Algorithms for the Automated Analysis of Age-Related Macular Degeneration Biomarkers on Optical Coherence Tomography: A Systematic Review

Purpose To assess the quality of optical coherence tomography (OCT) grading algorithms for retinal biomarkers of age-related macular degeneration (AMD). Methods Following a systematic review of the literature data on detection and quantification of AMD retinal biomarkers by available algorithms were extracted and descriptively synthesized. Algorithm quality was assessed using a modified version of the Quality Assessment of Diagnostic Accuracy Studies 2 checklist with a focus on accuracy against established reference standards and risk of bias. Results Thirty five studies reporting computer-aided diagnosis (CAD) tools for qualitative analysis or algorithms for quantitative analysis were identified. Compared with manual assessment in reference standards correlation coefficients ranged from 0.54 to 0.97 for drusen, 0.80 to 0.98 for geographic atrophy (GA), and 0.30 to 0.98 for intra- or subretinal fluid and pigment epithelial detachment (PED) detection by automated algorithms. CAD tools achieved area under the curve (AUC) values of 0.94 to 0.99, sensitivity of 0.90 to 1.00, and specificity of 0.89 to 0.92. Conclusions Automated analysis of AMD biomarkers on OCT is promising. However, most of the algorithm validation was performed in preselected patients, exhibiting the targeted biomarker only. In addition, type and quality of reported algorithm validation varied substantially. Translational Relevance The development of algorithms for combined, simultaneous analysis of multiple AMD biomarkers including AMD staging and the agreement on standardized validation procedures would be of considerable translational value for the clinician and the clinical researcher.

[1]  Srinivas R Sadda,et al.  Accuracy and reproducibility of automated drusen segmentation in eyes with non-neovascular age-related macular degeneration. , 2012, Investigative ophthalmology & visual science.

[2]  Frans Coenen,et al.  Age-related Macular Degeneration Identification In Volumetric Optical Coherence Tomography Using Decomposition and Local Feature Extraction , 2013 .

[3]  J. Hartigan Statistical theory in clustering , 1985 .

[4]  William J Feuer,et al.  QUANTITATIVE CHANGES IN RETINAL PIGMENT EPITHELIAL DETACHMENTS AS A PREDICTOR FOR RETREATMENT WITH ANTI-VEGF THERAPY , 2013, Retina.

[5]  P. Rosenfeld,et al.  Increasing volume of a retinal pigmented epithelial detachment as a predictor of submacular hemorrhage during anti-VEGF therapy. , 2013, Ophthalmic surgery, lasers & imaging retina.

[6]  Xavier Bresson,et al.  Geometric Applications of the Split Bregman Method: Segmentation and Surface Reconstruction , 2010, J. Sci. Comput..

[7]  Peter E. Hart,et al.  Nearest neighbor pattern classification , 1967, IEEE Trans. Inf. Theory.

[8]  R. Klein,et al.  Prevalence of age-related maculopathy. The Beaver Dam Eye Study. , 1992, Ophthalmology.

[9]  B. Lujan,et al.  Spectral domain optical coherence tomography imaging of drusen in nonexudative age-related macular degeneration. , 2011, Ophthalmology.

[10]  William J Feuer,et al.  Natural history of drusen morphology in age-related macular degeneration using spectral domain optical coherence tomography. , 2011, Ophthalmology.

[11]  William J Feuer,et al.  Quantitative imaging of retinal pigment epithelial detachments using spectral-domain optical coherence tomography. , 2012, American journal of ophthalmology.

[12]  Xiaodong Wu,et al.  Intraretinal Layer Segmentation of Macular Optical Coherence Tomography Images Using Optimal 3-D Graph Search , 2008, IEEE Transactions on Medical Imaging.

[13]  Tom Goldstein,et al.  The Split Bregman Method for L1-Regularized Problems , 2009, SIAM J. Imaging Sci..

[14]  Sina Farsiu,et al.  Fully automated detection of diabetic macular edema and dry age-related macular degeneration from optical coherence tomography images. , 2014, Biomedical optics express.

[15]  William J Feuer,et al.  Comparison of drusen area detected by spectral domain optical coherence tomography and color fundus imaging. , 2013, Investigative ophthalmology & visual science.

[16]  U. Schmidt-Erfurth,et al.  Automatic segmentation in three-dimensional analysis of fibrovascular pigmentepithelial detachment using high-definition optical coherence tomography , 2007, British Journal of Ophthalmology.

[17]  Xinjian Chen,et al.  Automated Estimation of Fluid Volume in 3D OCT Scans of Patients with CNV Due to AMD , 2012 .

[18]  Ronald Fedkiw,et al.  A Level Set Approach for the Numerical Simulation of Dendritic Growth , 2003, J. Sci. Comput..

[19]  P. Serrano-Aguilar,et al.  Development and validation of a computer-aided diagnostic tool to screen for age-related macular degeneration by optical coherence tomography , 2011, British Journal of Ophthalmology.

[20]  Oscar Martinez,et al.  Geometric Deformable Model Driven by CoCRFs: Application to Optical Coherence Tomography , 2008, MICCAI.

[21]  Milan Sonka,et al.  Stratified Sampling Voxel Classification for Segmentation of Intraretinal and Subretinal Fluid in Longitudinal Clinical OCT Data , 2015, IEEE Transactions on Medical Imaging.

[22]  William J Feuer,et al.  Change in drusen area over time compared using spectral-domain optical coherence tomography and color fundus imaging. , 2014, Investigative ophthalmology & visual science.

[23]  Milan Sonka,et al.  Three-Dimensional Analysis of Retinal Layer Texture: Identification of Fluid-Filled Regions in SD-OCT of the Macula , 2010, IEEE Transactions on Medical Imaging.

[24]  A. Merkur,et al.  Optical coherence tomography-based measurement of drusen load predicts development of advanced age-related macular degeneration. , 2014, American journal of ophthalmology.

[25]  Leo Breiman,et al.  Random Forests , 2001, Machine Learning.

[26]  Frans Coenen,et al.  One-class kernel subspace ensemble for medical image classification , 2014, EURASIP Journal on Advances in Signal Processing.

[27]  Sina Farsiu,et al.  Validated automatic segmentation of AMD pathology including drusen and geographic atrophy in SD-OCT images. , 2012, Investigative ophthalmology & visual science.

[28]  Joseph A. Izatt,et al.  Automatic segmentation of seven retinal layers in SDOCT images congruent with expert manual segmentation , 2010, Optics express.

[29]  Xinjian Chen,et al.  Automated 3-D Retinal Layer Segmentation of Macular Optical Coherence Tomography Images With Serous Pigment Epithelial Detachments , 2015, IEEE Transactions on Medical Imaging.

[30]  Sina Farsiu,et al.  Quantitative comparison of drusen segmented on SD-OCT versus drusen delineated on color fundus photographs. , 2010, Investigative ophthalmology & visual science.

[31]  Edsger W. Dijkstra,et al.  A note on two problems in connexion with graphs , 1959, Numerische Mathematik.

[32]  Paul A. Viola,et al.  Rapid object detection using a boosted cascade of simple features , 2001, Proceedings of the 2001 IEEE Computer Society Conference on Computer Vision and Pattern Recognition. CVPR 2001.

[33]  Yalin Zheng,et al.  Computerized assessment of intraretinal and subretinal fluid regions in spectral-domain optical coherence tomography images of the retina. , 2013, American journal of ophthalmology.

[34]  P. Rosenfeld,et al.  Identifying the boundaries of retinal pigment epithelial detachments using two spectral-domain optical coherence tomography instruments. , 2013, Ophthalmic surgery, lasers & imaging retina.

[35]  Vladimir N. Vapnik,et al.  The Nature of Statistical Learning Theory , 2000, Statistics for Engineering and Information Science.

[36]  James G Fujimoto,et al.  AGREEMENT AND REPRODUCIBILITY OF RETINAL PIGMENT EPITHELIAL DETACHMENT VOLUMETRIC MEASUREMENTS THROUGH OPTICAL COHERENCE TOMOGRAPHY , 2015, Retina.

[37]  Semi-Automated Segmentation of Symptomatic Exudate-Associated Derangements (SEADs) in 3D OCT Using Layer Segmentation , 2010 .

[38]  Xiaodong Wu,et al.  Optimal Surface Segmentation in Volumetric Images-A Graph-Theoretic Approach , 2006, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[39]  D. Rodger,et al.  Drusen and RPE atrophy automated quantification by optical coherence tomography in an elderly population , 2015, Eye.

[40]  Robert Tibshirani,et al.  Quantitative SD-OCT imaging biomarkers as indicators of age-related macular degeneration progression. , 2014, Investigative ophthalmology & visual science.

[41]  James M. Rehg,et al.  Automated macular pathology diagnosis in retinal OCT images using multi-scale spatial pyramid and local binary patterns in texture and shape encoding , 2011, Medical Image Anal..

[42]  Amy L. Oldenburg,et al.  Automated Segmentation of Intraretinal Cystoid Fluid in Optical Coherence Tomography , 2012, IEEE Transactions on Biomedical Engineering.

[43]  Robert M. Haralick,et al.  Textural Features for Image Classification , 1973, IEEE Trans. Syst. Man Cybern..

[44]  Xinjian Chen,et al.  Three-Dimensional Segmentation of Fluid-Associated Abnormalities in Retinal OCT: Probability Constrained Graph-Search-Graph-Cut , 2012, IEEE Transactions on Medical Imaging.

[45]  William J Feuer,et al.  Comparison of geographic atrophy measurements from the OCT fundus image and the sub-RPE slab image. , 2013, Ophthalmic surgery, lasers & imaging retina.

[46]  Alauddin Bhuiyan,et al.  Progress on retinal image analysis for age related macular degeneration , 2014, Progress in Retinal and Eye Research.

[47]  E Reichel,et al.  Topography of diabetic macular edema with optical coherence tomography. , 1998, Ophthalmology.

[48]  Olga Veksler,et al.  Fast approximate energy minimization via graph cuts , 2001, Proceedings of the Seventh IEEE International Conference on Computer Vision.

[49]  Alberto Maria Segre,et al.  Programs for Machine Learning , 1994 .

[50]  Hiroshi Ishikawa,et al.  Macular segmentation with optical coherence tomography. , 2005, Investigative ophthalmology & visual science.

[51]  Qiang Chen,et al.  Automated drusen segmentation and quantification in SD-OCT images , 2013, Medical Image Anal..

[52]  Sina Farsiu,et al.  Photoreceptor layer thinning over drusen in eyes with age-related macular degeneration imaged in vivo with spectral-domain optical coherence tomography. , 2009, Ophthalmology.

[53]  Zhihong Hu,et al.  Segmentation of the geographic atrophy in spectral-domain optical coherence tomography and fundus autofluorescence images. , 2013, Investigative ophthalmology & visual science.

[54]  V. Caselles,et al.  A geometric model for active contours in image processing , 1993 .

[55]  Sina Farsiu,et al.  Drusen Volume and Retinal Pigment Epithelium Abnormal Thinning Volume Predict 2-Year Progression of Age-Related Macular Degeneration. , 2016, Ophthalmology.

[56]  Jerry L Prince,et al.  Automatic segmentation of microcystic macular edema in OCT. , 2014, Biomedical optics express.

[57]  James M. Rehg,et al.  Computerized Macular Pathology Diagnosis in Spectral Domain Optical Coherence Tomography Scans Based on Multiscale Texture and Shape Features , 2022 .

[58]  Qiang Chen,et al.  Automated geographic atrophy segmentation for SD-OCT images using region-based C-V model via local similarity factor. , 2016, Biomedical optics express.

[59]  Daisuke Iwama,et al.  Automated assessment of drusen using three-dimensional spectral-domain optical coherence tomography. , 2012, Investigative ophthalmology & visual science.

[60]  U. Schmidt-Erfurth,et al.  Performance of OCT segmentation procedures to assess morphology and extension in geographic atrophy , 2011, Acta ophthalmologica.

[61]  S. Lee,et al.  Automated characterization of pigment epithelial detachment by optical coherence tomography. , 2012, Investigative ophthalmology & visual science.

[62]  Eric L Yuan,et al.  Quantitative classification of eyes with and without intermediate age-related macular degeneration using optical coherence tomography. , 2014, Ophthalmology.

[63]  Xinjian Chen,et al.  An automated framework for 3D serous pigment epithelium detachment segmentation in SD-OCT images , 2016, Scientific Reports.

[64]  Mirza Faisal Beg,et al.  Automatic detection of subretinal fluid and sub-retinal pigment epithelium fluid in optical coherence tomography images , 2013, 2013 35th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC).

[65]  Bram van Ginneken,et al.  Automated age-related macular degeneration classification in OCT using unsupervised feature learning , 2015, Medical Imaging.

[66]  S. Sadda,et al.  DRUSEN MEASUREMENTS COMPARISON BY FUNDUS PHOTOGRAPH MANUAL DELINEATION VERSUS OPTICAL COHERENCE TOMOGRAPHY RETINAL PIGMENT EPITHELIAL SEGMENTATION AUTOMATED ANALYSIS , 2014, Retina.

[67]  Joseph A. Izatt,et al.  Automatic Drusen Segmentation and Characterization in Spectral Domain Optical Coherence Tomography (SDOCT) Images of AMD Eyes , 2008 .

[68]  D. Rubin,et al.  Semi-automatic geographic atrophy segmentation for SD-OCT images. , 2013, Biomedical optics express.

[69]  James M. Rehg,et al.  Automated Macular Pathology Diagnosis in Retinal OCT Images Using Multi-Scale Spatial Pyramid with Local Binary Patterns , 2010, MICCAI.

[70]  Yoshua Bengio,et al.  Gradient-based learning applied to document recognition , 1998, Proc. IEEE.

[71]  Christian Ahlers,et al.  Performance of drusen detection by spectral-domain optical coherence tomography. , 2010, Investigative ophthalmology & visual science.

[72]  Alex M. Andrew,et al.  Level Set Methods and Fast Marching Methods: Evolving Interfaces in Computational Geometry, Fluid Mechanics, Computer Vision, and Materials Science (2nd edition) , 2000 .

[73]  Demetri Terzopoulos,et al.  Snakes: Active contour models , 2004, International Journal of Computer Vision.

[74]  Sina Farsiu,et al.  Fast detection and segmentation of drusen in retinal optical coherence tomography images , 2008, SPIE BiOS.

[75]  Matthäus Pilch,et al.  Automated segmentation of pathological cavities in optical coherence tomography scans. , 2013, Investigative ophthalmology & visual science.

[76]  Jerry L. Prince,et al.  Snakes, shapes, and gradient vector flow , 1998, IEEE Trans. Image Process..

[77]  Stefanie G. Schuman,et al.  Spatial correlation between hyperpigmentary changes on color fundus photography and hyperreflective foci on SDOCT in intermediate AMD. , 2012, Investigative ophthalmology & visual science.

[78]  Delia Cabrera Fernandez,et al.  Delineating fluid-filled region boundaries in optical coherence tomography images of the retina , 2005, IEEE Transactions on Medical Imaging.