis. The traditional image segmentation method relies on artificial means to extract and select information such as edges, colors, and textures in the image. It not only consumes considerable energy resources and people's time but also requires certain expertise to obtain useful feature information, which no longer meets the practical application requirements of medical image segmentation and recognition. As an efficient image segmentation method, convolutional neural networks (CNNs) have been widely promoted and applied in the field of medical image segmentation. However, CNNs that rely on simple feedforward methods have not met the actual needs of the rapid development of the medical field. Thus, this paper is inspired by the feedback mechanism of the human visual cortex, and an effective feedback mechanism calculation model and operation framework is proposed, and the feedback optimization problem is presented. A new feedback convolutional neural network algorithm based on neuron screening and neuron visual information recovery is constructed. So, a medical image segmentation algorithm based on a feedback mechanism convolutional neural network is proposed. The basic idea is as follows: The model for obtaining an initial region with the segmented medical image classifies the pixel block samples in the segmented image. Then, the initial results are optimized by threshold segmentation and morphological methods to obtain accurate medical image segmentation results. Experiments show that the proposed segmentation method has not only high segmentation accuracy but also extremely high adaptive segmentation ability for various medical images. The research in this paper provides a new perspective for medical image segmentation research. It is a new attempt to explore more advanced intelligent medical image segmentation methods. It also provides technical approaches and methods for further development and improvement of adaptive medical image segmentation technology.
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Deep Ensemble Learning Based Objective Grading of Macular Edema by Extracting Clinically Significant Findings from Fused Retinal Imaging Modalities. SENSORS 2019; 19:s19132970. [PMID: 31284442 PMCID: PMC6651513 DOI: 10.3390/s19132970] [Citation(s) in RCA: 20] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 05/26/2019] [Revised: 06/21/2019] [Accepted: 06/26/2019] [Indexed: 12/22/2022]
Abstract
Macular edema (ME) is a retinal condition in which central vision of a patient is affected. ME leads to accumulation of fluid in the surrounding macular region resulting in a swollen macula. Optical coherence tomography (OCT) and the fundus photography are the two widely used retinal examination techniques that can effectively detect ME. Many researchers have utilized retinal fundus and OCT imaging for detecting ME. However, to the best of our knowledge, no work is found in the literature that fuses the findings from both retinal imaging modalities for the effective and more reliable diagnosis of ME. In this paper, we proposed an automated framework for the classification of ME and healthy eyes using retinal fundus and OCT scans. The proposed framework is based on deep ensemble learning where the input fundus and OCT scans are recognized through the deep convolutional neural network (CNN) and are processed accordingly. The processed scans are further passed to the second layer of the deep CNN model, which extracts the required feature descriptors from both images. The extracted descriptors are then concatenated together and are passed to the supervised hybrid classifier made through the ensemble of the artificial neural networks, support vector machines and naïve Bayes. The proposed framework has been trained on 73,791 retinal scans and is validated on 5100 scans of publicly available Zhang dataset and Rabbani dataset. The proposed framework achieved the accuracy of 94.33% for diagnosing ME and healthy subjects and achieved the mean dice coefficient of 0.9019 ± 0.04 for accurately extracting the retinal fluids, 0.7069 ± 0.11 for accurately extracting hard exudates and 0.8203 ± 0.03 for accurately extracting retinal blood vessels against the clinical markings.
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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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Lu D, Heisler M, Lee S, Ding GW, Navajas E, Sarunic MV, Beg MF. Deep-learning based multiclass retinal fluid segmentation and detection in optical coherence tomography images using a fully convolutional neural network. Med Image Anal 2019; 54:100-110. [DOI: 10.1016/j.media.2019.02.011] [Citation(s) in RCA: 65] [Impact Index Per Article: 13.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/11/2018] [Revised: 02/15/2019] [Accepted: 02/15/2019] [Indexed: 11/28/2022]
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Hassan T, Akram MU, Masood MF, Yasin U. Deep structure tensor graph search framework for automated extraction and characterization of retinal layers and fluid pathology in retinal SD-OCT scans. Comput Biol Med 2019; 105:112-124. [DOI: 10.1016/j.compbiomed.2018.12.015] [Citation(s) in RCA: 21] [Impact Index Per Article: 4.2] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/04/2018] [Revised: 12/25/2018] [Accepted: 12/29/2018] [Indexed: 12/01/2022]
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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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39
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Gerendas BS, Kroisamer JS, Buehl W, Rezar-Dreindl SM, Eibenberger KM, Pablik E, Schmidt-Erfurth U, Sacu S. Correlation between morphological characteristics in spectral-domain-optical coherence tomography, different functional tests and a patient's subjective handicap in acute central serous chorioretinopathy. Acta Ophthalmol 2018; 96:e776-e782. [PMID: 29338130 DOI: 10.1111/aos.13665] [Citation(s) in RCA: 9] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/10/2017] [Accepted: 11/06/2017] [Indexed: 12/15/2022]
Abstract
PURPOSE The purpose of this study was to identify quantitatively measurable morphologic optical coherence tomography (OCT) characteristics in patients with an acute episode of central serous chorioretinopathy (CSC) and evaluate their correlation to functional and psychological variables for their use in daily clinical practice. METHODS Retinal thickness (RT), the height, area and volume of subretinal fluid (SRF)/pigment epithelium detachments were evaluated using the standardized procedures of the Vienna Reading Center. These morphologic characteristics were compared with functional variables [best-corrected visual acuity (BCVA), contrast sensitivity (CS), retinal sensitivity/microperimetry, fixation stability], and patients' subjective handicap from CSC using the National Eye Institute 25-item Visual Function Questionnaire (NEI VFQ-25). RESULTS Data from 39 CSC patients were included in this analysis. Three different SRF height measures showed a high negative correlation (r = -0.7) to retinal sensitivity within the central 9°, which was also negatively correlated with SRF area and volume (r = -0.6). The CS score and fixation stability (fixation points within 2°) showed a moderate negative correlation (r = -0.4) with SRF height variables. Comparison of the subjective handicap with morphological characteristics in spectral-domain (SD)-OCT showed SRF height had the highest correlation (r = -0.4) with the subjective problems reported and overall NEI VFQ-25 score. CONCLUSION In conclusion, SRF height measured in SD-OCT showed the best correlation with functional variables and patients' subjective handicap caused by the disease and therefore seems to be the best variable to look at in daily clinical routine. Even though area and volume also show a correlation, these cannot be so easily measured as height and are therefore not suggested for daily clinical routine.
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Affiliation(s)
- Bianca S. Gerendas
- Department of Ophthalmology; Vienna Reading Center; Vienna Clinical Trial Center; Medical University Vienna; Vienna Austria
| | - Julia-Sophie Kroisamer
- Department of Ophthalmology; Vienna Reading Center; Vienna Clinical Trial Center; Medical University Vienna; Vienna Austria
| | - Wolf Buehl
- Department of Ophthalmology; Vienna Reading Center; Vienna Clinical Trial Center; Medical University Vienna; Vienna Austria
| | - Sandra M. Rezar-Dreindl
- Department of Ophthalmology; Vienna Reading Center; Vienna Clinical Trial Center; Medical University Vienna; Vienna Austria
| | - Katharina M. Eibenberger
- Department of Ophthalmology; Vienna Reading Center; Vienna Clinical Trial Center; Medical University Vienna; Vienna Austria
| | - Eleonore Pablik
- Center for Medical Statistics, Informatics and Intelligent Systems; Section for Medical Statistics; Medical University of Vienna; Vienna Austria
| | - Ursula Schmidt-Erfurth
- Department of Ophthalmology; Vienna Reading Center; Vienna Clinical Trial Center; Medical University Vienna; Vienna Austria
| | - Stefan Sacu
- Department of Ophthalmology; Vienna Reading Center; Vienna Clinical Trial Center; Medical University Vienna; Vienna Austria
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Hassan T, Akram MU, Akhtar M, Khan SA, Yasin U. Multilayered Deep Structure Tensor Delaunay Triangulation and Morphing Based Automated Diagnosis and 3D Presentation of Human Macula. J Med Syst 2018; 42:223. [PMID: 30284052 DOI: 10.1007/s10916-018-1078-3] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/26/2018] [Accepted: 09/19/2018] [Indexed: 10/28/2022]
Abstract
Maculopathy is the group of diseases that affects central vision of a person and they are often associated with diabetes. Many researchers reported automated diagnosis of maculopathy from optical coherence tomography (OCT) images. However, to the best of our knowledge there is no literature that presents a complete 3D suite for the extraction as well as diagnosis of macula. Therefore, this paper presents a multilayered convolutional neural networks (CNN) structure tensor Delaunay triangulation and morphing based fully autonomous system that extracts up to nine retinal and choroidal layers along with the macular fluids. Furthermore, the proposed system utilizes the extracted retinal information for the automated diagnosis of maculopathy as well as for the robust reconstruction of 3D macula of retina. The proposed system has been validated on 41,921 retinal OCT scans acquired from different OCT machines and it significantly outperformed existing state of the art solutions by achieving the mean accuracy of 95.27% for extracting retinal and choroidal layers, mean dice coefficient of 0.90 for extracting fluid pathology and the overall accuracy of 96.07% for maculopathy diagnosis. To the best of our knowledge, the proposed framework is first of its kind that provides a fully automated and complete 3D integrated solution for the extraction of candidate macula along with its fully automated diagnosis against different macular syndromes.
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Affiliation(s)
- Taimur Hassan
- Department of Computer & Software Engineering, National University of Sciences and Technology (NUST), Islamabad, 44000, Pakistan.,Department of Electrical Engineering, Bahria University, Islamabad, 44000, Pakistan
| | - M Usman Akram
- Department of Computer & Software Engineering, National University of Sciences and Technology (NUST), Islamabad, 44000, Pakistan.
| | - Mahmood Akhtar
- School of Civil and Environmental Engineering's Research Centre for Integrated Transport Innovation (rCITI), University of New South Wales, Sydney, Australia
| | - Shoab Ahmad Khan
- Department of Computer & Software Engineering, National University of Sciences and Technology (NUST), Islamabad, 44000, Pakistan
| | - Ubaidullah Yasin
- Department of Ophthalmology, Armed Forces Institute of Ophthalmology, Rawalpindi, Pakistan
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Vidal PL, de Moura J, Novo J, Penedo MG, Ortega M. Intraretinal fluid identification via enhanced maps using optical coherence tomography images. BIOMEDICAL OPTICS EXPRESS 2018; 9:4730-4754. [PMID: 30319899 PMCID: PMC6179401 DOI: 10.1364/boe.9.004730] [Citation(s) in RCA: 12] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Received: 05/14/2018] [Revised: 07/16/2018] [Accepted: 08/12/2018] [Indexed: 05/28/2023]
Abstract
Nowadays, among the main causes of blindness in developed countries are age-related macular degeneration (AMD) and the diabetic macular edema (DME). Both diseases present, as a common symptom, the appearance of cystoid fluid regions inside the retinal layers. Optical coherence tomography (OCT) image modality was one of the main medical imaging techniques for the early diagnosis and monitoring of AMD and DME via this intraretinal fluid detection and characterization. We present a novel methodology to identify these fluid accumulations by means of generating binary maps (offering a direct representation of these areas) and heat maps (containing the region confidence). To achieve this, a set of 312 intensity and texture-based features were studied. The most relevant features were selected using the sequential forward selection (SFS) strategy and tested with three archetypal classifiers: LDC, SVM and Parzen window. Finally, the most proficient classifier is used to create the proposed maps. All of the tested classifiers returned satisfactory results, the best classifier achieving a mean test accuracy higher than 94% in all of the experiments. The suitability of the maps was evaluated in a context of a screening issue with three different datasets obtained with two different devices, testing the capabilities of the system to work independently of the used OCT device. The experiments with the map creation were performed using 323 OCT images. Using only the binary maps, more than 91.33% of the images were correctly classified. With only the heat maps, the proposed methodology correctly separated 93.50% of the images.
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Affiliation(s)
- Plácido L. Vidal
- Department of Computer Science, University of A Coruña, 15071 A Coruña,
Spain
- CITIC-Research Center of Information and Communication Technologies, University of A Coruña, 15071 A Coruña,
Spain
| | - Joaquim de Moura
- Department of Computer Science, University of A Coruña, 15071 A Coruña,
Spain
- CITIC-Research Center of Information and Communication Technologies, University of A Coruña, 15071 A Coruña,
Spain
| | - Jorge Novo
- Department of Computer Science, University of A Coruña, 15071 A Coruña,
Spain
- CITIC-Research Center of Information and Communication Technologies, University of A Coruña, 15071 A Coruña,
Spain
| | - Manuel G. Penedo
- Department of Computer Science, University of A Coruña, 15071 A Coruña,
Spain
- CITIC-Research Center of Information and Communication Technologies, University of A Coruña, 15071 A Coruña,
Spain
| | - Marcos Ortega
- Department of Computer Science, University of A Coruña, 15071 A Coruña,
Spain
- CITIC-Research Center of Information and Communication Technologies, University of A Coruña, 15071 A Coruña,
Spain
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Development of an efficient algorithm for the detection of macular edema from optical coherence tomography images. Int J Comput Assist Radiol Surg 2018; 13:1369-1377. [DOI: 10.1007/s11548-018-1795-6] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/22/2017] [Accepted: 05/17/2018] [Indexed: 10/16/2022]
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Schlegl T, Waldstein SM, Bogunovic H, Endstraßer F, Sadeghipour A, Philip AM, Podkowinski D, Gerendas BS, Langs G, Schmidt-Erfurth U. Fully Automated Detection and Quantification of Macular Fluid in OCT Using Deep Learning. Ophthalmology 2018; 125:549-558. [DOI: 10.1016/j.ophtha.2017.10.031] [Citation(s) in RCA: 274] [Impact Index Per Article: 45.7] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/04/2017] [Revised: 10/23/2017] [Accepted: 10/24/2017] [Indexed: 12/14/2022] Open
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Venhuizen FG, van Ginneken B, Liefers B, van Asten F, Schreur V, Fauser S, Hoyng C, Theelen T, Sánchez CI. Deep learning approach for the detection and quantification of intraretinal cystoid fluid in multivendor optical coherence tomography. BIOMEDICAL OPTICS EXPRESS 2018; 9:1545-1569. [PMID: 29675301 PMCID: PMC5905905 DOI: 10.1364/boe.9.001545] [Citation(s) in RCA: 78] [Impact Index Per Article: 13.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 11/15/2017] [Revised: 01/13/2018] [Accepted: 01/31/2018] [Indexed: 05/18/2023]
Abstract
We developed a deep learning algorithm for the automatic segmentation and quantification of intraretinal cystoid fluid (IRC) in spectral domain optical coherence tomography (SD-OCT) volumes independent of the device used for acquisition. A cascade of neural networks was introduced to include prior information on the retinal anatomy, boosting performance significantly. The proposed algorithm approached human performance reaching an overall Dice coefficient of 0.754 ± 0.136 and an intraclass correlation coefficient of 0.936, for the task of IRC segmentation and quantification, respectively. The proposed method allows for fast quantitative IRC volume measurements that can be used to improve patient care, reduce costs, and allow fast and reliable analysis in large population studies.
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Affiliation(s)
- Freerk G. Venhuizen
- Diagnostic Image Analysis Group, Radboud University Medical Center, Nijmegen,
the Netherlands
- Department of Ophthalmology, Donders Institute for Brain, Cognition and Behaviour, Radboud University Medical Center, Nijmegen,
the Netherlands
| | - Bram van Ginneken
- Diagnostic Image Analysis Group, Radboud University Medical Center, Nijmegen,
the Netherlands
| | - Bart Liefers
- Diagnostic Image Analysis Group, Radboud University Medical Center, Nijmegen,
the Netherlands
- Department of Ophthalmology, Donders Institute for Brain, Cognition and Behaviour, Radboud University Medical Center, Nijmegen,
the Netherlands
| | - Freekje van Asten
- Department of Ophthalmology, Donders Institute for Brain, Cognition and Behaviour, Radboud University Medical Center, Nijmegen,
the Netherlands
| | - Vivian Schreur
- Department of Ophthalmology, Donders Institute for Brain, Cognition and Behaviour, Radboud University Medical Center, Nijmegen,
the Netherlands
| | - Sascha Fauser
- Roche Pharma Research and Early Development, F. Hoffmann-La Roche Ltd, Basel,
Switzerland
- Cologne University Eye Clinic, Cologne,
Germany
| | - Carel Hoyng
- Department of Ophthalmology, Donders Institute for Brain, Cognition and Behaviour, Radboud University Medical Center, Nijmegen,
the Netherlands
| | - Thomas Theelen
- Diagnostic Image Analysis Group, Radboud University Medical Center, Nijmegen,
the Netherlands
- Department of Ophthalmology, Donders Institute for Brain, Cognition and Behaviour, Radboud University Medical Center, Nijmegen,
the Netherlands
| | - Clara I. Sánchez
- Diagnostic Image Analysis Group, Radboud University Medical Center, Nijmegen,
the Netherlands
- Department of Ophthalmology, Donders Institute for Brain, Cognition and Behaviour, Radboud University Medical Center, Nijmegen,
the Netherlands
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45
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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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Gopinath K, Sivaswamy J. Segmentation of Retinal Cysts From Optical Coherence Tomography Volumes Via Selective Enhancement. IEEE J Biomed Health Inform 2018; 23:273-282. [PMID: 29994501 DOI: 10.1109/jbhi.2018.2793534] [Citation(s) in RCA: 27] [Impact Index Per Article: 4.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/07/2022]
Abstract
Automated and accurate segmentation of cystoid structures in optical coherence tomography (OCT) is of interest in the early detection of retinal diseases. It is, however, a challenging task. We propose a novel method for localizing cysts in 3-D OCT volumes. The proposed work is biologically inspired and based on selective enhancement of the cysts, by inducing motion to a given OCT slice. A convolutional neural network is designed to learn a mapping function that combines the result of multiple such motions to produce a probability map for cyst locations in a given slice. The final segmentation of cysts is obtained via simple clustering of the detected cyst locations. The proposed method is evaluated on two public datasets and one private dataset. The public datasets include the one released for the OPTIMA cyst segmentation challenge (OCSC) in MICCAI 2015 and the DME dataset. After training on the OCSC train set, the method achieves a mean dice coefficient (DC) of 0.71 on the OCSC test set. The robustness of the algorithm was examined by cross validation on the DME and AEI (private) datasets and a mean DC values obtained were 0.69 and 0.79, respectively. Overall, the proposed system has the highest performance on all the benchmarks. These results underscore the strengths of the proposed method in handling variations in both data acquisition protocols and scanners.
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Lang A, Carass A, Jedynak BM, Solomon SD, Calabresi PA, Prince JL. Intensity inhomogeneity correction of SD-OCT data using macular flatspace. Med Image Anal 2018; 43:85-97. [PMID: 29040910 PMCID: PMC6311386 DOI: 10.1016/j.media.2017.09.008] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/16/2017] [Revised: 09/25/2017] [Accepted: 09/29/2017] [Indexed: 01/12/2023]
Abstract
Images of the retina acquired using optical coherence tomography (OCT) often suffer from intensity inhomogeneity problems that degrade both the quality of the images and the performance of automated algorithms utilized to measure structural changes. This intensity variation has many causes, including off-axis acquisition, signal attenuation, multi-frame averaging, and vignetting, making it difficult to correct the data in a fundamental way. This paper presents a method for inhomogeneity correction by acting to reduce the variability of intensities within each layer. In particular, the N3 algorithm, which is popular in neuroimage analysis, is adapted to work for OCT data. N3 works by sharpening the intensity histogram, which reduces the variation of intensities within different classes. To apply it here, the data are first converted to a standardized space called macular flat space (MFS). MFS allows the intensities within each layer to be more easily normalized by removing the natural curvature of the retina. N3 is then run on the MFS data using a modified smoothing model, which improves the efficiency of the original algorithm. We show that our method more accurately corrects gain fields on synthetic OCT data when compared to running N3 on non-flattened data. It also reduces the overall variability of the intensities within each layer, without sacrificing contrast between layers, and improves the performance of registration between OCT images.
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Affiliation(s)
- Andrew Lang
- Department of Electrical and Computer Engineering, The Johns Hopkins University, Baltimore, MD 21218, USA.
| | - Aaron Carass
- Department of Electrical and Computer Engineering, The Johns Hopkins University, Baltimore, MD 21218, USA; Department of Computer Science, The Johns Hopkins University, Baltimore, MD 21218, USA.
| | - Bruno M Jedynak
- Department of Mathematics and Statistics, Portland State University, Portland, OR 97201, USA.
| | - Sharon D Solomon
- Department of Ophthalmology, The Johns Hopkins School of Medicine, Baltimore, MD 21287, USA.
| | - Peter A Calabresi
- Department of Neurology, The Johns Hopkins School of Medicine, Baltimore, MD 21287, USA.
| | - Jerry L Prince
- Department of Electrical and Computer Engineering, The Johns Hopkins University, Baltimore, MD 21218, USA.
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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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50
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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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