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Braeu FA, Chuangsuwanich T, Tun TA, Perera SA, Husain R, Kadziauskienė A, Schmetterer L, Thiéry AH, Barbastathis G, Aung T, Girard MJA. Three-Dimensional Structural Phenotype of the Optic Nerve Head as a Function of Glaucoma Severity. JAMA Ophthalmol 2023; 141:882-889. [PMID: 37589980 PMCID: PMC10436184 DOI: 10.1001/jamaophthalmol.2023.3315] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/28/2023] [Accepted: 06/05/2023] [Indexed: 08/18/2023]
Abstract
Importance The 3-dimensional (3-D) structural phenotype of glaucoma as a function of severity was thoroughly described and analyzed, enhancing understanding of its intricate pathology beyond current clinical knowledge. Objective To describe the 3-D structural differences in both connective and neural tissues of the optic nerve head (ONH) between different glaucoma stages using traditional and artificial intelligence-driven approaches. Design, Setting, and Participants This cross-sectional, clinic-based study recruited 541 Chinese individuals receiving standard clinical care at Singapore National Eye Centre, Singapore, and 112 White participants of a prospective observational study at Vilnius University Hospital Santaros Klinikos, Vilnius, Lithuania. The study was conducted from May 2022 to January 2023. All participants had their ONH imaged using spectral-domain optical coherence tomography and had their visual field assessed by standard automated perimetry. Main Outcomes and Measures (1) Clinician-defined 3-D structural parameters of the ONH and (2) 3-D structural landmarks identified by geometric deep learning that differentiated ONHs among 4 groups: no glaucoma, mild glaucoma (mean deviation [MD], ≥-6.00 dB), moderate glaucoma (MD, -6.01 to -12.00 dB), and advanced glaucoma (MD, <-12.00 dB). Results Study participants included 213 individuals without glaucoma (mean age, 63.4 years; 95% CI, 62.5-64.3 years; 126 females [59.2%]; 213 Chinese [100%] and 0 White individuals), 204 with mild glaucoma (mean age, 66.9 years; 95% CI, 66.0-67.8 years; 91 females [44.6%]; 178 Chinese [87.3%] and 26 White [12.7%] individuals), 118 with moderate glaucoma (mean age, 68.1 years; 95% CI, 66.8-69.4 years; 49 females [41.5%]; 97 Chinese [82.2%] and 21 White [17.8%] individuals), and 118 with advanced glaucoma (mean age, 68.5 years; 95% CI, 67.1-69.9 years; 43 females [36.4%]; 53 Chinese [44.9%] and 65 White [55.1%] individuals). The majority of ONH structural differences occurred in the early glaucoma stage, followed by a plateau effect in the later stages. Using a deep neural network, 3-D ONH structural differences were found to be present in both neural and connective tissues. Specifically, a mean of 57.4% (95% CI, 54.9%-59.9%, for no to mild glaucoma), 38.7% (95% CI, 36.9%-40.5%, for mild to moderate glaucoma), and 53.1 (95% CI, 50.8%-55.4%, for moderate to advanced glaucoma) of ONH landmarks that showed major structural differences were located in neural tissues with the remaining located in connective tissues. Conclusions and Relevance This study uncovered complex 3-D structural differences of the ONH in both neural and connective tissues as a function of glaucoma severity. Future longitudinal studies should seek to establish a connection between specific 3-D ONH structural changes and fast visual field deterioration and aim to improve the early detection of patients with rapid visual field loss in routine clinical care.
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Affiliation(s)
- Fabian A. Braeu
- Ophthalmic Engineering & Innovation Laboratory, Singapore Eye Research Institute, Singapore National Eye Centre, Singapore
- Singapore–MIT Alliance for Research and Technology, Singapore
- Yong Loo Lin School of Medicine, National University of Singapore, Singapore
| | - Thanadet Chuangsuwanich
- Ophthalmic Engineering & Innovation Laboratory, Singapore Eye Research Institute, Singapore National Eye Centre, Singapore
- Yong Loo Lin School of Medicine, National University of Singapore, Singapore
| | - Tin A. Tun
- Singapore Eye Research Institute, Singapore National Eye Centre, Singapore
- Duke-NUS Graduate Medical School, Singapore
| | - Shamira A. Perera
- Singapore Eye Research Institute, Singapore National Eye Centre, Singapore
- Duke-NUS Graduate Medical School, Singapore
| | - Rahat Husain
- Singapore Eye Research Institute, Singapore National Eye Centre, Singapore
| | - Aiste Kadziauskienė
- Clinic of Ears, Nose, Throat and Eye Diseases, Institute of Clinical Medicine, Faculty of Medicine, Vilnius University, Vilnius, Lithuania
- Center of Eye Diseases, Vilnius University Hospital Santaros Klinikos, Vilnius, Lithuania
| | - Leopold Schmetterer
- Singapore Eye Research Institute, Singapore National Eye Centre, Singapore
- Duke-NUS Graduate Medical School, Singapore
- SERI-NTU Advanced Ocular Engineering (STANCE), Singapore
- School of Chemistry, Chemical Engineering and Biotechnology, Nanyang Technological University, Singapore
- Department of Clinical Pharmacology, Medical University of Vienna, Austria
- Center for Medical Physics and Biomedical Engineering, Medical University of Vienna, Austria
- Institute of Molecular and Clinical Ophthalmology, Basel, Switzerland
| | - Alexandre H. Thiéry
- Department of Statistics and Applied Probability, National University of Singapore, Singapore
| | - George Barbastathis
- Singapore–MIT Alliance for Research and Technology, Singapore
- Department of Mechanical Engineering, Massachusetts Institute of Technology, Cambridge
| | - Tin Aung
- Yong Loo Lin School of Medicine, National University of Singapore, Singapore
- Singapore Eye Research Institute, Singapore National Eye Centre, Singapore
- Duke-NUS Graduate Medical School, Singapore
| | - Michaël J. A. Girard
- Ophthalmic Engineering & Innovation Laboratory, Singapore Eye Research Institute, Singapore National Eye Centre, Singapore
- Duke-NUS Graduate Medical School, Singapore
- Institute of Molecular and Clinical Ophthalmology, Basel, Switzerland
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Abstract
Purpose (1) To assess the performance of geometric deep learning in diagnosing glaucoma from a single optical coherence tomography (OCT) scan of the optic nerve head and (2) to compare its performance to that obtained with a three-dimensional (3D) convolutional neural network (CNN), and with a gold-standard parameter, namely, the retinal nerve fiber layer (RNFL) thickness. Methods Scans of the optic nerve head were acquired with OCT for 477 glaucoma and 2296 nonglaucoma subjects. All volumes were automatically segmented using deep learning to identify seven major neural and connective tissues. Each optic nerve head was then represented as a 3D point cloud with approximately 1000 points. Geometric deep learning (PointNet) was then used to provide a glaucoma diagnosis from a single 3D point cloud. The performance of our approach (reported using the area under the curve [AUC]) was compared with that obtained with a 3D CNN, and with the RNFL thickness. Results PointNet was able to provide a robust glaucoma diagnosis solely from a 3D point cloud (AUC = 0.95 ± 0.01).The performance of PointNet was superior to that obtained with a 3D CNN (AUC = 0.87 ± 0.02 [raw OCT images] and 0.91 ± 0.02 [segmented OCT images]) and with that obtained from RNFL thickness alone (AUC = 0.80 ± 0.03). Conclusions We provide a proof of principle for the application of geometric deep learning in glaucoma. Our technique requires significantly less information as input to perform better than a 3D CNN, and with an AUC superior to that obtained from RNFL thickness. Translational Relevance Geometric deep learning may help us to improve and simplify diagnosis and prognosis applications in glaucoma.
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Affiliation(s)
- Alexandre H. Thiéry
- Department of Statistics and Data Science, National University of Singapore, Singapore
| | - Fabian Braeu
- Ophthalmic Engineering & Innovation Laboratory, Singapore Eye Research Institute, Singapore National Eye Centre, Singapore,Yong Loo Lin School of Medicine, National University of Singapore, Singapore
| | - Tin A. Tun
- Singapore Eye Research Institute, Singapore National Eye Centre, Singapore,Duke-NUS Graduate Medical School, Singapore
| | - Tin Aung
- Singapore Eye Research Institute, Singapore National Eye Centre, Singapore,Duke-NUS Graduate Medical School, Singapore
| | - Michaël J. A. Girard
- Ophthalmic Engineering & Innovation Laboratory, Singapore Eye Research Institute, Singapore National Eye Centre, Singapore,Duke-NUS Graduate Medical School, Singapore,Institute for Molecular and Clinical Ophthalmology, Basel, Switzerland
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Braeu FA, Thiéry AH, Tun TA, Kadziauskiene A, Barbastathis G, Aung T, Girard MJA. Geometric Deep Learning to Identify the Critical 3D Structural Features of the Optic Nerve Head for Glaucoma Diagnosis. Am J Ophthalmol 2023; 250:38-48. [PMID: 36646242 DOI: 10.1016/j.ajo.2023.01.008] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/24/2022] [Revised: 01/07/2023] [Accepted: 01/09/2023] [Indexed: 01/15/2023]
Abstract
PURPOSE To compare the performance of 2 relatively recent geometric deep learning techniques in diagnosing glaucoma from a single optical coherence tomographic (OCT) scan of the optic nerve head (ONH); and to identify the 3-dimensional (3D) structural features of the ONH that are critical for the diagnosis of glaucoma. DESIGN Comparison and evaluation of deep learning diagnostic algorithms. METHODS In this study, we included a total of 2247 nonglaucoma and 2259 glaucoma scans from 1725 participants. All participants had their ONHs imaged in 3D with Spectralis OCT. All OCT scans were automatically segmented using deep learning to identify major neural and connective tissues. Each ONH was then represented as a 3D point cloud. We used PointNet and dynamic graph convolutional neural network (DGCNN) to diagnose glaucoma from such 3D ONH point clouds and to identify the critical 3D structural features of the ONH for glaucoma diagnosis. RESULTS Both the DGCNN (area under the curve [AUC]: 0.97±0.01) and PointNet (AUC: 0.95±0.02) were able to accurately detect glaucoma from 3D ONH point clouds. The critical points (ie, critical structural features of the ONH) formed an hourglass pattern, with most of them located within the neuroretinal rim in the inferior and superior quadrant of the ONH. CONCLUSIONS The diagnostic accuracy of both geometric deep learning approaches was excellent. Moreover, we were able to identify the critical 3D structural features of the ONH for glaucoma diagnosis that tremendously improved the transparency and interpretability of our method. Consequently, our approach may have strong potential to be used in clinical applications for the diagnosis and prognosis of a wide range of ophthalmic disorders.
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Affiliation(s)
- Fabian A Braeu
- From the Ophthalmic Engineering & Innovation Laboratory, Singapore Eye Research Institute, Singapore National Eye Centre (F.A.B., M.J.A.G.), Singapore; Singapore-MIT Alliance for Research and Technology (F.A.B., G.B.), Singapore; Yong Loo Lin School of Medicine, National University of Singapore (F.A.B., T.A.), Singapore
| | - Alexandre H Thiéry
- Department of Statistics and Applied Probability, National University of Singapore (A.H.T.), Singapore
| | - Tin A Tun
- Singapore Eye Research Institute, Singapore National Eye Centre (T.A.T., T.A.), Singapore; Duke-NUS Graduate Medical School (T.A.T., T.A., M.J.A.G.), Singapore
| | - Aiste Kadziauskiene
- Clinic of Ears, Nose, Throat and Eye Diseases, Institute of Clinical Medicine, Faculty of Medicine, Vilnius University (A.K.), Vilnius, Lithuania; Center of Eye diseases, Vilnius University Hospital Santaros Klinikos (A.K.), Vilnius, Lithuania
| | - George Barbastathis
- Singapore-MIT Alliance for Research and Technology (F.A.B., G.B.), Singapore; Department of Mechanical Engineering, Massachusetts Institute of Technology (G.B.), Cambridge, Massachusetts, USA
| | - Tin Aung
- Yong Loo Lin School of Medicine, National University of Singapore (F.A.B., T.A.), Singapore; Singapore Eye Research Institute, Singapore National Eye Centre (T.A.T., T.A.), Singapore; Duke-NUS Graduate Medical School (T.A.T., T.A., M.J.A.G.), Singapore
| | - Michaël J A Girard
- From the Ophthalmic Engineering & Innovation Laboratory, Singapore Eye Research Institute, Singapore National Eye Centre (F.A.B., M.J.A.G.), Singapore; Duke-NUS Graduate Medical School (T.A.T., T.A., M.J.A.G.), Singapore; Institute for Molecular and Clinical Ophthalmology (M.J.A.G.), Basel, Switzerland.
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Girard MJA, Panda S, Tun TA, Wibroe EA, Najjar RP, Aung T, Thiéry AH, Hamann S, Fraser C, Milea D. Discriminating Between Papilledema and Optic Disc Drusen Using 3D Structural Analysis of the Optic Nerve Head. Neurology 2023; 100:e192-e202. [PMID: 36175153 DOI: 10.1212/wnl.0000000000201350] [Citation(s) in RCA: 6] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/04/2022] [Accepted: 08/19/2022] [Indexed: 02/05/2023] Open
Abstract
BACKGROUND AND OBJECTIVES The distinction of papilledema from other optic nerve head (ONH) lesions mimicking papilledema, such as optic disc drusen (ODD), can be difficult in clinical practice. We aimed the following: (1) to develop a deep learning algorithm to automatically identify major structures of the ONH in 3-dimensional (3D) optical coherence tomography (OCT) scans and (2) to exploit such information to robustly differentiate among ODD, papilledema, and healthy ONHs. METHODS This was a cross-sectional comparative study of patients from 3 sites (Singapore, Denmark, and Australia) with confirmed ODD, those with papilledema due to raised intracranial pressure, and healthy controls. Raster scans of the ONH were acquired using OCT imaging and then processed to improve deep-tissue visibility. First, a deep learning algorithm was developed to identify major ONH tissues and ODD regions. The performance of our algorithm was assessed using the Dice coefficient. Second, a classification algorithm (random forest) was designed to perform 3-class classifications (1: ODD, 2: papilledema, and 3: healthy ONHs) strictly from their drusen and prelamina swelling scores (calculated from the segmentations). To assess performance, we reported the area under the receiver operating characteristic curve for each class. RESULTS A total of 241 patients (256 imaged ONHs, including 105 ODD, 51 papilledema, and 100 healthy ONHs) were retrospectively included in this study. Using OCT images of the ONH, our segmentation algorithm was able to isolate neural and connective tissues and ODD regions/conglomerates whenever present. This was confirmed by an averaged Dice coefficient of 0.93 ± 0.03 on the test set, corresponding to good segmentation performance. Classification was achieved with high AUCs, that is, 0.99 ± 0.001 for the detection of ODD, 0.99 ± 0.005 for the detection of papilledema, and 0.98 ± 0.01 for the detection of healthy ONHs. DISCUSSION Our artificial intelligence approach can discriminate ODD from papilledema, strictly using a single OCT scan of the ONH. Our classification performance was very good in the studied population, with the caveat that validation in a much larger population is warranted. Our approach may have the potential to establish OCT imaging as one of the mainstays of diagnostic imaging for ONH disorders in neuro-ophthalmology, in addition to fundus photography.
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Affiliation(s)
- Michaël J A Girard
- From the Ophthalmic Engineering & Innovation Laboratory (M.J.A.G., S.P.), Singapore Eye Research Institute (T.A.T., R.P.N., T.A., D.M.), Singapore National Eye Centre; Duke-NUS Graduate Medical School (M.J.A.G., T.A.T., R.P.N., T.A., D.M.), Singapore; Institute for Molecular and Clinical Ophthalmology (M.J.A.G.), Basel, Switzerland; Department of Ophthalmology (E.A.W., S.H.), Rigshospitalet, University of Copenhagen, Denmark; Yong Loo Lin School of Medicine (R.P.N., T.A.), and Department of Statistics and Applied Probability (A.H.T.), National University of Singapore; and Save Sight Institute (C.F.), Faculty of Health and Medicine, The University of Sydney, New South Wales, Australia.
| | - Satish Panda
- From the Ophthalmic Engineering & Innovation Laboratory (M.J.A.G., S.P.), Singapore Eye Research Institute (T.A.T., R.P.N., T.A., D.M.), Singapore National Eye Centre; Duke-NUS Graduate Medical School (M.J.A.G., T.A.T., R.P.N., T.A., D.M.), Singapore; Institute for Molecular and Clinical Ophthalmology (M.J.A.G.), Basel, Switzerland; Department of Ophthalmology (E.A.W., S.H.), Rigshospitalet, University of Copenhagen, Denmark; Yong Loo Lin School of Medicine (R.P.N., T.A.), and Department of Statistics and Applied Probability (A.H.T.), National University of Singapore; and Save Sight Institute (C.F.), Faculty of Health and Medicine, The University of Sydney, New South Wales, Australia
| | - Tin Aung Tun
- From the Ophthalmic Engineering & Innovation Laboratory (M.J.A.G., S.P.), Singapore Eye Research Institute (T.A.T., R.P.N., T.A., D.M.), Singapore National Eye Centre; Duke-NUS Graduate Medical School (M.J.A.G., T.A.T., R.P.N., T.A., D.M.), Singapore; Institute for Molecular and Clinical Ophthalmology (M.J.A.G.), Basel, Switzerland; Department of Ophthalmology (E.A.W., S.H.), Rigshospitalet, University of Copenhagen, Denmark; Yong Loo Lin School of Medicine (R.P.N., T.A.), and Department of Statistics and Applied Probability (A.H.T.), National University of Singapore; and Save Sight Institute (C.F.), Faculty of Health and Medicine, The University of Sydney, New South Wales, Australia
| | - Elisabeth A Wibroe
- From the Ophthalmic Engineering & Innovation Laboratory (M.J.A.G., S.P.), Singapore Eye Research Institute (T.A.T., R.P.N., T.A., D.M.), Singapore National Eye Centre; Duke-NUS Graduate Medical School (M.J.A.G., T.A.T., R.P.N., T.A., D.M.), Singapore; Institute for Molecular and Clinical Ophthalmology (M.J.A.G.), Basel, Switzerland; Department of Ophthalmology (E.A.W., S.H.), Rigshospitalet, University of Copenhagen, Denmark; Yong Loo Lin School of Medicine (R.P.N., T.A.), and Department of Statistics and Applied Probability (A.H.T.), National University of Singapore; and Save Sight Institute (C.F.), Faculty of Health and Medicine, The University of Sydney, New South Wales, Australia
| | - Raymond P Najjar
- From the Ophthalmic Engineering & Innovation Laboratory (M.J.A.G., S.P.), Singapore Eye Research Institute (T.A.T., R.P.N., T.A., D.M.), Singapore National Eye Centre; Duke-NUS Graduate Medical School (M.J.A.G., T.A.T., R.P.N., T.A., D.M.), Singapore; Institute for Molecular and Clinical Ophthalmology (M.J.A.G.), Basel, Switzerland; Department of Ophthalmology (E.A.W., S.H.), Rigshospitalet, University of Copenhagen, Denmark; Yong Loo Lin School of Medicine (R.P.N., T.A.), and Department of Statistics and Applied Probability (A.H.T.), National University of Singapore; and Save Sight Institute (C.F.), Faculty of Health and Medicine, The University of Sydney, New South Wales, Australia
| | - Tin Aung
- From the Ophthalmic Engineering & Innovation Laboratory (M.J.A.G., S.P.), Singapore Eye Research Institute (T.A.T., R.P.N., T.A., D.M.), Singapore National Eye Centre; Duke-NUS Graduate Medical School (M.J.A.G., T.A.T., R.P.N., T.A., D.M.), Singapore; Institute for Molecular and Clinical Ophthalmology (M.J.A.G.), Basel, Switzerland; Department of Ophthalmology (E.A.W., S.H.), Rigshospitalet, University of Copenhagen, Denmark; Yong Loo Lin School of Medicine (R.P.N., T.A.), and Department of Statistics and Applied Probability (A.H.T.), National University of Singapore; and Save Sight Institute (C.F.), Faculty of Health and Medicine, The University of Sydney, New South Wales, Australia
| | - Alexandre H Thiéry
- From the Ophthalmic Engineering & Innovation Laboratory (M.J.A.G., S.P.), Singapore Eye Research Institute (T.A.T., R.P.N., T.A., D.M.), Singapore National Eye Centre; Duke-NUS Graduate Medical School (M.J.A.G., T.A.T., R.P.N., T.A., D.M.), Singapore; Institute for Molecular and Clinical Ophthalmology (M.J.A.G.), Basel, Switzerland; Department of Ophthalmology (E.A.W., S.H.), Rigshospitalet, University of Copenhagen, Denmark; Yong Loo Lin School of Medicine (R.P.N., T.A.), and Department of Statistics and Applied Probability (A.H.T.), National University of Singapore; and Save Sight Institute (C.F.), Faculty of Health and Medicine, The University of Sydney, New South Wales, Australia
| | - Steffen Hamann
- From the Ophthalmic Engineering & Innovation Laboratory (M.J.A.G., S.P.), Singapore Eye Research Institute (T.A.T., R.P.N., T.A., D.M.), Singapore National Eye Centre; Duke-NUS Graduate Medical School (M.J.A.G., T.A.T., R.P.N., T.A., D.M.), Singapore; Institute for Molecular and Clinical Ophthalmology (M.J.A.G.), Basel, Switzerland; Department of Ophthalmology (E.A.W., S.H.), Rigshospitalet, University of Copenhagen, Denmark; Yong Loo Lin School of Medicine (R.P.N., T.A.), and Department of Statistics and Applied Probability (A.H.T.), National University of Singapore; and Save Sight Institute (C.F.), Faculty of Health and Medicine, The University of Sydney, New South Wales, Australia
| | - Clare Fraser
- From the Ophthalmic Engineering & Innovation Laboratory (M.J.A.G., S.P.), Singapore Eye Research Institute (T.A.T., R.P.N., T.A., D.M.), Singapore National Eye Centre; Duke-NUS Graduate Medical School (M.J.A.G., T.A.T., R.P.N., T.A., D.M.), Singapore; Institute for Molecular and Clinical Ophthalmology (M.J.A.G.), Basel, Switzerland; Department of Ophthalmology (E.A.W., S.H.), Rigshospitalet, University of Copenhagen, Denmark; Yong Loo Lin School of Medicine (R.P.N., T.A.), and Department of Statistics and Applied Probability (A.H.T.), National University of Singapore; and Save Sight Institute (C.F.), Faculty of Health and Medicine, The University of Sydney, New South Wales, Australia
| | - Dan Milea
- From the Ophthalmic Engineering & Innovation Laboratory (M.J.A.G., S.P.), Singapore Eye Research Institute (T.A.T., R.P.N., T.A., D.M.), Singapore National Eye Centre; Duke-NUS Graduate Medical School (M.J.A.G., T.A.T., R.P.N., T.A., D.M.), Singapore; Institute for Molecular and Clinical Ophthalmology (M.J.A.G.), Basel, Switzerland; Department of Ophthalmology (E.A.W., S.H.), Rigshospitalet, University of Copenhagen, Denmark; Yong Loo Lin School of Medicine (R.P.N., T.A.), and Department of Statistics and Applied Probability (A.H.T.), National University of Singapore; and Save Sight Institute (C.F.), Faculty of Health and Medicine, The University of Sydney, New South Wales, Australia
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Panda SK, Cheong H, Tun TA, Devella SK, Senthil V, Krishnadas R, Buist ML, Perera S, Cheng CY, Aung T, Thiéry AH, Girard MJ. Describing the Structural Phenotype of the Glaucomatous Optic Nerve Head Using Artificial Intelligence. Am J Ophthalmol 2022; 236:172-182. [PMID: 34157276 DOI: 10.1016/j.ajo.2021.06.010] [Citation(s) in RCA: 12] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/15/2021] [Revised: 06/10/2021] [Accepted: 06/11/2021] [Indexed: 02/08/2023]
Abstract
PURPOSE To develop a novel deep-learning approach that can describe the structural phenotype of the glaucomatous optic nerve head (ONH) and can be used as a robust glaucoma diagnosis tool. DESIGN Retrospective, deep-learning approach diagnosis study. METHOD We trained a deep-learning network to segment 3 neural-tissue and 4 connective-tissue layers of the ONH. The segmented optical coherence tomography images were then processed by a customized autoencoder network with an additional parallel branch for binary classification. The encoder part of the autoencoder reduced the segmented optical coherence tomography images into a low-dimensional latent space (LS), whereas the decoder and the classification branches reconstructed the images and classified them as glaucoma or nonglaucoma, respectively. We performed principal component analysis on the latent parameters and identified the principal components (PCs). Subsequently, the magnitude of each PC was altered in steps and reported how it impacted the morphology of the ONH. RESULTS The image reconstruction quality and diagnostic accuracy increased with the size of the LS. With 54 parameters in the LS, the diagnostic accuracy was 92.0 ± 2.3% with a sensitivity of 90.0 ± 2.4% (at 95% specificity), and the corresponding Dice coefficient for the reconstructed images was 0.86 ± 0.04. By changing the magnitudes of PC in steps, we were able to reveal how the morphology of the ONH changes as one transitions from a "nonglaucoma" to a "glaucoma" condition. CONCLUSIONS Our network was able to identify novel biomarkers of the ONH for glaucoma diagnosis. Specifically, the structural features identified by our algorithm were found to be related to clinical observations of glaucoma.
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Cheong H, Krishna Devalla S, Chuangsuwanich T, Tun TA, Wang X, Aung T, Schmetterer L, Buist ML, Boote C, Thiéry AH, Girard MJA. OCT-GAN: single step shadow and noise removal from optical coherence tomography images of the human optic nerve head. Biomed Opt Express 2021; 12:1482-1498. [PMID: 33796367 PMCID: PMC7984803 DOI: 10.1364/boe.412156] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Received: 10/22/2020] [Revised: 01/12/2021] [Accepted: 01/15/2021] [Indexed: 06/12/2023]
Abstract
Speckle noise and retinal shadows within OCT B-scans occlude important edges, fine textures and deep tissues, preventing accurate and robust diagnosis by algorithms and clinicians. We developed a single process that successfully removed both noise and retinal shadows from unseen single-frame B-scans within 10.4ms. Mean average gradient magnitude (AGM) for the proposed algorithm was 57.2% higher than current state-of-the-art, while mean peak signal to noise ratio (PSNR), contrast to noise ratio (CNR), and structural similarity index metric (SSIM) increased by 11.1%, 154% and 187% respectively compared to single-frame B-scans. Mean intralayer contrast (ILC) improvement for the retinal nerve fiber layer (RNFL), photoreceptor layer (PR) and retinal pigment epithelium (RPE) layers decreased from 0.362 ± 0.133 to 0.142 ± 0.102, 0.449 ± 0.116 to 0.0904 ± 0.0769, 0.381 ± 0.100 to 0.0590 ± 0.0451 respectively. The proposed algorithm reduces the necessity for long image acquisition times, minimizes expensive hardware requirements and reduces motion artifacts in OCT images.
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Affiliation(s)
- Haris Cheong
- Ophthalmic Engineering and Innovation Laboratory (OEIL), Singapore Eye Research Institute, Singapore National Eye Centre, Singapore
- Department of Biomedical Engineering, Faculty of Engineering, National University of Singapore, Singapore
| | - Sripad Krishna Devalla
- Ophthalmic Engineering and Innovation Laboratory (OEIL), Singapore Eye Research Institute, Singapore National Eye Centre, Singapore
| | - Thanadet Chuangsuwanich
- Ophthalmic Engineering and Innovation Laboratory (OEIL), Singapore Eye Research Institute, Singapore National Eye Centre, Singapore
- Department of Biomedical Engineering, Faculty of Engineering, National University of Singapore, Singapore
| | - Tin A. Tun
- Singapore Eye Research Institute, Singapore National Eye Centre, Singapore
| | - Xiaofei Wang
- Beijing Advanced Innovation Center for Biomedical Engineering, School of Biological Science and Medical Engineering, Beihang University, Beijing, China
| | - Tin Aung
- Ophthalmic Engineering and Innovation Laboratory (OEIL), Singapore Eye Research Institute, Singapore National Eye Centre, Singapore
- Singapore Eye Research Institute, Singapore National Eye Centre, Singapore
| | - Leopold Schmetterer
- Singapore Eye Research Institute, Singapore National Eye Centre, Singapore
- Yong Loo Lin School of Medicine, National University of Singapore, Singapore
- School of Clinical and Biomedical Engineering, Nanyang Technological University, Singapore, Singapore
- SERI-NTU Advanced Ocular Engineering (STANCE), Singapore, Singapore
- Center for Medical Physics and Biomedical Engineering, Medical University Vienna, Vienna, Austria
- Department of Clinical Pharmacology, Medical University of Vienna, Vienna, Austria
- Institute of Molecular and Clinical Ophthalmology, Basel, Switzerland
| | - Martin L. Buist
- Department of Biomedical Engineering, Faculty of Engineering, National University of Singapore, Singapore
| | - Craig Boote
- Ophthalmic Engineering and Innovation Laboratory (OEIL), Singapore Eye Research Institute, Singapore National Eye Centre, Singapore
- Structural Biophysics Group, School of Optometry and Vision Sciences, Cardiff University, UK
| | - Alexandre H. Thiéry
- Department of Statistics and Applied Probability, National University of Singapore, Singapore, Singapore
| | - Michaël J. A. Girard
- Ophthalmic Engineering and Innovation Laboratory (OEIL), Singapore Eye Research Institute, Singapore National Eye Centre, Singapore
- Singapore Eye Research Institute, Singapore National Eye Centre, Singapore
- Duke-NUS Medical School, Singapore
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Devalla SK, Pham TH, Panda SK, Zhang L, Subramanian G, Swaminathan A, Yun CZ, Rajan M, Mohan S, Krishnadas R, Senthil V, De Leon JMS, Tun TA, Cheng CY, Schmetterer L, Perera S, Aung T, Thiéry AH, Girard MJA. Towards label-free 3D segmentation of optical coherence tomography images of the optic nerve head using deep learning. Biomed Opt Express 2020; 11:6356-6378. [PMID: 33282495 PMCID: PMC7687952 DOI: 10.1364/boe.395934] [Citation(s) in RCA: 21] [Impact Index Per Article: 5.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Received: 05/07/2020] [Revised: 08/17/2020] [Accepted: 08/19/2020] [Indexed: 05/06/2023]
Abstract
Recently proposed deep learning (DL) algorithms for the segmentation of optical coherence tomography (OCT) images to quantify the morphological changes to the optic nerve head (ONH) tissues during glaucoma have limited clinical adoption due to their device specific nature and the difficulty in preparing manual segmentations (training data). We propose a DL-based 3D segmentation framework that is easily translatable across OCT devices in a label-free manner (i.e. without the need to manually re-segment data for each device). Specifically, we developed 2 sets of DL networks: the 'enhancer' (enhance OCT image quality and harmonize image characteristics from 3 devices) and the 'ONH-Net' (3D segmentation of 6 ONH tissues). We found that only when the 'enhancer' was used to preprocess the OCT images, the 'ONH-Net' trained on any of the 3 devices successfully segmented ONH tissues from the other two unseen devices with high performance (Dice coefficients > 0.92). We demonstrate that is possible to automatically segment OCT images from new devices without ever needing manual segmentation data from them.
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Affiliation(s)
- Sripad Krishna Devalla
- Ophthalmic Engineering & Innovation Laboratory, Department of Biomedical Engineering, Faculty of Engineering, National University of Singapore, Singapore
| | - Tan Hung Pham
- Ophthalmic Engineering & Innovation Laboratory, Department of Biomedical Engineering, Faculty of Engineering, National University of Singapore, Singapore
- Singapore Eye Research Institute, Singapore National Eye Centre, Singapore
| | - Satish Kumar Panda
- Ophthalmic Engineering & Innovation Laboratory, Department of Biomedical Engineering, Faculty of Engineering, National University of Singapore, Singapore
| | - Liang Zhang
- Ophthalmic Engineering & Innovation Laboratory, Department of Biomedical Engineering, Faculty of Engineering, National University of Singapore, Singapore
| | - Giridhar Subramanian
- Ophthalmic Engineering & Innovation Laboratory, Department of Biomedical Engineering, Faculty of Engineering, National University of Singapore, Singapore
| | - Anirudh Swaminathan
- Ophthalmic Engineering & Innovation Laboratory, Department of Biomedical Engineering, Faculty of Engineering, National University of Singapore, Singapore
| | - Chin Zhi Yun
- Ophthalmic Engineering & Innovation Laboratory, Department of Biomedical Engineering, Faculty of Engineering, National University of Singapore, Singapore
| | | | | | | | | | - John Mark S De Leon
- Department of Health Eye Center, East Avenue Medical Center, Quezon City, Philippines
| | - Tin A Tun
- Ophthalmic Engineering & Innovation Laboratory, Department of Biomedical Engineering, Faculty of Engineering, National University of Singapore, Singapore
- Singapore Eye Research Institute, Singapore National Eye Centre, Singapore
| | - Ching-Yu Cheng
- Singapore Eye Research Institute, Singapore National Eye Centre, Singapore
- Ophthalmology & Visual Sciences Academic Clinical Program (Eye ACP), Duke-NUS Medical School, Singapore
| | - Leopold Schmetterer
- Singapore Eye Research Institute, Singapore National Eye Centre, Singapore
- Nanyang Technological University, Singapore
- Department of Clinical Pharmacology, Medical University of Vienna, Austria
- Center for Medical Physics and Biomedical Engineering, Medical University of Vienna, Austria
- Institute of Clinical and Molecular Ophthalmology, Basel, Switzerland
| | - Shamira Perera
- Singapore Eye Research Institute, Singapore National Eye Centre, Singapore
- Duke-NUS Graduate Medical School, 8 College Rd, Singapore 169857, Singapore
| | - Tin Aung
- Singapore Eye Research Institute, Singapore National Eye Centre, Singapore
- Duke-NUS Graduate Medical School, 8 College Rd, Singapore 169857, Singapore
| | - Alexandre H Thiéry
- Department of Statistics and Applied Probability, National University of Singapore, Singapore
| | - Michaël J A Girard
- Ophthalmic Engineering and Innovation Laboratory (OEIL), Singapore Eye Research Institute, 20 College Road, Singapore 169856, Singapore
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Devalla SK, Renukanand PK, Sreedhar BK, Subramanian G, Zhang L, Perera S, Mari JM, Chin KS, Tun TA, Strouthidis NG, Aung T, Thiéry AH, Girard MJA. DRUNET: a dilated-residual U-Net deep learning network to segment optic nerve head tissues in optical coherence tomography images. Biomed Opt Express 2018; 9:3244-3265. [PMID: 29984096 PMCID: PMC6033560 DOI: 10.1364/boe.9.003244] [Citation(s) in RCA: 90] [Impact Index Per Article: 15.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 05/29/2018] [Revised: 06/06/2018] [Accepted: 06/11/2018] [Indexed: 05/18/2023]
Abstract
Given that the neural and connective tissues of the optic nerve head (ONH) exhibit complex morphological changes with the development and progression of glaucoma, their simultaneous isolation from optical coherence tomography (OCT) images may be of great interest for the clinical diagnosis and management of this pathology. A deep learning algorithm (custom U-NET) was designed and trained to segment 6 ONH tissue layers by capturing both the local (tissue texture) and contextual information (spatial arrangement of tissues). The overall Dice coefficient (mean of all tissues) was 0.91 ± 0.05 when assessed against manual segmentations performed by an expert observer. Further, we automatically extracted six clinically relevant neural and connective tissue structural parameters from the segmented tissues. We offer here a robust segmentation framework that could also be extended to the 3D segmentation of the ONH tissues.
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Affiliation(s)
- Sripad Krishna Devalla
- Ophthalmic Engineering & Innovation Laboratory, Department of Biomedical Engineering, Faculty of Engineering, National University of Singapore, Singapore
| | - Prajwal K Renukanand
- Ophthalmic Engineering & Innovation Laboratory, Department of Biomedical Engineering, Faculty of Engineering, National University of Singapore, Singapore
| | - Bharathwaj K Sreedhar
- Ophthalmic Engineering & Innovation Laboratory, Department of Biomedical Engineering, Faculty of Engineering, National University of Singapore, Singapore
| | - Giridhar Subramanian
- Ophthalmic Engineering & Innovation Laboratory, Department of Biomedical Engineering, Faculty of Engineering, National University of Singapore, Singapore
| | - Liang Zhang
- Ophthalmic Engineering & Innovation Laboratory, Department of Biomedical Engineering, Faculty of Engineering, National University of Singapore, Singapore
| | - Shamira Perera
- Duke-NUS, Graduate Medical School, Singapore
- Singapore Eye Research Institute, Singapore National Eye Centre, Singapore
| | - Jean-Martial Mari
- GePaSud, Université de la Polynésie française, Tahiti, French Polynesia
| | - Khai Sing Chin
- Department of Statistics and Applied Probability, National University of Singapore, Singapore
| | - Tin A Tun
- Ophthalmic Engineering & Innovation Laboratory, Department of Biomedical Engineering, Faculty of Engineering, National University of Singapore, Singapore
- Singapore Eye Research Institute, Singapore National Eye Centre, Singapore
| | - Nicholas G Strouthidis
- Singapore Eye Research Institute, Singapore National Eye Centre, Singapore
- NIHR Biomedical Research Centre at Moorfields Eye Hospital NHS Foundation Trust and UCL Institute of Ophthalmology, London, UK
- Discipline of Clinical Ophthalmology and Eye Health, University of Sydney, Sydney, New South Wales, Australia
| | - Tin Aung
- Singapore Eye Research Institute, Singapore National Eye Centre, Singapore
| | - Alexandre H Thiéry
- Department of Statistics and Applied Probability, National University of Singapore, Singapore
| | - Michaël J A Girard
- Ophthalmic Engineering & Innovation Laboratory, Department of Biomedical Engineering, Faculty of Engineering, National University of Singapore, Singapore
- Singapore Eye Research Institute, Singapore National Eye Centre, Singapore
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Devalla SK, Chin KS, Mari JM, Tun TA, Strouthidis NG, Aung T, Thiéry AH, Girard MJA. A Deep Learning Approach to Digitally Stain Optical Coherence Tomography Images of the Optic Nerve Head. ACTA ACUST UNITED AC 2018; 59:63-74. [DOI: 10.1167/iovs.17-22617] [Citation(s) in RCA: 64] [Impact Index Per Article: 10.7] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/24/2022]
Affiliation(s)
- Sripad Krishna Devalla
- Ophthalmic Engineering and Innovation Laboratory, Department of Biomedical Engineering, Faculty of Engineering, National University of Singapore, Singapore
| | - Khai Sing Chin
- Department of Statistics and Applied Probability, National University of Singapore, Singapore
| | - Jean-Martial Mari
- GePaSud, Université de la Polynésie Française, Tahiti, French Polynesia
| | - Tin A. Tun
- Singapore Eye Research Institute, Singapore National Eye Centre, Singapore
| | - Nicholas G. Strouthidis
- Singapore Eye Research Institute, Singapore National Eye Centre, Singapore
- NIHR Biomedical Research Centre at Moorfields Eye Hospital NHS Foundation Trust and UCL Institute of Ophthalmology, London, United Kingdom
- Discipline of Clinical Ophthalmology and Eye Health, University of Sydney, Sydney, New South Wales, Australia
| | - Tin Aung
- Singapore Eye Research Institute, Singapore National Eye Centre, Singapore
- Yong Loo Lin School of Medicine, National University of Singapore, Singapore
| | - Alexandre H. Thiéry
- Department of Statistics and Applied Probability, National University of Singapore, Singapore
| | - Michaël J. A. Girard
- Ophthalmic Engineering and Innovation Laboratory, Department of Biomedical Engineering, Faculty of Engineering, National University of Singapore, Singapore
- Singapore Eye Research Institute, Singapore National Eye Centre, Singapore
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