Validation and Clinical Applicability of Whole-Volume Automated Segmentation of Optical Coherence Tomography in Retinal Disease Using Deep Learning

Marc Wilson; Reena Chopra; Megan Z. Wilson; Charlotte Cooper; Patricia MacWilliams; Yun Liu; Ellery Wulczyn; Daniela Florea; Cían O. Hughes; Alan Karthikesalingam; Hagar Khalid; Sandra Vermeirsch; Luke Nicholson; Pearse A. Keane; Konstantinos Balaskas; Christopher J. Kelly

doi:10.1001/jamaophthalmol.2021.2273

This diagnostic study evaluates a deep learning model for whole-volume segmentation of 4 clinically important pathological features as well as clinical applicability.

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References (82)

M. Wintergerst, T. Schultz, J. Birtel, A. Schuster, N. Pfeiffer, S. Schmitz-Valckenberg, F. Holz, R. Finger (2017)
Algorithms for the Automated Analysis of Age-Related Macular Degeneration Biomarkers on Optical Coherence Tomography: A Systematic Review
Translational Vision Science & Technology, 6
J. Schindelin, Ignacio Arganda-Carreras, E. Frise, V. Kaynig, M. Longair, T. Pietzsch, S. Preibisch, C. Rueden, S. Saalfeld, Benjamin Schmid, J. Tinevez, D. White, V. Hartenstein, K. Eliceiri, P. Tomančák, A. Cardona (2012)
Fiji: an open-source platform for biological-image analysis
Nature Methods, 9
G. Ying, Jiayan Huang, M. Maguire, G. Jaffe, J. Grunwald, C. Toth, Ebenezer Daniel, M. Klein, D. Pieramici, J. Wells, Daniel Martin (2013)
Baseline predictors for one-year visual outcomes with ranibizumab or bevacizumab for neovascular age-related macular degeneration.
Ophthalmology, 120 1
O. Ronneberger, P. Fischer, T. Brox (2015)
U-Net: Convolutional Networks for Biomedical Image Segmentation
ArXiv, abs/1505.04597
(Wilson EB. Probable inference, the law of succession, and statistical inference. J Am Stat Assoc. 1927;22(158):209-212. doi:10.1080/01621459.1927.10502953 )
Wilson EB. Probable inference, the law of succession, and statistical inference. J Am Stat Assoc. 1927;22(158):209-212. doi:10.1080/01621459.1927.10502953
Wilson EB. Probable inference, the law of succession, and statistical inference. J Am Stat Assoc. 1927;22(158):209-212. doi:10.1080/01621459.1927.10502953 , Wilson EB. Probable inference, the law of succession, and statistical inference. J Am Stat Assoc. 1927;22(158):209-212. doi:10.1080/01621459.1927.10502953
(Pokroy R, Mimouni M, Barayev E, . Prognostic value of subretinal hyperreflective material in neovascular age-related macular degeneration treated with bevacizumab. Retina. 2018;38(8):1485-1491. doi:10.1097/IAE.0000000000001748 28654630)
Pokroy R, Mimouni M, Barayev E, . Prognostic value of subretinal hyperreflective material in neovascular age-related macular degeneration treated with bevacizumab. Retina. 2018;38(8):1485-1491. doi:10.1097/IAE.0000000000001748 28654630
Pokroy R, Mimouni M, Barayev E, . Prognostic value of subretinal hyperreflective material in neovascular age-related macular degeneration treated with bevacizumab. Retina. 2018;38(8):1485-1491. doi:10.1097/IAE.0000000000001748 28654630, Pokroy R, Mimouni M, Barayev E, . Prognostic value of subretinal hyperreflective material in neovascular age-related macular degeneration treated with bevacizumab. Retina. 2018;38(8):1485-1491. doi:10.1097/IAE.0000000000001748 28654630
(Lu D, Heisler M, Lee S, . 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 30856455)
Lu D, Heisler M, Lee S, . 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 30856455
Lu D, Heisler M, Lee S, . 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 30856455, Lu D, Heisler M, Lee S, . 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 30856455
(Gurney-Champion OJ, Mahmood F, van Schie M, . Quantitative imaging for radiotherapy purposes. Radiother Oncol. 2020;146:66-75. doi:10.1016/j.radonc.2020.01.026 32114268)
Gurney-Champion OJ, Mahmood F, van Schie M, . Quantitative imaging for radiotherapy purposes. Radiother Oncol. 2020;146:66-75. doi:10.1016/j.radonc.2020.01.026 32114268
Gurney-Champion OJ, Mahmood F, van Schie M, . Quantitative imaging for radiotherapy purposes. Radiother Oncol. 2020;146:66-75. doi:10.1016/j.radonc.2020.01.026 32114268, Gurney-Champion OJ, Mahmood F, van Schie M, . Quantitative imaging for radiotherapy purposes. Radiother Oncol. 2020;146:66-75. doi:10.1016/j.radonc.2020.01.026 32114268
U. Schmidt-Erfurth, Wolf-Dieter Vogl, L. Jampol, H. Bogunović (2020)
Application of Automated Quantification of Fluid Volumes to Anti-VEGF Therapy of Neovascular Age-Related Macular Degeneration.
Ophthalmology
R. Guymer, C. Markey, I. McAllister, M. Gillies, A. Hunyor, J. Arnold (2019)
Tolerating Subretinal Fluid in Neovascular Age-Related Macular Degeneration Treated with Ranibizumab Using a Treat-and-Extend Regimen: FLUID Study 24-Month Results.
Ophthalmology, 126 5
(Keane PA, Patel PJ, Liakopoulos S, Heussen FM, Sadda SR, Tufail A. Evaluation of age-related macular degeneration with optical coherence tomography. Surv Ophthalmol. 2012;57(5):389-414. doi:10.1016/j.survophthal.2012.01.006 22898648)
Keane PA, Patel PJ, Liakopoulos S, Heussen FM, Sadda SR, Tufail A. Evaluation of age-related macular degeneration with optical coherence tomography. Surv Ophthalmol. 2012;57(5):389-414. doi:10.1016/j.survophthal.2012.01.006 22898648
Keane PA, Patel PJ, Liakopoulos S, Heussen FM, Sadda SR, Tufail A. Evaluation of age-related macular degeneration with optical coherence tomography. Surv Ophthalmol. 2012;57(5):389-414. doi:10.1016/j.survophthal.2012.01.006 22898648, Keane PA, Patel PJ, Liakopoulos S, Heussen FM, Sadda SR, Tufail A. Evaluation of age-related macular degeneration with optical coherence tomography. Surv Ophthalmol. 2012;57(5):389-414. doi:10.1016/j.survophthal.2012.01.006 22898648
A. Giani, M. Cigada, D. Esmaili, P. Salvetti, S. Luccarelli, E. Marziani, Cristiano Luiselli, P. Sabella, M. Cereda, C. Eandi, G. Staurenghi (2010)
ARTIFACTS IN AUTOMATIC RETINAL SEGMENTATION USING DIFFERENT OPTICAL COHERENCE TOMOGRAPHY INSTRUMENTS
Retina, 30
R. Sophie, N. Lu, P. Campochiaro (2015)
Predictors of Functional and Anatomic Outcomes in Patients with Diabetic Macular Edema Treated with Ranibizumab.
Ophthalmology, 122 7
(2020)
Quantitative analysis of change in retinal tissues in neovascular age-related macular degeneration using artificial intelligence
T. Schlegl, S. Waldstein, H. Bogunović, Franz Endstraßer, A. Sadeghipour, Ana-Maria Philip, D. Podkowinski, B. Gerendas, G. Langs, U. Schmidt-Erfurth (2017)
Fully Automated Detection and Quantification of Macular Fluid in OCT Using Deep Learning.
Ophthalmology, 125 4
(Schlegl T, Waldstein SM, Bogunovic H, . Fully automated detection and quantification of macular fluid in OCT using deep learning. Ophthalmology. 2018;125(4):549-558. doi:10.1016/j.ophtha.2017.10.031 29224926)
Schlegl T, Waldstein SM, Bogunovic H, . Fully automated detection and quantification of macular fluid in OCT using deep learning. Ophthalmology. 2018;125(4):549-558. doi:10.1016/j.ophtha.2017.10.031 29224926
Schlegl T, Waldstein SM, Bogunovic H, . Fully automated detection and quantification of macular fluid in OCT using deep learning. Ophthalmology. 2018;125(4):549-558. doi:10.1016/j.ophtha.2017.10.031 29224926, Schlegl T, Waldstein SM, Bogunovic H, . Fully automated detection and quantification of macular fluid in OCT using deep learning. Ophthalmology. 2018;125(4):549-558. doi:10.1016/j.ophtha.2017.10.031 29224926
Moraes (2021)
Quantitative analysis of optical coherence tomography for neovascular age-related macular degeneration using deep learning.
Ophthalmology, 128
(Sophie R, Lu N, Campochiaro PA. Predictors of functional and anatomic outcomes in patients with diabetic macular edema treated with ranibizumab. Ophthalmology. 2015;122(7):1395-1401. doi:10.1016/j.ophtha.2015.02.036 25870079)
Sophie R, Lu N, Campochiaro PA. Predictors of functional and anatomic outcomes in patients with diabetic macular edema treated with ranibizumab. Ophthalmology. 2015;122(7):1395-1401. doi:10.1016/j.ophtha.2015.02.036 25870079
Sophie R, Lu N, Campochiaro PA. Predictors of functional and anatomic outcomes in patients with diabetic macular edema treated with ranibizumab. Ophthalmology. 2015;122(7):1395-1401. doi:10.1016/j.ophtha.2015.02.036 25870079, Sophie R, Lu N, Campochiaro PA. Predictors of functional and anatomic outcomes in patients with diabetic macular edema treated with ranibizumab. Ophthalmology. 2015;122(7):1395-1401. doi:10.1016/j.ophtha.2015.02.036 25870079
P. Keane, P. Patel, S. Liakopoulos, F. Heussen, S. Sadda, A. Tufail (2012)
Evaluation of age-related macular degeneration with optical coherence tomography.
Survey of ophthalmology, 57 5
(Sadda SR, Wu Z, Walsh AC, . Errors in retinal thickness measurements obtained by optical coherence tomography. Ophthalmology. 2006;113(2):285-293. doi:10.1016/j.ophtha.2005.10.005 16406542)
Sadda SR, Wu Z, Walsh AC, . Errors in retinal thickness measurements obtained by optical coherence tomography. Ophthalmology. 2006;113(2):285-293. doi:10.1016/j.ophtha.2005.10.005 16406542
Sadda SR, Wu Z, Walsh AC, . Errors in retinal thickness measurements obtained by optical coherence tomography. Ophthalmology. 2006;113(2):285-293. doi:10.1016/j.ophtha.2005.10.005 16406542, Sadda SR, Wu Z, Walsh AC, . Errors in retinal thickness measurements obtained by optical coherence tomography. Ophthalmology. 2006;113(2):285-293. doi:10.1016/j.ophtha.2005.10.005 16406542
(Mehta N, Lee CS, Mendonça LSM, . Model-to-data approach for deep learning in optical coherence tomography intraretinal fluid segmentation. JAMA Ophthalmol. 2020;138(10):1017-1024. doi:10.1001/jamaophthalmol.2020.2769 32761143)
Mehta N, Lee CS, Mendonça LSM, . Model-to-data approach for deep learning in optical coherence tomography intraretinal fluid segmentation. JAMA Ophthalmol. 2020;138(10):1017-1024. doi:10.1001/jamaophthalmol.2020.2769 32761143
Mehta N, Lee CS, Mendonça LSM, . Model-to-data approach for deep learning in optical coherence tomography intraretinal fluid segmentation. JAMA Ophthalmol. 2020;138(10):1017-1024. doi:10.1001/jamaophthalmol.2020.2769 32761143, Mehta N, Lee CS, Mendonça LSM, . Model-to-data approach for deep learning in optical coherence tomography intraretinal fluid segmentation. JAMA Ophthalmol. 2020;138(10):1017-1024. doi:10.1001/jamaophthalmol.2020.2769 32761143
(Bogunovic H, Venhuizen F, Klimscha S, . RETOUCH: the Retinal OCT Fluid Detection and Segmentation Benchmark and Challenge. IEEE Trans Med Imaging. 2019;38(8):1858-1874. doi:10.1109/TMI.2019.2901398 30835214)
Bogunovic H, Venhuizen F, Klimscha S, . RETOUCH: the Retinal OCT Fluid Detection and Segmentation Benchmark and Challenge. IEEE Trans Med Imaging. 2019;38(8):1858-1874. doi:10.1109/TMI.2019.2901398 30835214
Bogunovic H, Venhuizen F, Klimscha S, . RETOUCH: the Retinal OCT Fluid Detection and Segmentation Benchmark and Challenge. IEEE Trans Med Imaging. 2019;38(8):1858-1874. doi:10.1109/TMI.2019.2901398 30835214, Bogunovic H, Venhuizen F, Klimscha S, . RETOUCH: the Retinal OCT Fluid Detection and Segmentation Benchmark and Challenge. IEEE Trans Med Imaging. 2019;38(8):1858-1874. doi:10.1109/TMI.2019.2901398 30835214
(Man MY, Ong MS, Mohamad MS, . A review on the bioinformatics tools for neuroimaging. Malays J Med Sci. 2015;22(Spec Issue):9-19.27006633)
Man MY, Ong MS, Mohamad MS, . A review on the bioinformatics tools for neuroimaging. Malays J Med Sci. 2015;22(Spec Issue):9-19.27006633
Man MY, Ong MS, Mohamad MS, . A review on the bioinformatics tools for neuroimaging. Malays J Med Sci. 2015;22(Spec Issue):9-19.27006633, Man MY, Ong MS, Mohamad MS, . A review on the bioinformatics tools for neuroimaging. Malays J Med Sci. 2015;22(Spec Issue):9-19.27006633
(Schmidt-Erfurth U, Vogl W-D, Jampol LM, Bogunović H. Application of automated quantification of fluid volumes to anti-VEGF therapy of neovascular age-related macular degeneration. Ophthalmology. 2020;127(9):1211-1219. doi:10.1016/j.ophtha.2020.03.010 32327254)
Schmidt-Erfurth U, Vogl W-D, Jampol LM, Bogunović H. Application of automated quantification of fluid volumes to anti-VEGF therapy of neovascular age-related macular degeneration. Ophthalmology. 2020;127(9):1211-1219. doi:10.1016/j.ophtha.2020.03.010 32327254
Schmidt-Erfurth U, Vogl W-D, Jampol LM, Bogunović H. Application of automated quantification of fluid volumes to anti-VEGF therapy of neovascular age-related macular degeneration. Ophthalmology. 2020;127(9):1211-1219. doi:10.1016/j.ophtha.2020.03.010 32327254, Schmidt-Erfurth U, Vogl W-D, Jampol LM, Bogunović H. Application of automated quantification of fluid volumes to anti-VEGF therapy of neovascular age-related macular degeneration. Ophthalmology. 2020;127(9):1211-1219. doi:10.1016/j.ophtha.2020.03.010 32327254
Lee (2017)
Deep-learning based, automated segmentation of macular edema in optical coherence tomography.
Biomed Opt Express, 8
L. Dice (1945)
Measures of the Amount of Ecologic Association Between Species
Ecology, 26
O. Gurney-Champion, F. Mahmood, M. Schie, R. Julian, B. George, M. Philippens, U. Heide, D. Thorwarth, K. Redalen (2020)
Quantitative imaging for radiotherapy purposes
Radiotherapy and Oncology, 146
Donghuan Lu, M. Heisler, Sieun Lee, G. Ding, E. Navajas, M. Sarunic, M. Beg (2019)
Deep‐learning based multiclass retinal fluid segmentation and detection in optical coherence tomography images using a fully convolutional neural network
Medical Image Analysis, 54
(De Fauw J, Ledsam JR, Romera-Paredes B, . Clinically applicable deep learning for diagnosis and referral in retinal disease. Nat Med. 2018;24(9):1342-1350. doi:10.1038/s41591-018-0107-6 30104768)
De Fauw J, Ledsam JR, Romera-Paredes B, . Clinically applicable deep learning for diagnosis and referral in retinal disease. Nat Med. 2018;24(9):1342-1350. doi:10.1038/s41591-018-0107-6 30104768
De Fauw J, Ledsam JR, Romera-Paredes B, . Clinically applicable deep learning for diagnosis and referral in retinal disease. Nat Med. 2018;24(9):1342-1350. doi:10.1038/s41591-018-0107-6 30104768, De Fauw J, Ledsam JR, Romera-Paredes B, . Clinically applicable deep learning for diagnosis and referral in retinal disease. Nat Med. 2018;24(9):1342-1350. doi:10.1038/s41591-018-0107-6 30104768
(World Medical Association. World Medical Association Declaration of Helsinki: ethical principles for medical research involving human subjects. JAMA. 2013;310(20):2191-2194. doi:10.1001/jama.2013.281053 24141714)
World Medical Association. World Medical Association Declaration of Helsinki: ethical principles for medical research involving human subjects. JAMA. 2013;310(20):2191-2194. doi:10.1001/jama.2013.281053 24141714
World Medical Association. World Medical Association Declaration of Helsinki: ethical principles for medical research involving human subjects. JAMA. 2013;310(20):2191-2194. doi:10.1001/jama.2013.281053 24141714, World Medical Association. World Medical Association Declaration of Helsinki: ethical principles for medical research involving human subjects. JAMA. 2013;310(20):2191-2194. doi:10.1001/jama.2013.281053 24141714
S. Waldstein, B. Gerendas, Alessio Montuoro, C. Simader, U. Schmidt-Erfurth (2015)
Quantitative comparison of macular segmentation performance using identical retinal regions across multiple spectral-domain optical coherence tomography instruments
British Journal of Ophthalmology, 99
(RETOUCH: Retinal OCT Fluid Challenge. September 14, 2017. Accessed September 8, 2020. https://retouch.grand-challenge.org/)
RETOUCH: Retinal OCT Fluid Challenge. September 14, 2017. Accessed September 8, 2020. https://retouch.grand-challenge.org/
RETOUCH: Retinal OCT Fluid Challenge. September 14, 2017. Accessed September 8, 2020. https://retouch.grand-challenge.org/, RETOUCH: Retinal OCT Fluid Challenge. September 14, 2017. Accessed September 8, 2020. https://retouch.grand-challenge.org/
R. Pokroy, M. Mimouni, Edward Barayev, F. Segev, Noa Geffen, A. Nemet, O. Segal (2017)
PROGNOSTIC VALUE OF SUBRETINAL HYPERREFLECTIVE MATERIAL IN NEOVASCULAR AGE-RELATED MACULAR DEGENERATION TREATED WITH BEVACIZUMAB
Retina, 38
(Costa RA, Skaf M, Melo LAS Jr, . Retinal assessment using optical coherence tomography. Prog Retin Eye Res. 2006;25(3):325-353. doi:10.1016/j.preteyeres.2006.03.001 16716639)
Costa RA, Skaf M, Melo LAS Jr, . Retinal assessment using optical coherence tomography. Prog Retin Eye Res. 2006;25(3):325-353. doi:10.1016/j.preteyeres.2006.03.001 16716639
Costa RA, Skaf M, Melo LAS Jr, . Retinal assessment using optical coherence tomography. Prog Retin Eye Res. 2006;25(3):325-353. doi:10.1016/j.preteyeres.2006.03.001 16716639, Costa RA, Skaf M, Melo LAS Jr, . Retinal assessment using optical coherence tomography. Prog Retin Eye Res. 2006;25(3):325-353. doi:10.1016/j.preteyeres.2006.03.001 16716639
(Ying G-S, Huang J, Maguire MG, ; Comparison of Age-related Macular Degeneration Treatments Trials Research Group. Baseline predictors for one-year visual outcomes with ranibizumab or bevacizumab for neovascular age-related macular degeneration. Ophthalmology. 2013;120(1):122-129. doi:10.1016/j.ophtha.2012.07.042 23047002)
Ying G-S, Huang J, Maguire MG, ; Comparison of Age-related Macular Degeneration Treatments Trials Research Group. Baseline predictors for one-year visual outcomes with ranibizumab or bevacizumab for neovascular age-related macular degeneration. Ophthalmology. 2013;120(1):122-129. doi:10.1016/j.ophtha.2012.07.042 23047002
Ying G-S, Huang J, Maguire MG, ; Comparison of Age-related Macular Degeneration Treatments Trials Research Group. Baseline predictors for one-year visual outcomes with ranibizumab or bevacizumab for neovascular age-related macular degeneration. Ophthalmology. 2013;120(1):122-129. doi:10.1016/j.ophtha.2012.07.042 23047002, Ying G-S, Huang J, Maguire MG, ; Comparison of Age-related Macular Degeneration Treatments Trials Research Group. Baseline predictors for one-year visual outcomes with ranibizumab or bevacizumab for neovascular age-related macular degeneration. Ophthalmology. 2013;120(1):122-129. doi:10.1016/j.ophtha.2012.07.042 23047002
Hyungwoo Lee, K. Kang, Hyewon Chung, Hyung Kim (2018)
Automated Segmentation of Lesions Including Subretinal Hyperreflective Material in Neovascular Age-related Macular Degeneration.
American journal of ophthalmology, 191
R. Costa, M. Skaf, L. Melo, D. Calucci, J. Cardillo, J. Castro, David Huang, M. Wojtkowski (2006)
Retinal assessment using optical coherence tomography
Progress in Retinal and Eye Research, 25
(Schindelin J, Arganda-Carreras I, Frise E, . Fiji: an open-source platform for biological-image analysis. Nat Methods. 2012;9(7):676-682. doi:10.1038/nmeth.2019 22743772)
Schindelin J, Arganda-Carreras I, Frise E, . Fiji: an open-source platform for biological-image analysis. Nat Methods. 2012;9(7):676-682. doi:10.1038/nmeth.2019 22743772
Schindelin J, Arganda-Carreras I, Frise E, . Fiji: an open-source platform for biological-image analysis. Nat Methods. 2012;9(7):676-682. doi:10.1038/nmeth.2019 22743772, Schindelin J, Arganda-Carreras I, Frise E, . Fiji: an open-source platform for biological-image analysis. Nat Methods. 2012;9(7):676-682. doi:10.1038/nmeth.2019 22743772
(Gerendas BS, Prager S, Deak G, . Predictive imaging biomarkers relevant for functional and anatomical outcomes during ranibizumab therapy of diabetic macular oedema. Br J Ophthalmol. 2018;102(2):195-203. doi:10.1136/bjophthalmol-2017-310483 28724636)
Gerendas BS, Prager S, Deak G, . Predictive imaging biomarkers relevant for functional and anatomical outcomes during ranibizumab therapy of diabetic macular oedema. Br J Ophthalmol. 2018;102(2):195-203. doi:10.1136/bjophthalmol-2017-310483 28724636
Gerendas BS, Prager S, Deak G, . Predictive imaging biomarkers relevant for functional and anatomical outcomes during ranibizumab therapy of diabetic macular oedema. Br J Ophthalmol. 2018;102(2):195-203. doi:10.1136/bjophthalmol-2017-310483 28724636, Gerendas BS, Prager S, Deak G, . Predictive imaging biomarkers relevant for functional and anatomical outcomes during ranibizumab therapy of diabetic macular oedema. Br J Ophthalmol. 2018;102(2):195-203. doi:10.1136/bjophthalmol-2017-310483 28724636
A. Rosenkrantz, M. Mendiratta-Lala, B. Bartholmai, D. Ganeshan, R. Abramson, K. Burton, J. Yu, E. Scalzetti, T. Yankeelov, R. Subramaniam, L. Lenchik (2015)
Clinical utility of quantitative imaging.
Academic radiology, 22 1
P. Rosenfeld (2016)
Optical Coherence Tomography and the Development of Antiangiogenic Therapies in Neovascular Age-Related Macular Degeneration
Investigative Ophthalmology & Visual Science, 57
(Gomez J, Doukky R, Germano G, Slomka P. New trends in quantitative nuclear cardiology methods. Curr Cardiovasc Imaging Rep. 2018;11(1):1. doi:10.1007/s12410-018-9443-7 30294409)
Gomez J, Doukky R, Germano G, Slomka P. New trends in quantitative nuclear cardiology methods. Curr Cardiovasc Imaging Rep. 2018;11(1):1. doi:10.1007/s12410-018-9443-7 30294409
Gomez J, Doukky R, Germano G, Slomka P. New trends in quantitative nuclear cardiology methods. Curr Cardiovasc Imaging Rep. 2018;11(1):1. doi:10.1007/s12410-018-9443-7 30294409, Gomez J, Doukky R, Germano G, Slomka P. New trends in quantitative nuclear cardiology methods. Curr Cardiovasc Imaging Rep. 2018;11(1):1. doi:10.1007/s12410-018-9443-7 30294409
(Oakley JD, Sodhi SK, Russakoff DB, Choudhry N. Automated deep learning-based multi-class fluid segmentation in swept-source optical coherence tomography images. Preprint. Posted online September 2, 2020. bioRxiv 278259. doi:10.1101/2020.09.01.278259 )
Oakley JD, Sodhi SK, Russakoff DB, Choudhry N. Automated deep learning-based multi-class fluid segmentation in swept-source optical coherence tomography images. Preprint. Posted online September 2, 2020. bioRxiv 278259. doi:10.1101/2020.09.01.278259
Oakley JD, Sodhi SK, Russakoff DB, Choudhry N. Automated deep learning-based multi-class fluid segmentation in swept-source optical coherence tomography images. Preprint. Posted online September 2, 2020. bioRxiv 278259. doi:10.1101/2020.09.01.278259 , Oakley JD, Sodhi SK, Russakoff DB, Choudhry N. Automated deep learning-based multi-class fluid segmentation in swept-source optical coherence tomography images. Preprint. Posted online September 2, 2020. bioRxiv 278259. doi:10.1101/2020.09.01.278259
T. Keenan, T. Clemons, A. Domalpally, M. Elman, Moshe Havilio, E. Agrón, Gidi Benyamini, E. Chew (2020)
Retinal specialist versus artificial intelligence detection of retinal fluid from optical coherence tomography: AREDS2 10-year Follow-On.
Ophthalmology
(Ritter M, Simader C, Bolz M, . Intraretinal cysts are the most relevant prognostic biomarker in neovascular age-related macular degeneration independent of the therapeutic strategy. Br J Ophthalmol. 2014;98(12):1629-1635. doi:10.1136/bjophthalmol-2014-305186 25079064)
Ritter M, Simader C, Bolz M, . Intraretinal cysts are the most relevant prognostic biomarker in neovascular age-related macular degeneration independent of the therapeutic strategy. Br J Ophthalmol. 2014;98(12):1629-1635. doi:10.1136/bjophthalmol-2014-305186 25079064
Ritter M, Simader C, Bolz M, . Intraretinal cysts are the most relevant prognostic biomarker in neovascular age-related macular degeneration independent of the therapeutic strategy. Br J Ophthalmol. 2014;98(12):1629-1635. doi:10.1136/bjophthalmol-2014-305186 25079064, Ritter M, Simader C, Bolz M, . Intraretinal cysts are the most relevant prognostic biomarker in neovascular age-related macular degeneration independent of the therapeutic strategy. Br J Ophthalmol. 2014;98(12):1629-1635. doi:10.1136/bjophthalmol-2014-305186 25079064
(Chopra R, Moraes G, Fu DJ, . Quantitative analysis of change in retinal tissues in neovascular age-related macular degeneration using artificial intelligence. Invest Ophthalmol Vis Sci. 2020;61(7):1152.)
Chopra R, Moraes G, Fu DJ, . Quantitative analysis of change in retinal tissues in neovascular age-related macular degeneration using artificial intelligence. Invest Ophthalmol Vis Sci. 2020;61(7):1152.
Chopra R, Moraes G, Fu DJ, . Quantitative analysis of change in retinal tissues in neovascular age-related macular degeneration using artificial intelligence. Invest Ophthalmol Vis Sci. 2020;61(7):1152., Chopra R, Moraes G, Fu DJ, . Quantitative analysis of change in retinal tissues in neovascular age-related macular degeneration using artificial intelligence. Invest Ophthalmol Vis Sci. 2020;61(7):1152.
(Rosenfeld PJ. Optical coherence tomography and the development of antiangiogenic therapies in neovascular age-related macular degeneration. Invest Ophthalmol Vis Sci. 2016;57(9):OCT14-OCT26. doi:10.1167/iovs.16-19969 27409464)
Rosenfeld PJ. Optical coherence tomography and the development of antiangiogenic therapies in neovascular age-related macular degeneration. Invest Ophthalmol Vis Sci. 2016;57(9):OCT14-OCT26. doi:10.1167/iovs.16-19969 27409464
Rosenfeld PJ. Optical coherence tomography and the development of antiangiogenic therapies in neovascular age-related macular degeneration. Invest Ophthalmol Vis Sci. 2016;57(9):OCT14-OCT26. doi:10.1167/iovs.16-19969 27409464, Rosenfeld PJ. Optical coherence tomography and the development of antiangiogenic therapies in neovascular age-related macular degeneration. Invest Ophthalmol Vis Sci. 2016;57(9):OCT14-OCT26. doi:10.1167/iovs.16-19969 27409464
(Toth CA, Decroos FC, Ying G-S, . Identification of fluid on optical coherence tomography by treating ophthalmologists versus a reading center in the comparison of age-related macular degeneration treatments trials. Retina. 2015;35(7):1303-1314. doi:10.1097/IAE.0000000000000483 26102433)
Toth CA, Decroos FC, Ying G-S, . Identification of fluid on optical coherence tomography by treating ophthalmologists versus a reading center in the comparison of age-related macular degeneration treatments trials. Retina. 2015;35(7):1303-1314. doi:10.1097/IAE.0000000000000483 26102433
Toth CA, Decroos FC, Ying G-S, . Identification of fluid on optical coherence tomography by treating ophthalmologists versus a reading center in the comparison of age-related macular degeneration treatments trials. Retina. 2015;35(7):1303-1314. doi:10.1097/IAE.0000000000000483 26102433, Toth CA, Decroos FC, Ying G-S, . Identification of fluid on optical coherence tomography by treating ophthalmologists versus a reading center in the comparison of age-related macular degeneration treatments trials. Retina. 2015;35(7):1303-1314. doi:10.1097/IAE.0000000000000483 26102433
B. Gerendas, S. Prager, G. Deak, C. Simader, J. Lammer, S. Waldstein, T. Guerin, M. Kundi, U. Schmidt-Erfurth (2017)
Predictive imaging biomarkers relevant for functional and anatomical outcomes during ranibizumab therapy of diabetic macular oedema
British Journal of Ophthalmology, 102
(Dice LR. Measures of the amount of ecologic association between species. Ecology. 1945;26(3):297-302. doi:10.2307/1932409 )
Dice LR. Measures of the amount of ecologic association between species. Ecology. 1945;26(3):297-302. doi:10.2307/1932409
Dice LR. Measures of the amount of ecologic association between species. Ecology. 1945;26(3):297-302. doi:10.2307/1932409 , Dice LR. Measures of the amount of ecologic association between species. Ecology. 1945;26(3):297-302. doi:10.2307/1932409
World Medical Association (2013)
World Medical Association Declaration of Helsinki: ethical principles for medical research involving human subjects.
JAMA, 310
Jeffrey Fauw, J. Ledsam, Bernardino Romera-Paredes, Stanislav Nikolov, Nenad Tomašev, Sam Blackwell, Harry Askham, Xavier Glorot, Brendan O'Donoghue, D. Visentin, George Driessche, Balaji Lakshminarayanan, Clemens Meyer, Faith Mackinder, Simon Bouton, K. Ayoub, Reena Chopra, Dominic King, A. Karthikesalingam, Cían Hughes, R. Raine, J. Hughes, D. Sim, C. Egan, A. Tufail, Hugh Montgomery, D. Hassabis, G. Rees, T. Back, P. Khaw, Mustafa Suleyman, Julien Cornebise, P. Keane, O. Ronneberger (2018)
Clinically applicable deep learning for diagnosis and referral in retinal disease
Nature Medicine, 24
Nihaal Mehta, Cecilia Lee, Luísa Mendonça, Khadija Raza, Phillip Braun, J. Duker, N. Waheed, Aaron Lee (2020)
Model-to-Data Approach for Deep Learning in Optical Coherence Tomography Intraretinal Fluid Segmentation
JAMA Ophthalmology, 138
M. Ritter, C. Simader, M. Bolz, G. Deak, Ulrike Mayr-Sponer, R. Sayegh, M. Kundi, U. Schmidt-Erfurth (2014)
Intraretinal cysts are the most relevant prognostic biomarker in neovascular age-related macular degeneration independent of the therapeutic strategy
British Journal of Ophthalmology, 98
E. Wilson (1927)
Probable Inference, the Law of Succession, and Statistical Inference
Journal of the American Statistical Association, 22
P. Anbeek, K. Vincken, M. Osch, R. Bisschops, J. Grond (2004)
Automatic segmentation of different-sized white matter lesions by voxel probability estimation
Medical image analysis, 8 3
(Roberts PK, Vogl W-D, Gerendas BS, . Quantification of fluid resolution and visual acuity gain in patients with diabetic macular edema using deep learning: a post hoc analysis of a randomized clinical trial. JAMA Ophthalmol. 2020;138(9):945-953. doi:10.1001/jamaophthalmol.2020.2457 32722799)
Roberts PK, Vogl W-D, Gerendas BS, . Quantification of fluid resolution and visual acuity gain in patients with diabetic macular edema using deep learning: a post hoc analysis of a randomized clinical trial. JAMA Ophthalmol. 2020;138(9):945-953. doi:10.1001/jamaophthalmol.2020.2457 32722799
Roberts PK, Vogl W-D, Gerendas BS, . Quantification of fluid resolution and visual acuity gain in patients with diabetic macular edema using deep learning: a post hoc analysis of a randomized clinical trial. JAMA Ophthalmol. 2020;138(9):945-953. doi:10.1001/jamaophthalmol.2020.2457 32722799, Roberts PK, Vogl W-D, Gerendas BS, . Quantification of fluid resolution and visual acuity gain in patients with diabetic macular edema using deep learning: a post hoc analysis of a randomized clinical trial. JAMA Ophthalmol. 2020;138(9):945-953. doi:10.1001/jamaophthalmol.2020.2457 32722799
(Monteiro M, Newcombe VFJ, Mathieu F, . Multiclass semantic segmentation and quantification of traumatic brain injury lesions on head CT using deep learning: an algorithm development and multicentre validation study. Lancet Digit Health. 2020;2(6):e314-e322. doi:10.1016/S2589-7500(20)30085-6 33328125)
Monteiro M, Newcombe VFJ, Mathieu F, . Multiclass semantic segmentation and quantification of traumatic brain injury lesions on head CT using deep learning: an algorithm development and multicentre validation study. Lancet Digit Health. 2020;2(6):e314-e322. doi:10.1016/S2589-7500(20)30085-6 33328125
Monteiro M, Newcombe VFJ, Mathieu F, . Multiclass semantic segmentation and quantification of traumatic brain injury lesions on head CT using deep learning: an algorithm development and multicentre validation study. Lancet Digit Health. 2020;2(6):e314-e322. doi:10.1016/S2589-7500(20)30085-6 33328125, Monteiro M, Newcombe VFJ, Mathieu F, . Multiclass semantic segmentation and quantification of traumatic brain injury lesions on head CT using deep learning: an algorithm development and multicentre validation study. Lancet Digit Health. 2020;2(6):e314-e322. doi:10.1016/S2589-7500(20)30085-6 33328125
Cecilia Lee, Ariel Tyring, Nicolaas Deruyter, Yue Wu, A. Rokem, Aaron Lee (2017)
Deep-Learning Based, Automated Segmentation of Macular Edema in Optical Coherence Tomography
bioRxiv
M. Monteiro, V. Newcombe, François Mathieu, K. Adatia, K. Kamnitsas, Enzo Ferrante, T. Das, D. Whitehouse, D. Rueckert, D. Menon, B. Glocker (2020)
Multiclass semantic segmentation and quantification of traumatic brain injury lesions on head CT using deep learning: an algorithm development and multicentre validation study.
The Lancet. Digital health, 2 6
H. Bogunović, F. Venhuizen, S. Klimscha, S. Apostolopoulos, A. Bab-Hadiashar, Ulas Bagci, M. Beg, Loza Bekalo, Qiang Chen, Carlos Ciller, Karthik Gopinath, A. Gostar, Kiwan Jeon, Zexuan Ji, S. Kang, D. Koozekanani, Donghuan Lu, Dustin Morley, K. Parhi, Hyoung Park, A. Rashno, M. Sarunic, S. Shaikh, J. Sivaswamy, Ruwan Tennakoon, Shivin Yadav, Sandro Zanet, S. Waldstein, B. Gerendas, C. Klaver, C. Sánchez, U. Schmidt-Erfurth (2019)
RETOUCH: The Retinal OCT Fluid Detection and Segmentation Benchmark and Challenge
IEEE Transactions on Medical Imaging, 38
(Ronneberger O, Fischer P, Brox T. U-Net: convolutional networks for biomedical image segmentation. In: Navab N, Hornegger J, Wells WM, Frangi AF, eds. Medical Image Computing and Computer-Assisted Intervention—MICCAI 2015. Springer; 2015:234-241. doi:10.1007/978-3-319-24574-4_28 )
Ronneberger O, Fischer P, Brox T. U-Net: convolutional networks for biomedical image segmentation. In: Navab N, Hornegger J, Wells WM, Frangi AF, eds. Medical Image Computing and Computer-Assisted Intervention—MICCAI 2015. Springer; 2015:234-241. doi:10.1007/978-3-319-24574-4_28
Ronneberger O, Fischer P, Brox T. U-Net: convolutional networks for biomedical image segmentation. In: Navab N, Hornegger J, Wells WM, Frangi AF, eds. Medical Image Computing and Computer-Assisted Intervention—MICCAI 2015. Springer; 2015:234-241. doi:10.1007/978-3-319-24574-4_28 , Ronneberger O, Fischer P, Brox T. U-Net: convolutional networks for biomedical image segmentation. In: Navab N, Hornegger J, Wells WM, Frangi AF, eds. Medical Image Computing and Computer-Assisted Intervention—MICCAI 2015. Springer; 2015:234-241. doi:10.1007/978-3-319-24574-4_28
(Information Commissioner’s Office. Anonymisation: Managing Data Protection Risk Code of Practice. 2015. Accessed March 1, 2016. https://ico.org.uk/media/1061/anonymisation-code.pdf)
Information Commissioner’s Office. Anonymisation: Managing Data Protection Risk Code of Practice. 2015. Accessed March 1, 2016. https://ico.org.uk/media/1061/anonymisation-code.pdf
Information Commissioner’s Office. Anonymisation: Managing Data Protection Risk Code of Practice. 2015. Accessed March 1, 2016. https://ico.org.uk/media/1061/anonymisation-code.pdf, Information Commissioner’s Office. Anonymisation: Managing Data Protection Risk Code of Practice. 2015. Accessed March 1, 2016. https://ico.org.uk/media/1061/anonymisation-code.pdf
A. Euser, F. Dekker, S. Cessie (2008)
A practical approach to Bland-Altman plots and variation coefficients for log transformed variables.
Journal of clinical epidemiology, 61 10
(Lee H, Kang KE, Chung H, Kim HC. Automated segmentation of lesions including subretinal hyperreflective material in neovascular age-related macular degeneration. Am J Ophthalmol. 2018;191:64-75. doi:10.1016/j.ajo.2018.04.007 29655643)
Lee H, Kang KE, Chung H, Kim HC. Automated segmentation of lesions including subretinal hyperreflective material in neovascular age-related macular degeneration. Am J Ophthalmol. 2018;191:64-75. doi:10.1016/j.ajo.2018.04.007 29655643
Lee H, Kang KE, Chung H, Kim HC. Automated segmentation of lesions including subretinal hyperreflective material in neovascular age-related macular degeneration. Am J Ophthalmol. 2018;191:64-75. doi:10.1016/j.ajo.2018.04.007 29655643, Lee H, Kang KE, Chung H, Kim HC. Automated segmentation of lesions including subretinal hyperreflective material in neovascular age-related macular degeneration. Am J Ophthalmol. 2018;191:64-75. doi:10.1016/j.ajo.2018.04.007 29655643
(Wintergerst MWM, Schultz T, Birtel J, . Algorithms for the automated analysis of age-related macular degeneration biomarkers on optical coherence tomography: a systematic review. Transl Vis Sci Technol. 2017;6(4):10-10. doi:10.1167/tvst.6.4.10 28729948)
Wintergerst MWM, Schultz T, Birtel J, . Algorithms for the automated analysis of age-related macular degeneration biomarkers on optical coherence tomography: a systematic review. Transl Vis Sci Technol. 2017;6(4):10-10. doi:10.1167/tvst.6.4.10 28729948
Wintergerst MWM, Schultz T, Birtel J, . Algorithms for the automated analysis of age-related macular degeneration biomarkers on optical coherence tomography: a systematic review. Transl Vis Sci Technol. 2017;6(4):10-10. doi:10.1167/tvst.6.4.10 28729948, Wintergerst MWM, Schultz T, Birtel J, . Algorithms for the automated analysis of age-related macular degeneration biomarkers on optical coherence tomography: a systematic review. Transl Vis Sci Technol. 2017;6(4):10-10. doi:10.1167/tvst.6.4.10 28729948
J. Oakley, Simrat Sodhi, Daniel Russakoff, N. Choudhry (2020)
Automated Deep Learning-based Multi-class Fluid Segmentation in Swept-Source Optical Coherence Tomography Images
bioRxiv
Christiane, Druml, Wolzt, Pleiner (2000)
World Medical Association Declaration of Helsinki
International Journal of Pharmaceutical Medicine, 14
(Moraes G, Fu DJ, Wilson M, . Quantitative analysis of optical coherence tomography for neovascular age-related macular degeneration using deep learning. Ophthalmology. 2021;128(5):693-705. doi:10.1016/j.ophtha.2020.09.025 32980396)
Moraes G, Fu DJ, Wilson M, . Quantitative analysis of optical coherence tomography for neovascular age-related macular degeneration using deep learning. Ophthalmology. 2021;128(5):693-705. doi:10.1016/j.ophtha.2020.09.025 32980396
Moraes G, Fu DJ, Wilson M, . Quantitative analysis of optical coherence tomography for neovascular age-related macular degeneration using deep learning. Ophthalmology. 2021;128(5):693-705. doi:10.1016/j.ophtha.2020.09.025 32980396, Moraes G, Fu DJ, Wilson M, . Quantitative analysis of optical coherence tomography for neovascular age-related macular degeneration using deep learning. Ophthalmology. 2021;128(5):693-705. doi:10.1016/j.ophtha.2020.09.025 32980396
Philipp Roberts, Wolf-Dieter Vogl, B. Gerendas, A. Glassman, H. Bogunović, L. Jampol, U. Schmidt-Erfurth (2020)
Quantification of Fluid Resolution and Visual Acuity Gain in Patients With Diabetic Macular Edema Using Deep Learning: A Post Hoc Analysis of a Randomized Clinical Trial.
JAMA ophthalmology
(Rosenkrantz AB, Mendiratta-Lala M, Bartholmai BJ, . Clinical utility of quantitative imaging. Acad Radiol. 2015;22(1):33-49. doi:10.1016/j.acra.2014.08.011 25442800)
Rosenkrantz AB, Mendiratta-Lala M, Bartholmai BJ, . Clinical utility of quantitative imaging. Acad Radiol. 2015;22(1):33-49. doi:10.1016/j.acra.2014.08.011 25442800
Rosenkrantz AB, Mendiratta-Lala M, Bartholmai BJ, . Clinical utility of quantitative imaging. Acad Radiol. 2015;22(1):33-49. doi:10.1016/j.acra.2014.08.011 25442800, Rosenkrantz AB, Mendiratta-Lala M, Bartholmai BJ, . Clinical utility of quantitative imaging. Acad Radiol. 2015;22(1):33-49. doi:10.1016/j.acra.2014.08.011 25442800
Mei Man, Mei Ong, M. Mohamad, S. Deris, G. Sulong, J. Yunus, F. Harun (2015)
A Review on the Bioinformatics Tools for Neuroimaging.
The Malaysian journal of medical sciences : MJMS, 22 Spec Issue
(Waldstein SM, Gerendas BS, Montuoro A, Simader C, Schmidt-Erfurth U. Quantitative comparison of macular segmentation performance using identical retinal regions across multiple spectral-domain optical coherence tomography instruments. Br J Ophthalmol. 2015;99(6):794-800. doi:10.1136/bjophthalmol-2014-305573 25563763)
Waldstein SM, Gerendas BS, Montuoro A, Simader C, Schmidt-Erfurth U. Quantitative comparison of macular segmentation performance using identical retinal regions across multiple spectral-domain optical coherence tomography instruments. Br J Ophthalmol. 2015;99(6):794-800. doi:10.1136/bjophthalmol-2014-305573 25563763
Waldstein SM, Gerendas BS, Montuoro A, Simader C, Schmidt-Erfurth U. Quantitative comparison of macular segmentation performance using identical retinal regions across multiple spectral-domain optical coherence tomography instruments. Br J Ophthalmol. 2015;99(6):794-800. doi:10.1136/bjophthalmol-2014-305573 25563763, Waldstein SM, Gerendas BS, Montuoro A, Simader C, Schmidt-Erfurth U. Quantitative comparison of macular segmentation performance using identical retinal regions across multiple spectral-domain optical coherence tomography instruments. Br J Ophthalmol. 2015;99(6):794-800. doi:10.1136/bjophthalmol-2014-305573 25563763
(Euser AM, Dekker FW, le Cessie S. A practical approach to Bland-Altman plots and variation coefficients for log transformed variables. J Clin Epidemiol. 2008;61(10):978-982. doi:10.1016/j.jclinepi.2007.11.003 18468854)
Euser AM, Dekker FW, le Cessie S. A practical approach to Bland-Altman plots and variation coefficients for log transformed variables. J Clin Epidemiol. 2008;61(10):978-982. doi:10.1016/j.jclinepi.2007.11.003 18468854
Euser AM, Dekker FW, le Cessie S. A practical approach to Bland-Altman plots and variation coefficients for log transformed variables. J Clin Epidemiol. 2008;61(10):978-982. doi:10.1016/j.jclinepi.2007.11.003 18468854, Euser AM, Dekker FW, le Cessie S. A practical approach to Bland-Altman plots and variation coefficients for log transformed variables. J Clin Epidemiol. 2008;61(10):978-982. doi:10.1016/j.jclinepi.2007.11.003 18468854
C. Toth, F. Decroos, G. Ying, S. Stinnett, Cynthia Heydary, R. Burns, M. Maguire, Daniel Martin, G. Jaffe (2015)
IDENTIFICATION OF FLUID ON OPTICAL COHERENCE TOMOGRAPHY BY TREATING OPHTHALMOLOGISTS VERSUS A READING CENTER IN THE COMPARISON OF AGE-RELATED MACULAR DEGENERATION TREATMENTS TRIALS
Retina, 35
(Anbeek P, Vincken KL, van Osch MJP, Bisschops RHC, van der Grond J. Automatic segmentation of different-sized white matter lesions by voxel probability estimation. Med Image Anal. 2004;8(3):205-215. doi:10.1016/j.media.2004.06.019 15450216)
Anbeek P, Vincken KL, van Osch MJP, Bisschops RHC, van der Grond J. Automatic segmentation of different-sized white matter lesions by voxel probability estimation. Med Image Anal. 2004;8(3):205-215. doi:10.1016/j.media.2004.06.019 15450216
Anbeek P, Vincken KL, van Osch MJP, Bisschops RHC, van der Grond J. Automatic segmentation of different-sized white matter lesions by voxel probability estimation. Med Image Anal. 2004;8(3):205-215. doi:10.1016/j.media.2004.06.019 15450216, Anbeek P, Vincken KL, van Osch MJP, Bisschops RHC, van der Grond J. Automatic segmentation of different-sized white matter lesions by voxel probability estimation. Med Image Anal. 2004;8(3):205-215. doi:10.1016/j.media.2004.06.019 15450216
(Giani A, Cigada M, Esmaili DD, . Artifacts in automatic retinal segmentation using different optical coherence tomography instruments. Retina. 2010;30(4):607-616. doi:10.1097/IAE.0b013e3181c2e09d 20094011)
Giani A, Cigada M, Esmaili DD, . Artifacts in automatic retinal segmentation using different optical coherence tomography instruments. Retina. 2010;30(4):607-616. doi:10.1097/IAE.0b013e3181c2e09d 20094011
Giani A, Cigada M, Esmaili DD, . Artifacts in automatic retinal segmentation using different optical coherence tomography instruments. Retina. 2010;30(4):607-616. doi:10.1097/IAE.0b013e3181c2e09d 20094011, Giani A, Cigada M, Esmaili DD, . Artifacts in automatic retinal segmentation using different optical coherence tomography instruments. Retina. 2010;30(4):607-616. doi:10.1097/IAE.0b013e3181c2e09d 20094011
S. Sadda, Ziqiang Wu, A. Walsh, L. Richine, J. Dougall, R. Cortez, L. Labree (2006)
Errors in retinal thickness measurements obtained by optical coherence tomography.
Ophthalmology, 113 2
(Keenan TD, Clemons TE, Domalpally A, . 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(1):100-109. doi:10.1016/j.ophtha.2020.06.038 32598950)
Keenan TD, Clemons TE, Domalpally A, . 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(1):100-109. doi:10.1016/j.ophtha.2020.06.038 32598950
Keenan TD, Clemons TE, Domalpally A, . 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(1):100-109. doi:10.1016/j.ophtha.2020.06.038 32598950, Keenan TD, Clemons TE, Domalpally A, . 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(1):100-109. doi:10.1016/j.ophtha.2020.06.038 32598950
J. Gomez, R. Doukky, G. Germano, P. Slomka (2018)
New Trends in Quantitative Nuclear Cardiology Methods
Current Cardiovascular Imaging Reports, 11
(Guymer RH, Markey CM, McAllister IL, Gillies MC, Hunyor AP, Arnold JJ; FLUID Investigators. Tolerating subretinal fluid in neovascular age-related macular degeneration treated with ranibizumab using a treat-and-extend regimen: FLUID Study 24-month results. Ophthalmology. 2019;126(5):723-734. doi:10.1016/j.ophtha.2018.11.025 30502372)
Guymer RH, Markey CM, McAllister IL, Gillies MC, Hunyor AP, Arnold JJ; FLUID Investigators. Tolerating subretinal fluid in neovascular age-related macular degeneration treated with ranibizumab using a treat-and-extend regimen: FLUID Study 24-month results. Ophthalmology. 2019;126(5):723-734. doi:10.1016/j.ophtha.2018.11.025 30502372
Guymer RH, Markey CM, McAllister IL, Gillies MC, Hunyor AP, Arnold JJ; FLUID Investigators. Tolerating subretinal fluid in neovascular age-related macular degeneration treated with ranibizumab using a treat-and-extend regimen: FLUID Study 24-month results. Ophthalmology. 2019;126(5):723-734. doi:10.1016/j.ophtha.2018.11.025 30502372, Guymer RH, Markey CM, McAllister IL, Gillies MC, Hunyor AP, Arnold JJ; FLUID Investigators. Tolerating subretinal fluid in neovascular age-related macular degeneration treated with ranibizumab using a treat-and-extend regimen: FLUID Study 24-month results. Ophthalmology. 2019;126(5):723-734. doi:10.1016/j.ophtha.2018.11.025 30502372
(2015)
Medical Image Computing and Computer-Assisted Intervention-MICCAI

Publisher: American Medical Association
ISSN: 2168-6165
eISSN: 2168-6173
DOI: 10.1001/jamaophthalmol.2021.2273
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Validation and Clinical Applicability of Whole-Volume Automated Segmentation of Optical Coherence Tomography in Retinal Disease Using Deep Learning

Validation and Clinical Applicability of Whole-Volume Automated Segmentation of Optical Coherence Tomography in Retinal Disease Using Deep Learning

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Validation and Clinical Applicability of Whole-Volume Automated Segmentation of Optical Coherence Tomography in Retinal Disease Using Deep Learning

Validation and Clinical Applicability of Whole-Volume Automated Segmentation of Optical Coherence Tomography in Retinal Disease Using Deep Learning

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