Computational image analysis for prognosis determination in DME

Vision Research

Volumn 139, Issue , 2017, Pages 204-210

  Gerendas, Bianca S ^a   Bogunovic, Hrvoje ^a   Sadeghipour, Amir ^a   Schlegl, Thomas ^a   Langs, Georg ^a   Waldstein, Sebastian M ^a   Schmidt Erfurth, Ursula ^a  

^a MEDICAL UNIVERSITY OF VIENNA   (Austria)

Author keywords

Computational image analysis; Diabetic macular edema; Large scale data analysis; Machine learning; Prediction; Random forest

Indexed keywords

VASCULOTROPIN INHIBITOR; ANGIOGENESIS INHIBITOR; VASCULOTROPIN A; VEGFA PROTEIN, HUMAN;

ARTICLE; BEST CORRECTED VISUAL ACUITY; CONTROLLED STUDY; DIABETIC MACULAR EDEMA; DIAGNOSTIC ACCURACY; DIAGNOSTIC TEST ACCURACY STUDY; HUMAN; IMAGE ANALYSIS; IMAGE QUALITY; IMAGE SEGMENTATION; INTRARETINAL CYSTOID FLUID; MACHINE LEARNING; MAJOR CLINICAL STUDY; MATHEMATICAL ANALYSIS; OPTICAL COHERENCE TOMOGRAPHY; PILOT STUDY; PREDICTION; PRIORITY JOURNAL; PROGNOSIS; RANDOM FOREST; RANDOMIZED CONTROLLED TRIAL; RETINA; RETINA DISEASE; RETINA FOVEA; RETINAL OUTER NUCLEAR LAYER; RETINAL THICKNESS; SPECTRAL DOMAIN OPTICAL COHERENCE TOMOGRAPHY; STRUCTURE ACTIVITY RELATION; SUBRETINAL FLUID; TREATMENT DURATION; TREATMENT OUTCOME; ALGORITHM; ANTAGONISTS AND INHIBITORS; DIABETIC RETINOPATHY; DIAGNOSTIC IMAGING; FEMALE; FLUORESCENCE ANGIOGRAPHY; IMAGE PROCESSING; INTRAVITREAL DRUG ADMINISTRATION; MACULAR EDEMA; MALE; MIDDLE AGED; PROSPECTIVE STUDY;

ALGORITHMS; ANGIOGENESIS INHIBITORS; DIABETIC RETINOPATHY; FEMALE; FLUORESCEIN ANGIOGRAPHY; HUMANS; IMAGE PROCESSING, COMPUTER-ASSISTED; INTRAVITREAL INJECTIONS; MACHINE LEARNING; MACULAR EDEMA; MALE; MIDDLE AGED; PILOT PROJECTS; PROGNOSIS; PROSPECTIVE STUDIES; TOMOGRAPHY, OPTICAL COHERENCE; VASCULAR ENDOTHELIAL GROWTH FACTOR A;

EID: 85019024523     PISSN: 00426989     EISSN: 18785646     Source Type: Journal    
DOI: 10.1016/j.visres.2017.03.008     Document Type: Article

References (21)

1. Abramoff, M.D., Lou, Y., Erginay, A., Clarida, W., Amelon, R., Folk, J.C., et al. Improved automated detection of diabetic retinopathy on a publicly available dataset through integration of deep learning. Investigative Ophthalmology & Visual Science 57:13 (2016), 5200–5206.
2. Adhi, M., Semy, S.K., Stein, D.W., Potter, D.M., Kuklinski, W.S., Sleeper, H.A., et al. Application of novel software algorithms to spectral-domain optical coherence tomography for automated detection of diabetic retinopathy. Ophthalmic Surgery, Lasers and Imaging Retina 47:5 (2016), 410–417.
3. Chen, X., Niemeijer, M., Zhang, L., Lee, K., Abramoff, M.D., Sonka, M., Three-dimensional segmentation of fluid-associated abnormalities in retinal OCT: probability constrained graph-search-graph-cut. IEEE Transactions on Medical Imaging 31:8 (2012), 1521–1531.
4. Garvin, M.K., Abramoff, M.D., Kardon, R., Russell, S.R., Wu, X., Sonka, M., Intraretinal layer segmentation of macular optical coherence tomography images using optimal 3-D graph search. IEEE Transactions on Medical Imaging 27:10 (2008), 1495–1505.
5. Gerendas, B., Simader, C., Deak, G.G., Prager, S.G., Lammer, J., Waldstein, S.M., et al. Morphological parameters relevant for visual and anatomic outcomes during anti-VEGF therapy of diabetic macular edema in the RESTORE trial. Investigative Ophthalmology & Visual Science, 55(13), 2014, 1791.
6. Gerendas, B.S., Waldstein, S.M., Simader, C., Deak, G., Hajnajeeb, B., Zhang, L., et al. Three-dimensional automated choroidal volume assessment on standard spectral-domain optical coherence tomography and correlation with the level of diabetic macular edema. American Journal of Ophthalmology, 158(5), 2014 1039–48 e1.
7. Gulshan, V., Peng, L., Coram, M., Stumpe, M.C., Wu, D., Narayanaswamy, A., et al. Development and validation of a deep learning algorithm for detection of diabetic retinopathy in retinal fundus photographs. JAMA, 2016.
8. Kapetanakis, V.V., Rudnicka, A.R., Liew, G., Owen, C.G., Lee, A., Louw, V., et al. A study of whether automated Diabetic Retinopathy Image Assessment could replace manual grading steps in the English National Screening Programme. Journal of Medical Screening 22:3 (2015), 112–118.
9. Philip, A.-M., Podkowinski, D., Pablik, E., Montuoro, A., Waldstein, S.M., Gerendas, B., et al. Presence of subretinal fluid at baseline preserves from photoreceptor alterations in diabetic macular edema and cystoid macular edema due to central retinal vein occlusion. Investigative Ophthalmology & Visual Science, 57(12), 2016, 4164.
10. Ritter, M., Simader, C., Bolz, M., Deak, G.G., Mayr-Sponer, U., Sayegh, R., et al. Intraretinal cysts are the most relevant prognostic biomarker in neovascular age-related macular degeneration independent of the therapeutic strategy. British Journal of Ophthalmology 98:12 (2014), 1629–1635.
11. Schlegl, T., Waldstein, S.M., Vogl, W.D., Schmidt-Erfurth, U., Langs, G., Predicting semantic descriptions from medical images with convolutional neural networks. Information Processing in Medical Imaging 24 (2015), 437–448.
12. Schmidt-Erfurth, U., Bogunovic, H., Schlegl, T., Sadeghipour, A., Waldstein, S.M., Gerendas, B.S., Prognostic factors in the treatment of diabetic macular edema (DME) using aflibercept, ranibizumab and bevacizumab (DRCR.net protocol T). Investigative Ophthalmology & Visual Science, 2016 ARVO Annual Meeting 2016.
13. Schmidt-Erfurth, U., Waldstein, S.M., Deak, G.G., Kundi, M., Simader, C., Pigment epithelial detachment followed by retinal cystoid degeneration leads to vision loss in treatment of neovascular age-related macular degeneration. Ophthalmology 122:4 (2015), 822–832.
14. Shen, Y., Liu, K., Xu, X., Correlation between visual function and photoreceptor integrity in diabetic macular edema: spectral-domain optical coherence tomography. Current Eye Research 41:3 (2016), 391–399.
15. Shin, H.J., Lee, S.H., Chung, H., Kim, H.C., Association between photoreceptor integrity and visual outcome in diabetic macular edema. Graefe's Archive for Clinical and Experimental Ophthalmology 250:1 (2012), 61–70.
16. Sophie, R., Lu, N., Campochiaro, P.A., Predictors of functional and anatomic outcomes in patients with diabetic macular edema treated with ranibizumab. Ophthalmology, 2015.
17. Wang, J., Zhang, M., Pechauer, A.D., Liu, L., Hwang, T.S., Wilson, D.J., et al. Automated volumetric segmentation of retinal fluid on optical coherence tomography. Biomedical Optics Express 7:4 (2016), 1577–1589.
18. Wells, J.A., Glassman, A.R., Ayala, A.R., Jampol, L.M., Aiello, L.P., Antoszyk, A.N., et al. Aflibercept, bevacizumab, or ranibizumab for diabetic macular edema. The New England Journal of Medicine 372:13 (2015), 1193–1203.
19. Wells, J.A., Glassman, A.R., Jampol, L.M., Aiello, L.P., Antoszyk, A.N., Baker, C.W., et al. Association of baseline visual acuity and retinal thickness with 1-year efficacy of aflibercept, bevacizumab, and ranibizumab for diabetic macular edema. JAMA Ophthalmology 134:2 (2016), 127–134.
20. Wong, T.Y., Bressler, N.M., Artificial intelligence with deep learning technology looks into diabetic retinopathy screening. JAMA, 2016.
21. Yohannan, J., Bittencourt, M., Sepah, Y.J., Hatef, E., Sophie, R., Moradi, A., et al. Association of retinal sensitivity to integrity of photoreceptor inner/outer segment junction in patients with diabetic macular edema. Ophthalmology 120:6 (2013), 1254–1261.