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Wijesinghe RE, Kahatapitiya NS, Lee C, Han S, Kim S, Saleah SA, Seong D, Silva BN, Wijenayake U, Ravichandran NK, Jeon M, Kim J. Growing Trend to Adopt Speckle Variance Optical Coherence Tomography for Biological Tissue Assessments in Pre-Clinical Applications. MICROMACHINES 2024; 15:564. [PMID: 38793137 PMCID: PMC11122893 DOI: 10.3390/mi15050564] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 03/25/2024] [Revised: 04/19/2024] [Accepted: 04/22/2024] [Indexed: 05/26/2024]
Abstract
Speckle patterns are a generic feature in coherent imaging techniques like optical coherence tomography (OCT). Although speckles are granular like noise texture, which degrades the image, they carry information that can be benefited by processing and thereby furnishing crucial information of sample structures, which can serve to provide significant important structural details of samples in in vivo longitudinal pre-clinical monitoring and assessments. Since the motions of tissue molecules are indicated through speckle patterns, speckle variance OCT (SV-OCT) can be well-utilized for quantitative assessments of speckle variance (SV) in biological tissues. SV-OCT has been acknowledged as a promising method for mapping microvasculature in transverse-directional blood vessels with high resolution in micrometers in both the transverse and depth directions. The fundamental scope of this article reviews the state-of-the-art and clinical benefits of SV-OCT to assess biological tissues for pre-clinical applications. In particular, focus on precise quantifications of in vivo vascular response, therapy assessments, and real-time temporal vascular effects of SV-OCT are primarily emphasized. Finally, SV-OCT-incorporating pre-clinical techniques with high potential are presented for future biomedical applications.
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Affiliation(s)
- Ruchire Eranga Wijesinghe
- Department of Electrical and Electronic Engineering, Faculty of Engineering, Sri Lanka Institute of Information Technology, Malabe 10115, Sri Lanka;
- Center for Excellence in Intelligent Informatics, Electronics & Transmission (CIET), Sri Lanka Institute of Information Technology, Malabe 10115, Sri Lanka
| | - Nipun Shantha Kahatapitiya
- Department of Computer Engineering, Faculty of Engineering, University of Sri Jayewardenepura, Nugegoda 10250, Sri Lanka; (N.S.K.); (U.W.)
| | - Changho Lee
- Department of Artificial Intelligence Convergence, Chonnam National University, Gwangju 61186, Republic of Korea
- Department of Nuclear Medicine, Chonnam National University Medical School & Hwasun Hospital, 264, Seoyang-ro, Hwasun 58128, Republic of Korea
| | - Sangyeob Han
- ICT Convergence Research Center, Kyungpook National University, 80, Daehak-ro, Buk-gu, Daegu 41566, Republic of Korea
| | - Shinheon Kim
- ICT Convergence Research Center, Kyungpook National University, 80, Daehak-ro, Buk-gu, Daegu 41566, Republic of Korea
| | - Sm Abu Saleah
- ICT Convergence Research Center, Kyungpook National University, 80, Daehak-ro, Buk-gu, Daegu 41566, Republic of Korea
| | - Daewoon Seong
- School of Electronic and Electrical Engineering, College of IT Engineering, Kyungpook National University, 80, Daehak-ro, Buk-gu, Daegu 41566, Republic of Korea
| | - Bhagya Nathali Silva
- Center for Excellence in Intelligent Informatics, Electronics & Transmission (CIET), Sri Lanka Institute of Information Technology, Malabe 10115, Sri Lanka
- Faculty of Computing, Sri Lanka Institute of Information Technology, Malabe 10115, Sri Lanka
| | - Udaya Wijenayake
- Department of Computer Engineering, Faculty of Engineering, University of Sri Jayewardenepura, Nugegoda 10250, Sri Lanka; (N.S.K.); (U.W.)
| | - Naresh Kumar Ravichandran
- Center for Scientific Instrumentation, Korea Basic Science Institute, 169-148, Gwahak-ro, Yuseong-gu, Daejeon 34133, Republic of Korea
| | - Mansik Jeon
- School of Electronic and Electrical Engineering, College of IT Engineering, Kyungpook National University, 80, Daehak-ro, Buk-gu, Daegu 41566, Republic of Korea
| | - Jeehyun Kim
- School of Electronic and Electrical Engineering, College of IT Engineering, Kyungpook National University, 80, Daehak-ro, Buk-gu, Daegu 41566, Republic of Korea
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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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Wang X, Zhang Y, Ma Y, Kwapong WR, Ying J, Lu J, Ma S, Yan Q, Yi Q, Zhao Y. Automated evaluation of retinal hyperreflective foci changes in diabetic macular edema patients before and after intravitreal injection. Front Med (Lausanne) 2023; 10:1280714. [PMID: 37869163 PMCID: PMC10587607 DOI: 10.3389/fmed.2023.1280714] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/21/2023] [Accepted: 09/21/2023] [Indexed: 10/24/2023] Open
Abstract
Purpose Fast and automated reconstruction of retinal hyperreflective foci (HRF) is of great importance for many eye-related disease understanding. In this paper, we introduced a new automated framework, driven by recent advances in deep learning to automatically extract 12 three-dimensional parameters from the segmented hyperreflective foci in optical coherence tomography (OCT). Methods Unlike traditional convolutional neural networks, which struggle with long-range feature correlations, we introduce a spatial and channel attention module within the bottleneck layer, integrated into the nnU-Net architecture. Spatial Attention Block aggregates features across spatial locations to capture related features, while Channel Attention Block heightens channel feature contrasts. The proposed model was trained and tested on 162 retinal OCT volumes of patients with diabetic macular edema (DME), yielding robust segmentation outcomes. We further investigate HRF's potential as a biomarker of DME. Results Results unveil notable discrepancies in the amount and volume of HRF subtypes. In the whole retinal layer (WR), the mean distance from HRF to the retinal pigmented epithelium was significantly reduced after treatment. In WR, the improvement in central macular thickness resulting from intravitreal injection treatment was positively correlated with the mean distance from HRF subtypes to the fovea. Conclusion Our study demonstrates the applicability of OCT for automated quantification of retinal HRF in DME patients, offering an objective, quantitative approach for clinical and research applications.
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Affiliation(s)
- Xingguo Wang
- Cixi Biomedical Research Institute, Wenzhou Medical University, Ningbo, China
- Institute of Biomedical Engineering, Ningbo Institute of Materials Technology and Engineering, Chinese Academy of Sciences, Ningbo, China
| | - Yanyan Zhang
- The Affiliated Ningbo Eye Hospital of Wenzhou Medical University, Ningbo, China
| | - Yuhui Ma
- Institute of Biomedical Engineering, Ningbo Institute of Materials Technology and Engineering, Chinese Academy of Sciences, Ningbo, China
| | | | - Jianing Ying
- Health Science Center, Ningbo University, Ningbo, China
| | - Jiayi Lu
- Cixi Biomedical Research Institute, Wenzhou Medical University, Ningbo, China
- Institute of Biomedical Engineering, Ningbo Institute of Materials Technology and Engineering, Chinese Academy of Sciences, Ningbo, China
| | - Shaodong Ma
- Institute of Biomedical Engineering, Ningbo Institute of Materials Technology and Engineering, Chinese Academy of Sciences, Ningbo, China
| | - Qifeng Yan
- Institute of Biomedical Engineering, Ningbo Institute of Materials Technology and Engineering, Chinese Academy of Sciences, Ningbo, China
| | - Quanyong Yi
- The Affiliated Ningbo Eye Hospital of Wenzhou Medical University, Ningbo, China
| | - Yitian Zhao
- Cixi Biomedical Research Institute, Wenzhou Medical University, Ningbo, China
- Institute of Biomedical Engineering, Ningbo Institute of Materials Technology and Engineering, Chinese Academy of Sciences, Ningbo, China
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Bai Y, Li J, Shi L, Jiang Q, Yan B, Wang Z. DME-DeepLabV3+: a lightweight model for diabetic macular edema extraction based on DeepLabV3+ architecture. Front Med (Lausanne) 2023; 10:1150295. [PMID: 37746086 PMCID: PMC10515718 DOI: 10.3389/fmed.2023.1150295] [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: 01/24/2023] [Accepted: 08/25/2023] [Indexed: 09/26/2023] Open
Abstract
Introduction Diabetic macular edema (DME) is a major cause of vision impairment in the patients with diabetes. Optical Coherence Tomography (OCT) is an important ophthalmic imaging method, which can enable early detection of DME. However, it is difficult to achieve high-efficiency and high-precision extraction of DME in OCT images because the sources of OCT images are diverse and the quality of OCT images is not stable. Thus, it is still required to design a model to improve the accuracy of DME extraction in OCT images. Methods A lightweight model (DME-DeepLabV3+) was proposed for DME extraction using a DeepLabV3+ architecture. In this model, MobileNetV2 model was used as the backbone for extracting low-level features of DME. The improved ASPP with sawtooth wave-like dilation rate was used for extracting high-level features of DME. Then, the decoder was used to fuse and refine low-level and high-level features of DME. Finally, 1711 OCT images were collected from the Kermany dataset and the Affiliated Eye Hospital. 1369, 171, and 171 OCT images were randomly selected for training, validation, and testing, respectively. Conclusion In ablation experiment, the proposed DME-DeepLabV3+ model was compared against DeepLabV3+ model with different setting to evaluate the effects of MobileNetV2 and improved ASPP on DME extraction. DME-DeepLabV3+ had better extraction performance, especially in small-scale macular edema regions. The extraction results of DME-DeepLabV3+ were close to ground truth. In comparative experiment, the proposed DME-DeepLabV3+ model was compared against other models, including FCN, UNet, PSPNet, ICNet, and DANet, to evaluate DME extraction performance. DME-DeepLabV3+ model had better DME extraction performance than other models as shown by greater pixel accuracy (PA), mean pixel accuracy (MPA), precision (Pre), recall (Re), F1-score (F1), and mean Intersection over Union (MIoU), which were 98.71%, 95.23%, 91.19%, 91.12%, 91.15%, and 91.18%, respectively. Discussion DME-DeepLabV3+ model is suitable for DME extraction in OCT images and can assist the ophthalmologists in the management of ocular diseases.
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Affiliation(s)
- Yun Bai
- College of Information Science, Shanghai Ocean University, Shanghai, China
| | - Jing Li
- College of Information Science, Shanghai Ocean University, Shanghai, China
| | - Lianjun Shi
- The Affiliated Eye Hospital, Nanjing Medical University, Nanjing, China
| | - Qin Jiang
- The Affiliated Eye Hospital, Nanjing Medical University, Nanjing, China
| | - Biao Yan
- Eye Institute, Eye and ENT Hospital, Shanghai Medical College, Fudan University, Shanghai, China
| | - Zhenhua Wang
- College of Information Science, Shanghai Ocean University, Shanghai, China
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Li F, Pan W, Xiang W, Zou H. Automatic segmentation of multitype retinal fluid from optical coherence tomography images using semisupervised deep learning network. Br J Ophthalmol 2023; 107:1350-1355. [PMID: 35697498 DOI: 10.1136/bjophthalmol-2022-321348] [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: 02/22/2022] [Accepted: 05/19/2022] [Indexed: 11/03/2022]
Abstract
BACKGROUND/AIMS To develop and validate a deep learning model for automated segmentation of multitype retinal fluid using optical coherence tomography (OCT) images. METHODS We retrospectively collected a total of 2814 completely anonymised OCT images with subretinal fluid (SRF) and intraretinal fluid (IRF) from 141 patients between July 2018 and June 2020, constituting our in-house retinal OCT dataset. On this dataset, we developed a novel semisupervised retinal fluid segmentation deep network (Ref-Net) to automatically identify SRF and IRF in a coarse-to-refine fashion. We performed quantitative and qualitative analyses on the model's performance while verifying its generalisation ability by using our in-house retinal OCT dataset for training and an unseen Kermany dataset for testing. We also determined the importance of major components in the semisupervised Ref-Net through extensive ablation. The main outcome measures were Dice similarity coefficient (Dice), sensitivity (Sen), specificity (Spe) and mean absolute error (MAE). RESULTS Our model trained on a handful of labelled OCT images manifested higher performance (Dice: 81.2%, Sen: 87.3%, Spe: 98.8% and MAE: 1.1% for SRF; Dice: 78.0%, Sen: 83.6%, Spe: 99.3% and MAE: 0.5% for IRF) over most cutting-edge segmentation models. It obtained expert-level performance with only 80 labelled OCT images and even exceeded two out of three ophthalmologists with 160 labelled OCT images. Its satisfactory generalisation capability across an unseen dataset was also demonstrated. CONCLUSION The semisupervised Ref-Net required only la few labelled OCT images to generate outstanding performance in automate segmentation of multitype retinal fluid, which has the potential for providing assistance for clinicians in the management of ocular disease.
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Affiliation(s)
- Feng Li
- School of Optical-Electrical and Computer Engineering, University of Shanghai for Science and Technology, Shanghai, China
| | - WenZhe Pan
- School of Optical-Electrical and Computer Engineering, University of Shanghai for Science and Technology, Shanghai, China
| | - Wenjie Xiang
- School of Optical-Electrical and Computer Engineering, University of Shanghai for Science and Technology, Shanghai, China
| | - Haidong Zou
- Shanghai Eye Disease Prevention and Treatment Center, Shanghai, China
- Shanghai General Hospital, Shanghai, China
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Gende M, de Moura J, Novo J, Penedo MG, Ortega M. A new generative approach for optical coherence tomography data scarcity: unpaired mutual conversion between scanning presets. Med Biol Eng Comput 2023; 61:1093-1112. [PMID: 36680707 PMCID: PMC10083164 DOI: 10.1007/s11517-022-02742-6] [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: 05/31/2022] [Accepted: 12/09/2022] [Indexed: 01/22/2023]
Abstract
In optical coherence tomography (OCT), there is a trade-off between the scanning time and image quality, leading to a scarcity of high quality data. OCT platforms provide different scanning presets, producing visually distinct images, limiting their compatibility. In this work, a fully automatic methodology for the unpaired visual conversion of the two most prevalent scanning presets is proposed. Using contrastive unpaired translation generative adversarial architectures, low quality images acquired with the faster Macular Cube preset can be converted to the visual style of high visibility Seven Lines scans and vice-versa. This modifies the visual appearance of the OCT images generated by each preset while preserving natural tissue structure. The quality of original and synthetic generated images was compared using BRISQUE. The synthetic generated images achieved very similar scores to original images of their target preset. The generative models were validated in automatic and expert separability tests. These models demonstrated they were able to replicate the genuine look of the original images. This methodology has the potential to create multi-preset datasets with which to train robust computer-aided diagnosis systems by exposing them to the visual features of different presets they may encounter in real clinical scenarios without having to obtain additional data. Graphical Abstract Unpaired mutual conversion between scanning presets. Two generative adversarial models are trained for the conversion of OCT images into images of another scanning preset, replicating the visual features that characterise said preset.
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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, A Coruña, 15006, A Coruña, Spain.,Centro de investigación, CITIC, Universidade da Coruña, Campus de Elviña s/n, A Coruña, 15071, A Coruña, Spain
| | - Joaquim de Moura
- Grupo, VARPA, Instituto de Investigación Biomédica de A Coruña (INIBIC), Universidade da Coruña, Xubias de Arriba, 84, A Coruña, 15006, A Coruña, Spain. .,Centro de investigación, CITIC, Universidade da Coruña, Campus de Elviña s/n, A Coruña, 15071, A Coruña, Spain.
| | - Jorge Novo
- Grupo, VARPA, Instituto de Investigación Biomédica de A Coruña (INIBIC), Universidade da Coruña, Xubias de Arriba, 84, A Coruña, 15006, A Coruña, Spain.,Centro de investigación, CITIC, Universidade da Coruña, Campus de Elviña s/n, A Coruña, 15071, A Coruña, Spain
| | - Manuel G Penedo
- Grupo, VARPA, Instituto de Investigación Biomédica de A Coruña (INIBIC), Universidade da Coruña, Xubias de Arriba, 84, A Coruña, 15006, A Coruña, Spain.,Centro de investigación, CITIC, Universidade da Coruña, Campus de Elviña s/n, A Coruña, 15071, A Coruña, Spain
| | - Marcos Ortega
- Grupo, VARPA, Instituto de Investigación Biomédica de A Coruña (INIBIC), Universidade da Coruña, Xubias de Arriba, 84, A Coruña, 15006, A Coruña, Spain.,Centro de investigación, CITIC, Universidade da Coruña, Campus de Elviña s/n, A Coruña, 15071, A Coruña, Spain
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Chew L, Mohammadzadeh V, Mohammadi M, Toriz V, Rosa N, Gorin MB, Amini N, Nouri-Mahdavi K. Measurement of the Inner Macular Layers for Monitoring of Glaucoma: Confounding Effects of Age-Related Macular Degeneration. Ophthalmol Glaucoma 2023; 6:68-77. [PMID: 35750324 PMCID: PMC9937646 DOI: 10.1016/j.ogla.2022.06.006] [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: 01/07/2022] [Revised: 06/08/2022] [Accepted: 06/10/2022] [Indexed: 01/25/2023]
Abstract
OBJECTIVE To investigate the confounding effect of nonexudative age-related macular degeneration (AMD), specifically drusen and outer retinal atrophy, on the architecture and automated segmentation of the inner retinal layers as measured with OCT. DESIGN Observational cross-sectional study. SUBJECTS Two hundred sixty-three consecutive eyes with nonexudative AMD were identified through a retrospective chart review. Exclusion criteria were a diagnosis of glaucoma or glaucoma suspect, other retinal pathology affecting the macula, axial length > 26.5 mm or spherical equivalent less than -6 diopters, any other optic nerve or neurologic disorders, or poor image quality. METHODS Drusen were automatically segmented on macular OCT B-scans with a publicly available and validated deep learning approach. Automated segmentation of the inner plexiform layer (IPL)/inner nuclear layer (INL) boundary was carried out with the device's proprietary software. MAIN OUTCOME MEASURES Quality of segmentation of the IPL/INL boundary as a function of drusen size and presence of inner retinal layer displacement in the area of macular pathology (drusen or atrophy). RESULTS One hundred twenty-five eyes (65 patients) met the inclusion criteria. Drusen size varied between 16 and 272 μm (mean, 118 μm). Automated segmentation had a 22% chance of failure if the drusen height was between 145 and 185 μm and was most likely to fail with drusen heights above 185 μm. When drusen height was normalized by total retinal thickness, segmentation failed 36% of the time when the drusen to total retinal thickness ratio was 0.45 or above. Images were likely to show displacement of inner retinal layers with drusen heights above 176 μm and a normalized drusen height ratio of 0.5 or higher. Eighty-seven percent of images with outer retinal atrophy displayed incorrect segmentation. CONCLUSIONS Outer retinal diseases can alter the retinal topography and affect the segmentation accuracy of the inner retinal layers. Large drusen may cause segmentation error and compression of the inner macular layers. Geographic atrophy confounds automated segmentation in a high proportion of eyes. Clinicians should be cognizant of the effects of outer retinal disease on the inner retinal layer measurements when interpreting the results of macular OCT imaging in patients with glaucoma.
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Affiliation(s)
- Leila Chew
- Glaucoma Division, Stein Eye Institute, David Geffen School of Medicine, University of California Los Angeles, Los Angeles, California
| | - Vahid Mohammadzadeh
- Glaucoma Division, Stein Eye Institute, David Geffen School of Medicine, University of California Los Angeles, Los Angeles, California
| | - Massood Mohammadi
- Glaucoma Division, Stein Eye Institute, David Geffen School of Medicine, University of California Los Angeles, Los Angeles, California
| | - Veronica Toriz
- Department of Computer Science, California State University Los Angeles, Los Angeles, California
| | - Nancy Rosa
- Department of Computer Science, California State University Los Angeles, Los Angeles, California
| | - Michael B. Gorin
- Retinal Disorders and Ophthalmic Genetics Division, Stein Eye Institute, David Geffen School of Medicine, University of California Los Angeles, Los Angeles, California
| | - Navid Amini
- Department of Computer Science, California State University Los Angeles, Los Angeles, California
| | - Kouros Nouri-Mahdavi
- Glaucoma Division, Stein Eye Institute, David Geffen School of Medicine, University of California Los Angeles, Los Angeles, California.
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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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Wu J, Liu S, Xiao Z, Zhang F, Geng L. Joint segmentation of retinal layers and macular edema in optical coherence tomography scans based on RLMENet. Med Phys 2022; 49:7150-7166. [PMID: 36574592 DOI: 10.1002/mp.15866] [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: 02/25/2022] [Revised: 07/05/2022] [Accepted: 07/09/2022] [Indexed: 12/29/2022] Open
Abstract
PURPOSE The segmentation of retinal layers and fluid lesions on retinal optical coherence tomography (OCT) images is an important component of screening and diagnosing retinopathy in clinical ophthalmic treatment. We designed a novel network for accurate segmentation of the seven tissue layers of the retina and lesion areas of diabetic macular edema (DME), which can assist doctors to quantitatively analyze the disease. METHODS In this article, we propose the Retinal Layer Macular Edema Network (RLMENet) model to achieve end-to-end joint segmentation of retinal layers and fluids. The network employs dense multiscale attention to enhance the extraction of retinal layer and fluid detail information and achieve efficient long-range modeling, which improves the receptive field and obtains multiscale features. As the more complex decoder part is designed, which integrates more low-level feature information on the decoder side, more features are extracted to gradually restore the resolution of the feature map and improve the segmentation accuracy. RESULTS We used part of the OCT2017 dataset to train and verify the model to divide the data into a training set, validation set, and test set and set it to a 7:2:1 ratio. We evaluated our method on the ISIC2017 dataset. Experimental results showed that the RLMENet model designed in this work can accurately segment seven retinal tissue layers and DME lesions on the retinal OCT dataset. Finally, the MIoU value in the test set reached 86.55%. The model can be extended to other medical image segmentation datasets to achieve better segmentation performance. CONCLUSIONS The proposed method was superior to the existing segmentation methods, achieved a more refined segmentation effect and provided an auxiliary analysis tool for clinical diagnosis and treatment. This article is protected by copyright. All rights reserved.
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Affiliation(s)
- Jun Wu
- School of Electronics and Information Engineering, TianGong University, Tianjin, 300387, China
| | - Shuang Liu
- School of Electronics and Information Engineering, TianGong University, Tianjin, 300387, China
| | - Zhitao Xiao
- School of Life Sciences, TianGong University, Tianjin, 300387, China
| | - Fang Zhang
- School of Life Sciences, TianGong University, Tianjin, 300387, China
| | - Lei Geng
- School of Life Sciences, TianGong University, Tianjin, 300387, China
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End-to-End Multi-Task Learning Approaches for the Joint Epiretinal Membrane Segmentation and Screening in OCT Images. Comput Med Imaging Graph 2022; 98:102068. [DOI: 10.1016/j.compmedimag.2022.102068] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/19/2022] [Revised: 03/28/2022] [Accepted: 04/18/2022] [Indexed: 02/07/2023]
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Zhang Y, Yao J, Quan Y, Wang J, Xing Y, Zhou A. [Treatment response to Conbercept of different types of diabetic macular edema classified based on optical coherence tomography]. NAN FANG YI KE DA XUE XUE BAO = JOURNAL OF SOUTHERN MEDICAL UNIVERSITY 2021; 41:1501-1508. [PMID: 34755665 DOI: 10.12122/j.issn.1673-4254.2021.10.08] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Subscribe] [Scholar Register] [Indexed: 11/24/2022]
Abstract
OBJECTIVE To compare different types of diabetic macular edema (DME) classified based on optical coherence tomography (OCT) for their responses to Conbercept injection and analyze the factors that affect the treatment responses. METHODS We retrospectively analyzed the clinical data of 65 patients (76 eyes) with DME diagnosed and treated with intravitreal injection of Conbercept (1+PRN) in our hospital from February, 2019 to February, 2021. According to OCT findings, DME in these patients was classified into cystic macular edema (CME; 28 eyes), serous retinal detachment (SRD; 33 eyes), and diffuse retinal thickening (DRT; 15 eyes). The best corrected visual acuity (BCVA) and central retinal thickness (CRT) were measured before and at 3 months after the first treatment. The baseline OCT characteristics of different types of DME were compared, and the correlation of these OCT characteristics with the treatment response to Conbercept was analyzed. RESULTS All the patients showed significant improvement of the BCVA 3 months after the treatment (P < 0.05). For all the 3 types of DME, the CRT at 3 months after the first treatment was significantly reduced as compared to the baseline (P < 0.05). The number of hyperreflective foci (HF) in the outer retina and the proportion of ellipsoid zone (EZ) interruption were the greatest in SRD group (P < 0.05). The baseline outer retinal HF was significantly correlated with the baseline CRT, CRT changes and CRT after treatment (all P < 0.05). The patients with baseline outer limiting membrane (ELM)/ EZ disruption had poorer baseline BCVA, greater baseline CRT, greater variation of CRT and poorer BCVA at 3 months after treatment (all P < 0.05). CONCLUSION For all the 3 types of DME, treatment with intravitreal injection of Conbercept can significantly improve the BCVA and CRT of the patients. DME of the SRD type has the best morphological response to Conbercept, while the DRT type has a relatively poor response. A greater number of HF at baseline may indicate a better morphological response to Conbercept treatment, and baseline ELM/EZ disruption may suggest a poor visual prognosis at 3 months after treatment.
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Affiliation(s)
- Y Zhang
- Department of Ophthalmology, Second Affiliated Hospital of Xi'an Jiaotong University, Xi'an 710004, China
| | - J Yao
- Department of Ophthalmology, Second Affiliated Hospital of Xi'an Jiaotong University, Xi'an 710004, China
| | - Y Quan
- Department of Ophthalmology, Second Affiliated Hospital of Xi'an Jiaotong University, Xi'an 710004, China
| | - J Wang
- Department of Ophthalmology, Second Affiliated Hospital of Xi'an Jiaotong University, Xi'an 710004, China
| | - Y Xing
- Department of Ophthalmology, Second Affiliated Hospital of Xi'an Jiaotong University, Xi'an 710004, China
| | - A Zhou
- Department of Ophthalmology, Second Affiliated Hospital of Xi'an Jiaotong University, Xi'an 710004, China
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CDC-Net: Cascaded decoupled convolutional network for lesion-assisted detection and grading of retinopathy using optical coherence tomography (OCT) scans. Biomed Signal Process Control 2021. [DOI: 10.1016/j.bspc.2021.103030] [Citation(s) in RCA: 9] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/12/2023]
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Hassan B, Qin S, Ahmed R, Hassan T, Taguri AH, Hashmi S, Werghi N. Deep learning based joint segmentation and characterization of multi-class retinal fluid lesions on OCT scans for clinical use in anti-VEGF therapy. Comput Biol Med 2021; 136:104727. [PMID: 34385089 DOI: 10.1016/j.compbiomed.2021.104727] [Citation(s) in RCA: 23] [Impact Index Per Article: 7.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/19/2021] [Revised: 07/31/2021] [Accepted: 08/01/2021] [Indexed: 11/19/2022]
Abstract
BACKGROUND In anti-vascular endothelial growth factor (anti-VEGF) therapy, an accurate estimation of multi-class retinal fluid (MRF) is required for the activity prescription and intravitreal dose. This study proposes an end-to-end deep learning-based retinal fluids segmentation network (RFS-Net) to segment and recognize three MRF lesion manifestations, namely, intraretinal fluid (IRF), subretinal fluid (SRF), and pigment epithelial detachment (PED), from multi-vendor optical coherence tomography (OCT) imagery. The proposed image analysis tool will optimize anti-VEGF therapy and contribute to reducing the inter- and intra-observer variability. METHOD The proposed RFS-Net architecture integrates the atrous spatial pyramid pooling (ASPP), residual, and inception modules in the encoder path to learn better features and conserve more global information for precise segmentation and characterization of MRF lesions. The RFS-Net model is trained and validated using OCT scans from multiple vendors (Topcon, Cirrus, Spectralis), collected from three publicly available datasets. The first dataset consisted of OCT volumes obtained from 112 subjects (a total of 11,334 B-scans) is used for both training and evaluation purposes. Moreover, the remaining two datasets are only used for evaluation purposes to check the trained RFS-Net's generalizability on unseen OCT scans. The two evaluation datasets contain a total of 1572 OCT B-scans from 1255 subjects. The performance of the proposed RFS-Net model is assessed through various evaluation metrics. RESULTS The proposed RFS-Net model achieved the mean F1 scores of 0.762, 0.796, and 0.805 for segmenting IRF, SRF, and PED. Moreover, with the automated segmentation of the three retinal manifestations, the RFS-Net brings a considerable gain in efficiency compared to the tedious and demanding manual segmentation procedure of the MRF. CONCLUSIONS Our proposed RFS-Net is a potential diagnostic tool for the automatic segmentation of MRF (IRF, SRF, and PED) lesions. It is expected to strengthen the inter-observer agreement, and standardization of dosimetry is envisaged as a result.
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Affiliation(s)
- Bilal Hassan
- School of Automation Science and Electrical Engineering, Beihang University (BUAA), Beijing, 100191, China.
| | - Shiyin Qin
- School of Automation Science and Electrical Engineering, Beihang University (BUAA), Beijing, 100191, China; School of Electrical Engineering and Intelligentization, Dongguan University of Technology, Dongguan, 523808, China
| | - Ramsha Ahmed
- School of Computer and Communication Engineering, University of Science and Technology Beijing (USTB), Beijing, 100083, China
| | - Taimur Hassan
- Center for Cyber-Physical Systems, Khalifa University of Science and Technology, Abu Dhabi, 127788, United Arab Emirates
| | - Abdel Hakeem Taguri
- Abu Dhabi Healthcare Company (SEHA), Abu Dhabi, 127788, United Arab Emirates
| | - Shahrukh Hashmi
- Abu Dhabi Healthcare Company (SEHA), Abu Dhabi, 127788, United Arab Emirates
| | - Naoufel Werghi
- Center for Cyber-Physical Systems, Khalifa University of Science and Technology, Abu Dhabi, 127788, United Arab Emirates
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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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