Koozekanani, D.[Dara],
Boyer, K.L.[Kim L.],
Roberts, C.[Cynthia],
Retinal thickness measurements from optical coherence tomography using
a markov boundary model,
MedImg(20), No. 9, September 2001, pp. 900-916.
IEEE Top Reference.
0110
BibRef
Earlier:
CVPR00(II: 363-370).
IEEE DOI
0005
BibRef
Koozekanani, D.[Dara],
Boyer, K.L.[Kim L.],
Roberts, C.[Cynthia],
Tracking the Optic Nervehead in OCT Video Using Dual Eigenspaces and an
Adaptive Vascular Distribution Model,
MedImg(22), No. 12, December 2003, pp. 1519-1536.
IEEE Abstract.
0401
BibRef
Earlier:
Add A4:
Katz, S.[Steven],
CVPR01(I:934-941).
IEEE DOI
0110
OCT: Optical Coherence Tompgraphy.
BibRef
Boyer, K.L.[Kim L.],
Herzog, A.[Artemas],
Roberts, C.[Cynthia],
Automatic Recovery of the Optic Nervehead Geometry in Optical Coherence
Tomography,
MedImg(25), No. 5, May 2006, pp. 553-570.
IEEE DOI
0605
BibRef
Earlier: A2, A1, A3:
Extracting the Optic Disk Endpoints in Optical Coherence Tomography
Data,
WACV05(I: 263-268).
IEEE DOI
0502
BibRef
Cabrera Fernandez, D.C.,
Delineating Fluid-Filled Region Boundaries in Optical Coherence
Tomography Images of the Retina,
MedImg(24), No. 8, August 2005, pp. 929-945.
IEEE DOI
0508
BibRef
Dudgeon, S.M.[Sinead M.],
Keating, D.[David],
Parks, S.[Stuart],
Simultaneous structural and functional imaging of the macula using
combined optical coherence tomography ophthalmoscope and multifocal
electroretinogram,
JOSA-A(24), No. 5, May 2007, pp. 1394-1401.
WWW Link.
0801
BibRef
Zawadzki, R.J.[Robert J.],
Choi, S.S.[Stacey S.],
Jones, S.M.[Steven M.],
Oliver, S.S.[Scot S.],
Werner, J.S.[John S.],
Adaptive optics-optical coherence tomography: optimizing visualization
of microscopic retinal structures in three dimensions,
JOSA-A(24), No. 5, May 2007, pp. 1373-1383.
WWW Link.
0801
BibRef
Burns, S.A.[Stephen A.],
Tumbar, R.[Remy],
Elsner, A.E.[Ann E.],
Ferguson, D.[Daniel],
Hammer, D.X.[Daniel X.],
Large-field-of-view, modular, stabilized, adaptive-optics-based
scanning laser ophthalmoscope,
JOSA-A(24), No. 5, May 2007, pp. 1313-1326.
WWW Link.
0801
BibRef
Bigelow, C.E.[Chad E.],
Iftimia, N.V.[Nicusor V.],
Ferguson, R.D.[R. Daniel],
Ustun, T.E.[Teoman E.],
Bloom, B.[Benjamin],
Hammer, D.X.[Daniel X.],
Compact multimodal adaptive-optics spectral-domain optical coherence
tomography instrument for retinal imaging,
JOSA-A(24), No. 5, May 2007, pp. 1327-1336.
WWW Link.
0801
BibRef
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,
MedImg(27), No. 10, October 2008, pp. 1495-1505.
IEEE DOI
0810
See also Vessel Boundary Delineation on Fundus Images Using Graph-Based Approach.
BibRef
Garvin, M.K.,
Abramoff, M.D.,
Wu, X.,
Russell, S.R.,
Burns, T.L.,
Sonka, M.,
Automated 3-D Intraretinal Layer Segmentation of Macular
Spectral-Domain Optical Coherence Tomography Images,
MedImg(28), No. 9, September 2009, pp. 1436-1447.
IEEE DOI
0909
BibRef
Lee, K.,
Niemeijer, M.,
Garvin, M.K.,
Kwon, Y.H.,
Sonka, M.,
Abramoff, M.D.,
Segmentation of the Optic Disc in 3-D OCT Scans of the Optic Nerve Head,
MedImg(29), No. 1, January 2010, pp. 159-168.
IEEE DOI
1001
BibRef
Grzywacz, N.M.,
de Juan, J.,
Ferrone, C.,
Giannini, D.,
Huang, D.,
Koch, G.,
Russo, V.,
Tan, O.,
Bruni, C.,
Statistics of Optical Coherence Tomography Data From Human Retina,
MedImg(29), No. 6, June 2010, pp. 1224-1237.
IEEE DOI
1007
BibRef
Yazdanpanah, A.,
Hamarneh, G.,
Smith, B.R.,
Sarunic, M.V.,
Segmentation of Intra-Retinal Layers From Optical Coherence Tomography
Images Using an Active Contour Approach,
MedImg(30), No. 2, February 2011, pp. 484-496.
IEEE DOI
1102
BibRef
Zhu, H.,
Crabb, D.P.,
Schlottmann, P.G.,
Wollstein, G.,
Garway-Heath, D.F.,
Aligning Scan Acquisition Circles in Optical Coherence Tomography
Images of The Retinal Nerve Fibre Layer,
MedImg(30), No. 6, June 2011, pp. 1228-1238.
IEEE DOI
1101
BibRef
Ghorbel, I.[Itebeddine],
Rossant, F.[Florence],
Bloch, I.[Isabelle],
Tick, S.[Sarah],
Paques, M.[Michel],
Automated segmentation of macular layers in OCT images and quantitative
evaluation of performances,
PR(44), No. 8, August 2011, pp. 1590-1603.
Elsevier DOI
1104
Optical coherence tomography; Retinal imaging; Automated segmentation;
Quantitative evaluation
BibRef
Molnár, J.[József],
Chetverikov, D.[Dmitry],
DeBuc, D.C.[Delia Cabrera],
Gao, W.[Wei],
Somfai, G.M.[Gábor Márk],
Layer extraction in rodent retinal images acquired by optical coherence
tomography,
MVA(23), No. 6, November 2012, pp. 1129-1139.
WWW Link.
1210
BibRef
Hu, Z.H.[Zhi-Hong],
Niemeijer, M.,
Abramoff, M.D.,
Garvin, M.K.,
Multimodal Retinal Vessel Segmentation From Spectral-Domain Optical
Coherence Tomography and Fundus Photography,
MedImg(31), No. 10, October 2012, pp. 1900-1911.
IEEE DOI
1210
See also Automated 3-D Intraretinal Layer Segmentation of Macular Spectral-Domain Optical Coherence Tomography Images.
BibRef
Golabbakhsh, M.,
Rabbani, H.,
Vessel-based registration of fundus and optical coherence tomography
projection images of retina using a quadratic registration model,
IET-IPR(7), No. 8, November 2013, pp. 768-776.
DOI Link
1402
curvelet transforms
BibRef
Sigal, I.A.,
Grimm, J.L.,
Schuman, J.S.,
Kagemann, L.,
Ishikawa, H.,
Wollstein, G.,
A Method to Estimate Biomechanics and Mechanical Properties of Optic
Nerve Head Tissues From Parameters Measurable Using Optical Coherence
Tomography,
MedImg(33), No. 6, June 2014, pp. 1381-1389.
IEEE DOI
1407
Biomechanics
BibRef
Bogunovic, H.,
Sonka, M.,
Kwon, Y.H.,
Kemp, P.,
Abramoff, M.D.,
Wu, X.D.[Xiao-Dong],
Multi-Surface and Multi-Field Co-Segmentation of 3-D Retinal Optical
Coherence Tomography,
MedImg(33), No. 12, December 2014, pp. 2242-2253.
IEEE DOI
1412
biomedical optical imaging
BibRef
Miri, M.S.,
Abramoff, M.D.,
Lee, K.,
Niemeijer, M.,
Wang, J.K.,
Kwon, Y.H.,
Garvin, M.K.,
Multimodal Segmentation of Optic Disc and Cup From SD-OCT and Color
Fundus Photographs Using a Machine-Learning Graph-Based Approach,
MedImg(34), No. 9, September 2015, pp. 1854-1866.
IEEE DOI
1509
Biomedical optical imaging
BibRef
Rossant, F.[Florence],
Bloch, I.[Isabelle],
Ghorbel, I.[Itebeddine],
Paques, M.[Michel],
Parallel Double Snakes. Application to the segmentation of retinal
layers in 2D-OCT for pathological subjects,
PR(48), No. 12, 2015, pp. 3857-3870.
Elsevier DOI
1509
BibRef
Earlier: A3, A1, A2, A4:
Modeling a parallelism constraint in active contours. Application to
the segmentation of eye vessels and retinal layers,
ICIP11(445-448).
IEEE DOI
1201
Parametric active contours
BibRef
Lagarrigue-Charbonnier, M.,
Rossant, F.[Florence],
Bloch, I.[Isabelle],
Errera, M.H.,
Paques, M.[Michel],
Segmentation of retinal vessels in adaptive optics images for
assessment of vasculitis,
IPTA16(1-6)
IEEE DOI
1703
adaptive optics
BibRef
Shi, F.,
Chen, X.,
Zhao, H.,
Zhu, W.,
Xiang, D.,
Gao, E.,
Sonka, M.,
Chen, H.,
Automated 3-D Retinal Layer Segmentation of Macular Optical Coherence
Tomography Images With Serous Pigment Epithelial Detachments,
MedImg(34), No. 2, February 2015, pp. 441-452.
IEEE DOI
1502
Diseases
BibRef
Ochoa, N.A.[Noé Alcalá],
Moreno, G.M.[Gilberto Muńoz],
Reduction of phase fluctuations in swept-source
optical coherence tomography,
SPIE(Newsroom), January 13, 2016
DOI Link
1602
Errors in the detection of phase changes by optical coherence
tomography can be reduced significantly by using volumetric recording
of images and digital image-processing techniques.
BibRef
Amini, Z.,
Rabbani, H.,
Statistical Modeling of Retinal Optical Coherence Tomography,
MedImg(35), No. 6, June 2016, pp. 1544-1554.
IEEE DOI
1606
Adaptive optics
BibRef
Samieinasab, M.,
Amini, Z.,
Rabbani, H.,
Multivariate Statistical Modeling of Retinal Optical Coherence
Tomography,
MedImg(39), No. 11, November 2020, pp. 3475-3487.
IEEE DOI
2011
Noise reduction, Retina, Data models, Mixture models, Speckle,
Analytical models,
speckle noise reduction
BibRef
Tajmirriahi, M.[Mahnoosh],
Amini, Z.[Zahra],
Hamidi, A.[Arsham],
Zam, A.[Azhar],
Rabbani, H.[Hossein],
Modeling of Retinal Optical Coherence Tomography Based on Stochastic
Differential Equations: Application to Denoising,
MedImg(40), No. 8, August 2021, pp. 2129-2141.
IEEE DOI
2108
Noise reduction, Mathematical model, Stochastic processes,
Laplace equations, Fractals, Technological innovation, Retina,
stochastic differential equation (SDE)
BibRef
Cheng, J.,
Tao, D.,
Quan, Y.,
Wong, D.W.K.,
Cheung, G.C.M.,
Akiba, M.,
Liu, J.,
Speckle Reduction in 3D Optical Coherence Tomography of Retina by
A-Scan Reconstruction,
MedImg(35), No. 10, October 2016, pp. 2270-2279.
IEEE DOI
1610
Data acquisition
BibRef
Novosel, J.,
Vermeer, K.A.,
de Jong, J.H.,
Wang, Z.,
van Vliet, L.J.,
Joint Segmentation of Retinal Layers and Focal Lesions in 3-D OCT
Data of Topologically Disrupted Retinas,
MedImg(36), No. 6, June 2017, pp. 1276-1286.
IEEE DOI
1706
Attenuation, Diseases, Image segmentation, Lesions, Level set,
Pathology, Retina, Loosely coupled level sets,
age-related macular degeneration, attenuation coefficients,
central serous retinopathy, diabetic-macular, edema
BibRef
Duan, J.M.[Jin-Ming],
Tench, C.[Christopher],
Gottlob, I.[Irene],
Proudlock, F.[Frank],
Bai, L.[Li],
Automated segmentation of retinal layers from optical coherence
tomography images using geodesic distance,
PR(72), No. 1, 2017, pp. 158-175.
Elsevier DOI
1708
BibRef
Earlier:
Optical coherence tomography image segmentation,
ICIP15(4278-4282)
IEEE DOI
1512
Optical coherence tomography (OCT)
BibRef
Wagner, M.[Marcus],
An Application of Quadratic Measure Filters to the Segmentation of
Chorio-Retinal OCT Data,
JMIV(60), No. 2, February 2018, pp. 216-231.
Springer DOI
1802
BibRef
Rasti, R.,
Rabbani, H.,
Mehridehnavi, A.,
Hajizadeh, F.,
Macular OCT Classification Using a Multi-Scale Convolutional Neural
Network Ensemble,
MedImg(37), No. 4, April 2018, pp. 1024-1034.
IEEE DOI
1804
Diabetes, Feature extraction, Imaging, Noise reduction, Pathology,
Retina, Solid modeling, CAD system,
macular pathology
BibRef
Lee, P.H.,
Chan, C.C.,
Huang, S.L.,
Chen, A.,
Chen, H.H.,
Extracting Blood Vessels From Full-Field OCT Data of Human Skin by
Short-Time RPCA,
MedImg(37), No. 8, August 2018, pp. 1899-1909.
IEEE DOI
1808
BibRef
Earlier:
Blood vessel extraction from OCT data by short-time RPCA,
ICIP16(394-398)
IEEE DOI
1610
Biomedical imaging, Skin, Face, Sparse matrices, Blood flow,
Red blood cells, Robust principal component analysis,
blood vessel detection
BibRef
Lian, J.[Jian],
Hou, S.J.[Su-Juan],
Sui, X.D.[Xiao-Dan],
Xu, F.Z.[Fang-Zhou],
Zheng, Y.J.[Yuan-Jie],
Deblurring retinal optical coherence tomography via a convolutional
neural network with anisotropic and double convolution layer,
IET-CV(12), No. 6, September 2018, pp. 900-907.
DOI Link
1808
BibRef
Röhlig, M.[Martin],
Schmidt, C.[Christoph],
Prakasam, R.K.[Ruby Kala],
Rosenthal, P.[Paul],
Schumann, H.[Heidrun],
Stachs, O.[Oliver],
Visual analysis of retinal changes with optical coherence tomography,
VC(34), No. 9, September 2018, pp. 1209-1224.
Springer DOI
1809
BibRef
Dubose, T.B.,
Cunefare, D.,
Cole, E.,
Milanfar, P.,
Izatt, J.A.,
Farsiu, S.,
Statistical Models of Signal and Noise and Fundamental Limits of
Segmentation Accuracy in Retinal Optical Coherence Tomography,
MedImg(37), No. 9, September 2018, pp. 1978-1988.
IEEE DOI
1809
Image segmentation, Retina, Probability density function, Speckle,
Imaging, Additives, Diseases, Estimation theory, Cramer-Rao bound,
biomedical imaging
BibRef
Xiang, D.H.[De-Hui],
Tian, H.H.[Hai-Hong],
Yang, X.L.[Xiao-Ling],
Shi, F.[Fei],
Zhu, W.F.[Wei-Fang],
Chen, H.Y.[Hao-Yu],
Chen, X.J.[Xin-Jian],
Automatic Segmentation of Retinal Layer in OCT Images With Choroidal
Neovascularization,
IP(27), No. 12, December 2018, pp. 5880-5891.
IEEE DOI
1810
biomedical optical imaging, diseases, feature extraction,
image classification, image segmentation,
neural network and graph search
BibRef
Wang, M.[Meng],
Zhu, W.F.[Wei-Fang],
Shi, F.[Fei],
Su, J.Z.[Jin-Zhu],
Chen, H.Y.[Hao-Yu],
Yu, K.[Kai],
Zhou, Y.[Yi],
Peng, Y.Y.[Yuan-Yuan],
Chen, Z.Y.[Zhong-Yue],
Chen, X.J.[Xin-Jian],
MsTGANet: Automatic Drusen Segmentation From Retinal OCT Images,
MedImg(41), No. 2, February 2022, pp. 394-406.
IEEE DOI
2202
Image segmentation, Retina, Feature extraction, Transformers,
Task analysis, Interference, Decoding, segmentation
BibRef
Dwork, N.,
Smith, G.T.,
Leng, T.,
Pauly, J.M.,
Bowden, A.K.,
Automatically Determining the Confocal Parameters From OCT B-Scans
for Quantification of the Attenuation Coefficients,
MedImg(38), No. 1, January 2019, pp. 261-268.
IEEE DOI
1901
Attenuation, Zirconium, Imaging, Signal to noise ratio, Scattering,
Approximation algorithms, Biomedical image processing,
optimization
BibRef
Fang, L.Y.[Le-Yuan],
Jin, Y.X.[Yu-Xuan],
Huang, L.F.[Lai-Feng],
Guo, S.[Siyu],
Zhao, G.[Guangzhe],
Chen, X.D.[Xiang-Dong],
Iterative fusion convolutional neural networks for classification of
optical coherence tomography images,
JVCIR(59), 2019, pp. 327-333.
Elsevier DOI
1903
Classification, Convolutional neural network (CNN),
Deep learning, Optical coherence tomography (OCT), Retinal
BibRef
Huang, L.F.[Lai-Feng],
He, X.X.[Xing-Xin],
Fang, L.Y.[Le-Yuan],
Rabbani, H.[Hossein],
Chen, X.D.[Xiang-Dong],
Automatic Classification of Retinal Optical Coherence Tomography
Images With Layer Guided Convolutional Neural Network,
SPLetters(26), No. 7, July 2019, pp. 1026-1030.
IEEE DOI
1906
Retina, Training, Lesions, Feature extraction, Diseases, Distortion,
Image segmentation, Optical coherence tomography (OCT),
OCT classification
BibRef
Fang, L.Y.[Le-Yuan],
Wang, C.[Chong],
Li, S.T.[Shu-Tao],
Rabbani, H.[Hossein],
Chen, X.D.[Xiang-Dong],
Liu, Z.M.[Zhi-Min],
Attention to Lesion: Lesion-Aware Convolutional Neural Network for
Retinal Optical Coherence Tomography Image Classification,
MedImg(38), No. 8, August 2019, pp. 1959-1970.
IEEE DOI
1908
Retina, Lesions, Diseases, Image classification, Convolution, Kernel,
Feature extraction, Optical coherence tomography,
image classification
BibRef
He, X.X.[Xing-Xin],
Deng, Y.[Ying],
Fang, L.Y.[Le-Yuan],
Peng, Q.H.[Qing-Hua],
Multi-Modal Retinal Image Classification with Modality-Specific
Attention Network,
MedImg(40), No. 6, June 2021, pp. 1591-1602.
IEEE DOI
2106
Retina, Deep learning, Feature extraction, Biomedical imaging,
Optical imaging, Image segmentation, Training, Fundus photography,
convolutional neural network
BibRef
Seeböck, P.,
Waldstein, S.M.,
Klimscha, S.,
Bogunovic, H.,
Schlegl, T.,
Gerendas, B.S.,
Donner, R.,
Schmidt-Erfurth, U.,
Langs, G.,
Unsupervised Identification of Disease Marker Candidates in Retinal
OCT Imaging Data,
MedImg(38), No. 4, April 2019, pp. 1037-1047.
IEEE DOI
1904
Diseases, Retina, Biomedical imaging, Training, Anomaly detection,
Task analysis, Unsupervised deep learning, anomaly detection,
optical coherence tomography
BibRef
Bogunovic, H.,
Venhuizen, F.,
Klimscha, S.,
Apostolopoulos, S.,
Bab-Hadiashar, A.,
Bagci, U.,
Beg, M.F.,
Bekalo, L.,
Chen, Q.,
Ciller, C.,
Gopinath, K.,
Gostar, A.K.,
Jeon, K.,
Ji, Z.,
Kang, S.H.,
Koozekanani, D.D.,
Lu, D.,
Morley, D.,
Parhi, K.K.,
Park, H.S.,
Rashno, A.,
Sarunic, M.,
Shaikh, S.,
Sivaswamy, J.,
Tennakoon, R.,
Yadav, S.,
de Zanet, S.,
Waldstein, S.M.,
Gerendas, B.S.,
Klaver, C.,
Sánchez, C.I.,
Schmidt-Erfurth, U.,
RETOUCH: The Retinal OCT Fluid Detection and Segmentation Benchmark
and Challenge,
MedImg(38), No. 8, August 2019, pp. 1858-1874.
IEEE DOI
1908
Retina, Image segmentation, Diseases, Biomedical imaging,
Image analysis, Fluids, Benchmark testing, Evaluation,
retina
BibRef
Liu, X.M.[Xiao-Ming],
Xu, K.[Ke],
Zhou, P.[Peng],
Chi, J.N.[Jian-Nan],
Edge detection of retinal OCT image based on complex shearlet transform,
IET-IPR(13), No. 10, 22 August 2019, pp. 1686-1693.
DOI Link
1909
BibRef
Ngo, L.,
Cha, J.,
Han, J.,
Deep Neural Network Regression for Automated Retinal Layer
Segmentation in Optical Coherence Tomography Images,
IP(29), No. , 2020, pp. 303-312.
IEEE DOI
1910
Image segmentation, Retina, Training, Image edge detection,
Deep learning, Computational complexity, Neural networks,
optical coherence tomography
BibRef
Huang, L.,
Fu, Y.,
Chen, R.,
Yang, S.,
Qiu, H.,
Wu, X.,
Zhao, S.,
Gu, Y.,
Li, P.,
SNR-Adaptive OCT Angiography Enabled by Statistical Characterization
of Intensity and Decorrelation With Multi-Variate Time Series Model,
MedImg(38), No. 11, November 2019, pp. 2695-2704.
IEEE DOI
1911
Decorrelation, Signal to noise ratio, Time series analysis,
Indexes, Mathematical model, Kernel, Angiography,
multi-variate time series
BibRef
Liang, J.[Jinbo],
Cai, J.F.[Jie-Fan],
Xie, J.P.[Jun-Peng],
Xie, X.S.[Xiang-Sheng],
Zhou, J.Y.[Jian-Ying],
Yu, X.Y.[Xiang-Yang],
Depth-resolved and auto-focus imaging through scattering layer with
wavelength compensation,
JOSA-A(36), No. 6, June 2019, pp. 944-949.
DOI Link
1912
Image metrics, Image quality, Optical coherence tomography,
Optical imaging, Spatial light modulators, Speckle patterns
BibRef
Li, A.[Anhu],
Liu, X.S.[Xing-Sheng],
Gong, W.[Wei],
Sun, W.S.[Wan-Song],
Sun, J.F.[Jian-Feng],
Prelocation image stitching method based on flexible and precise
boresight adjustment using Risley prisms,
JOSA-A(36), No. 2, February 2019, pp. 305-311.
DOI Link
1912
CCD cameras, Image quality, Image registration,
Imaging techniques, Medical imaging, Optical coherence tomography
BibRef
Mishra, S.S.,
Mandal, B.,
Puhan, N.B.,
Multi-Level Dual-Attention Based CNN for Macular Optical Coherence
Tomography Classification,
SPLetters(26), No. 12, December 2019, pp. 1793-1797.
IEEE DOI
2001
diseases, feature extraction, image classification,
learning (artificial intelligence), medical image processing,
optical coherence tomography (OCT)
BibRef
Seeböck, P.,
Orlando, J.I.,
Schlegl, T.,
Waldstein, S.M.,
Bogunovic, H.,
Klimscha, S.,
Langs, G.,
Schmidt-Erfurth, U.,
Exploiting Epistemic Uncertainty of Anatomy Segmentation for Anomaly
Detection in Retinal OCT,
MedImg(39), No. 1, January 2020, pp. 87-98.
IEEE DOI
2001
BibRef
And:
Correction:
MedImg(39), No. 4, April 2020, pp. 1291-1291.
IEEE DOI
2004
Retina, Uncertainty, Diseases, Anomaly detection, Image segmentation,
Biomarkers, Training, Weakly supervised learning,
epistemic uncertainty
BibRef
Wang, P.Y.[Peng-Yu],
Zhu, H.Q.[Hong-Qing],
Ling, X.F.[Xiao-Feng],
Intravascular optical coherence tomography image segmentation based on
Gaussian mixture model and adaptive fourth-order PDE,
SIViP(14), No. 1, February 2020, pp. 29-37.
WWW Link.
2001
BibRef
Pan, L.,
Shi, F.,
Xiang, D.,
Yu, K.,
Duan, L.,
Zheng, J.,
Chen, X.,
OCTRexpert: A Feature-Based 3D Registration Method for Retinal OCT
Images,
IP(29), 2020, pp. 3885-3897.
IEEE DOI
2002
Image registration, optical coherence tomography (OCT), retinal image
BibRef
Zhang, H.Q.[Hua-Qi],
Wang, G.L.[Guang-Lei],
Li, Y.[Yan],
Wang, H.R.[Hong-Rui],
Faster R-CNN, fourth-order partial differential equation and
global-local active contour model (FPDE-GLACM) for plaque segmentation
in IV-OCT image,
SIViP(14), No. 3, April 2020, pp. 509-517.
WWW Link.
2004
BibRef
Zhang, J.,
Qiao, Y.,
Sarabi, M.S.,
Khansari, M.M.,
Gahm, J.K.,
Kashani, A.H.,
Shi, Y.,
3D Shape Modeling and Analysis of Retinal Microvasculature in
OCT-Angiography Images,
MedImg(39), No. 5, May 2020, pp. 1335-1346.
IEEE DOI
2005
Shape, Retina, Surface reconstruction,
Surface treatment, Image reconstruction, Solid modeling,
shape analysis
BibRef
Daneshmand, P.G.,
Rabbani, H.,
Mehridehnavi, A.,
Super-Resolution of Optical Coherence Tomography Images by Scale
Mixture Models,
IP(29), 2020, pp. 5662-5676.
IEEE DOI
2005
Image reconstruction, GSM, Spatial resolution, Signal resolution,
Mixture models, Estimation, Optical coherent tomography (OCT),
Gaussian/Laplacia scale mixture
BibRef
Amini, Z.,
Rabbani, H.,
Selesnick, I.,
Sparse Domain Gaussianization for Multi-Variate Statistical Modeling
of Retinal OCT Images,
IP(29), 2020, pp. 6873-6884.
IEEE DOI
2007
Transforms, Hidden Markov models, Correlation, Data models,
Probability density function, Noise reduction,
denoising
BibRef
Das, V.,
Prabhakararao, E.,
Dandapat, S.,
Bora, P.K.,
B-Scan Attentive CNN for the Classification of Retinal Optical
Coherence Tomography Volumes,
SPLetters(27), 2020, pp. 1025-1029.
IEEE DOI
2007
Retina, Pathology, Feature extraction,
Diseases, Imaging, Reliability, Attention, classification,
optical coherence tomography (OCT) volume
BibRef
Girish, G.N.,
Kothari, A.R.[Abhishek R.],
Rajan, J.[Jeny],
Marker controlled watershed transform for intra-retinal cysts
segmentation from optical coherence tomography B-scans,
PRL(139), 2020, pp. 86-94.
Elsevier DOI
2011
Optical coherence tomography, Segmentation, Retinal cyst,
Cystoid macular edema, -means clustering, Watershed transformation
BibRef
Shehryar, T.[Tehmina],
Akram, M.U.[Muhammad U.],
Khalid, S.[Samina],
Nasreen, S.[Shamila],
Tariq, A.[Anum],
Perwaiz, A.[Aqib],
Shaukat, A.[Arslan],
Improved automated detection of glaucoma by correlating fundus and
SD-OCT image analysis,
IJIST(30), No. 4, 2020, pp. 1046-1065.
DOI Link
2011
biomedical informatics, telemedicine, computer aided diagnosis,
fundus, glaucoma, optical coherence tomography
BibRef
Chen, R.,
Yao, L.,
Liu, K.,
Cao, T.,
Li, H.,
Li, P.,
Improvement of Decorrelation-Based OCT Angiography by an Adaptive
Spatial-Temporal Kernel in Monitoring Stimulus-Evoked Hemodynamic
Responses,
MedImg(39), No. 12, December 2020, pp. 4286-4296.
IEEE DOI
2012
Decorrelation, Estimation, Hemodynamics, Uncertainty, Kernel,
Dynamic range, Medical and biological imaging,
optical coherence tomography angiography
BibRef
Kande, N.A.,
Dakhane, R.,
Dukkipati, A.,
Yalavarthy, P.K.,
SiameseGAN: A Generative Model for Denoising of Spectral Domain
Optical Coherence Tomography Images,
MedImg(40), No. 1, January 2021, pp. 180-192.
IEEE DOI
2012
Noise reduction, Feature extraction,
Signal to noise ratio, Generators, deep generative model
BibRef
Koresh, H.J.D.[H. James Deva],
Chacko, S.[Shanty],
Periyanayagi, M.,
A modified capsule network algorithm for oct corneal image
segmentation,
PRL(143), 2021, pp. 104-112.
Elsevier DOI
2102
Corneal OCT segmentation, Corneal OCT classification,
Speckle noise reduction, Segmentation capsules, Classification capsules
BibRef
Daneshmand, P.G.,
Mehridehnavi, A.,
Rabbani, H.,
Reconstruction of Optical Coherence Tomography Images Using Mixed Low
Rank Approximation and Second Order Tensor Based Total Variation
Method,
MedImg(40), No. 3, March 2021, pp. 865-878.
IEEE DOI
2103
Tensors, Image reconstruction, Noise reduction, Correlation, Retina,
Redundancy, Approximation algorithms,
nuclear norm
BibRef
Danesh, H.[Hajar],
Maghooli, K.[Keivan],
Dehghani, A.[Alireza],
Kafieh, R.[Rahele],
Automatic production of synthetic labelled OCT images using an active
shape model,
IET-IPR(14), No. 15, 15 December 2020, pp. 3812-3818.
DOI Link
2103
BibRef
Wang, M.,
Zhu, W.,
Yu, K.,
Chen, Z.,
Shi, F.,
Zhou, Y.,
Ma, Y.,
Peng, Y.,
Bao, D.,
Feng, S.,
Ye, L.,
Xiang, D.,
Chen, X.,
Semi-Supervised Capsule cGAN for Speckle Noise Reduction in Retinal
OCT Images,
MedImg(40), No. 4, April 2021, pp. 1168-1183.
IEEE DOI
2104
Speckle, Noise reduction, Retina, Task analysis, Data models,
Semisupervised learning, Heuristic algorithms, Semi-supervision,
speckle noise
BibRef
Meng, Q.Q.[Qing-Quan],
Zuo, C.[Chang],
Shi, F.[Fei],
Zhu, W.F.[Wei-Fang],
Xiang, D.[Dehui],
Chen, H.Y.[Hao-Yu],
Chen, X.J.[Xin-Jian],
Three-dimensional choroid neovascularization growth prediction from
longitudinal retinal OCT images based on a hybrid model,
PRL(146), 2021, pp. 108-114.
Elsevier DOI
2105
Choroid neovascularization, Growth prediction,
Reaction-diffusion model, Hyperelastic biomechanical model,
Optical coherence tomography
BibRef
Huang, Y.Q.[Yong-Qiang],
Xia, W.J.[Wen-Jun],
Lu, Z.X.[Ze-Xin],
Liu, Y.[Yan],
Chen, H.[Hu],
Zhou, J.[Jiliu],
Fang, L.Y.[Le-Yuan],
Zhang, Y.[Yi],
Noise-Powered Disentangled Representation for Unsupervised Speckle
Reduction of Optical Coherence Tomography Images,
MedImg(40), No. 10, October 2021, pp. 2600-2614.
IEEE DOI
2110
Noise measurement, Speckle, Generators, Noise reduction,
Image reconstruction, Image denoising,
unsupervised learning
BibRef
Sitenko, D.[Dmitrij],
Boll, B.[Bastian],
Schnörr, C.[Christoph],
Assignment Flow for Order-Constrained OCT Segmentation,
IJCV(129), No. 11, November 2021, pp. 3088-3118.
Springer DOI
2110
BibRef
Earlier:
GCPR20(58-71).
Springer DOI
2110
BibRef
Liu, X.M.[Xiao-Ming],
Wang, S.C.[Shao-Cheng],
Cao, J.[Jun],
Zhang, Y.[Ying],
Wang, M.[Man],
Uncertainty-guided self-ensembling model for semi-supervised
segmentation of multiclass retinal fluid in optical coherence
tomography images,
IJIST(32), No. 1, 2022, pp. 369-386.
DOI Link
2201
attention mechanism, deep learning, fluid region segmentation,
optical coherence tomography, semi-supervised, uncertainty
BibRef
Hao, J.K.[Jin-Kui],
Li, F.[Fei],
Hao, H.Y.[Hua-Ying],
Fu, H.Z.[Hua-Zhu],
Xu, Y.[Yanwu],
Higashita, R.[Risa],
Zhang, X.L.[Xiu-Lan],
Liu, J.[Jiang],
Zhao, Y.T.[Yi-Tian],
Hybrid Variation-Aware Network for Angle-Closure Assessment in AS-OCT,
MedImg(41), No. 2, February 2022, pp. 254-265.
IEEE DOI
2202
Iris, Feature extraction, Lighting,
Picture archiving and communication systems, Visualization,
AS-OCT
BibRef
He, X.X.[Xing-Xin],
Fang, L.Y.[Le-Yuan],
Tan, M.K.[Ming-Kui],
Chen, X.D.[Xiang-Dong],
Intra- and Inter-Slice Contrastive Learning for Point Supervised OCT
Fluid Segmentation,
IP(31), 2022, pp. 1870-1881.
IEEE DOI
2202
Fluids, Image segmentation, Retina, Annotations, Task analysis,
Proposals, Convolutional neural networks,
contrastive learning
BibRef
Nabijiang, M.[Maidina],
Wan, X.[Xinjuan],
Huang, S.S.[Sheng-Song],
Liu, Q.[Qi],
Wei, B.[Bixia],
Zhu, J.N.[Jia-Ning],
Xie, X.D.[Xiao-Dong],
BAM: Block attention mechanism for OCT image classification,
IET-IPR(16), No. 5, 2022, pp. 1376-1388.
DOI Link
2203
BibRef
Mozumder, M.[Meghdoot],
Hauptmann, A.[Andreas],
Nissilä, I.[Ilkka],
Arridge, S.R.[Simon R.],
Tarvainen, T.[Tanja],
A Model-Based Iterative Learning Approach for Diffuse Optical
Tomography,
MedImg(41), No. 5, May 2022, pp. 1289-1299.
IEEE DOI
2205
Mathematical models, Image reconstruction, Inverse problems,
Absorption, Tomography, absolute imaging
BibRef
Xing, G.[Gang],
Chen, L.[Li],
Wang, H.[Hualin],
Zhang, J.[Jiong],
Sun, D.[Dongke],
Xu, F.[Feng],
Lei, J.Q.[Jian-Qin],
Xu, X.[Xiayu],
Multi-Scale Pathological Fluid Segmentation in OCT With a Novel
Curvature Loss in Convolutional Neural Network,
MedImg(41), No. 6, June 2022, pp. 1547-1559.
IEEE DOI
2206
Fluids, Lesions, Retina, Image segmentation, Shape, Loss measurement,
Pathology, Image segmentation, loss function,
pathological fluid
BibRef
Rubinoff, I.[Ian],
Miller, D.A.[David A.],
Kuranov, R.[Roman],
Wang, Y.B.[Yuan-Bo],
Fang, R.[Raymond],
Volpe, N.J.[Nicholas J.],
Zhang, H.F.[Hao F.],
High-Speed Balanced-Detection Visible-Light Optical Coherence
Tomography in the Human Retina Using Subpixel Spectrometer
Calibration,
MedImg(41), No. 7, July 2022, pp. 1724-1734.
IEEE DOI
2207
Imaging, Retina, Floors, Calibration, Band-pass filters,
Optical fiber polarization, Optical fiber dispersion, retina
BibRef
Miao, H.Y.[Han-Yuan],
Zhou, X.H.[Xiao-Hong],
Wang, W.[Wei],
Jiang, W.L.[Wei-Liang],
Jin, T.[Tao],
Improved sparse representation algorithm for optical coherence
tomography images,
IJIST(32), No. 4, 2022, pp. 1286-1293.
DOI Link
2207
K-SVD algorithm, optical coherence tomography,
piecewise orthogonal matching pursuit algorithm, speckle noise
BibRef
Bayhaqi, Y.A.[Yakub A.],
Hamidi, A.[Arsham],
Canbaz, F.[Ferda],
Navarini, A.A.[Alexander A.],
Cattin, P.C.[Philippe C.],
Zam, A.[Azhar],
Deep-Learning-Based Fast Optical Coherence Tomography (OCT) Image
Denoising for Smart Laser Osteotomy,
MedImg(41), No. 10, October 2022, pp. 2615-2628.
IEEE DOI
2210
Laser ablation, Speckle, Laser feedback, Image denoising,
Artificial neural networks, Noise reduction, Laser beam cutting,
optical coherence tomography
BibRef
Geng, M.F.[Mu-Feng],
Meng, X.X.[Xiang-Xi],
Zhu, L.[Lei],
Jiang, Z.[Zhe],
Gao, M.[Mengdi],
Huang, Z.[Zhiyu],
Qiu, B.[Bin],
Hu, Y.C.[Yi-Cheng],
Zhang, Y.[Yibao],
Ren, Q.S.[Qiu-Shi],
Lu, Y.[Yanye],
Triplet Cross-Fusion Learning for Unpaired Image Denoising in Optical
Coherence Tomography,
MedImg(41), No. 11, November 2022, pp. 3357-3372.
IEEE DOI
2211
Noise reduction, Image denoising, Noise measurement, Speckle,
Training, Task analysis, Biomedical imaging, Unpaired learning,
image restoration
BibRef
He, X.X.[Xing-Xin],
Zhong, Z.[Zhun],
Fang, L.Y.[Le-Yuan],
He, M.[Min],
Sebe, N.[Nicu],
Structure-Guided Cross-Attention Network for Cross-Domain OCT Fluid
Segmentation,
IP(32), 2023, pp. 309-320.
IEEE DOI
2301
Fluids, Retina, Adaptation models, Task analysis,
Semantic segmentation, Training, Performance evaluation, retinal structure
BibRef
Wu, J.[Jun],
Zhang, Y.X.[Ya-Xin],
Xiao, Z.[Zhitao],
Zhang, F.[Fang],
Geng, L.[Lei],
Automated segmentation of diabetic macular edema in OCT B-scan images
based on RCU-Net,
IJIST(33), No. 1, 2023, pp. 299-311.
DOI Link
2301
deep learning, diabetic macular edema, image segmentation,
optical coherence tomography
BibRef
Mathews, M.R.[Mili Rosline],
Anzar, S.T.M.[Sharafudeen Thaha Mohammed],
A lightweight deep learning model for retinal optical coherence
tomography image classification,
IJIST(33), No. 1, 2023, pp. 204-216.
DOI Link
2301
convolutional neural network, deep learning, image processing,
optical coherence tomography, retinal diseases
BibRef
Niederleithner, M.,
de Sisternes, L.,
Stino, H.,
Sedova, A.,
Schlegl, T.,
Bagherinia, H.,
Britten, A.,
Matten, P.,
Schmidt-Erfurth, U.,
Pollreisz, A.,
Drexler, W.,
Leitgeb, R.A.,
Schmoll, T.,
Ultra-Widefield OCT Angiography,
MedImg(42), No. 4, April 2023, pp. 1009-1020.
IEEE DOI
2304
Retina, Optical imaging, Measurement by laser beam,
Optical interferometry, Physics, Biomedical engineering, ophthalmic imaging
BibRef
Niu, S.[Sijie],
Xing, R.[Ruiwen],
Gao, X.[Xizhan],
Liu, T.T.[Ting-Ting],
Chen, Y.H.[Yue-Hui],
A fine-to-coarse-to-fine weakly supervised framework for volumetric
SD-OCT image segmentation,
IET-CV(17), No. 2, 2023, pp. 123-134.
DOI Link
2304
BibRef
Rasti, R.[Reza],
Biglari, A.[Armin],
Rezapourian, M.[Mohammad],
Yang, Z.Y.[Zi-Yun],
Farsiu, S.[Sina],
RetiFluidNet: A Self-Adaptive and Multi-Attention Deep Convolutional
Network for Retinal OCT Fluid Segmentation,
MedImg(42), No. 5, May 2023, pp. 1413-1423.
IEEE DOI
2305
Fluids, Retina, Image segmentation, Visualization, Task analysis,
Optimization, Lesions, Medical image segmentation, fluid segmentation
BibRef
Pappu, G.P.[Geetha Pavani],
Tankala, S.[Sreekar],
Talabhaktula, K.[Krishna],
Biswal, B.[Birendra],
EANet: Multiscale autoencoder based edge attention network for fluid
segmentation from SD-OCT images,
IJIST(33), No. 3, 2023, pp. 909-927.
DOI Link
2305
autoencoder, edge enhancement, macular edema,
multiscale attention mechanism, retinal fluid segmentation
BibRef
Ganjee, R.[Razieh],
Moghaddam, M.E.[Mohsen Ebrahimi],
Nourinia, R.[Ramin],
A generalizable approach based on the U-Net model for automatic
intraretinal cyst segmentation in SD-OCT images,
IJIST(33), No. 5, 2023, pp. 1647-1660.
DOI Link
2310
intraretinal cyst, optical coherence tomography (OCT),
segmentation, U-Net
BibRef
Yang, B.[Bing],
Zhang, X.Q.[Xiao-Qing],
Li, S.Q.[San-Qian],
Higashita, R.[Risa],
Liu, J.[Jiang],
HA-Net: Hierarchical Attention Network Based on Multi-Task Learning
for Ciliary Muscle Segmentation in AS-OCT,
SPLetters(30), 2023, pp. 1342-1346.
IEEE DOI
2310
BibRef
Kugelman, J.[Jason],
Alonso-Caneiro, D.[David],
Read, S.A.[Scott A.],
Vincent, S.J.[Stephen J.],
Collins, M.J.[Michael J.],
Enhanced OCT chorio-retinal segmentation in low-data settings with
semi-supervised GAN augmentation using cross-localisation,
CVIU(237), 2023, pp. 103852.
Elsevier DOI
2311
BibRef
Earlier:
OCT chorio-retinal segmentation with adversarial loss,
DICTA21(01-08)
IEEE DOI
2201
Generative adversarial networks, Semi-supervised learning,
Deep learning, OCT, Machine learning, GANs, Data augmentation.
Training, Image segmentation, Image analysis,
Optical coherence tomography, Semantics, neural networks
BibRef
Shen, Y.[Yuhe],
Li, J.[Jiang],
Zhu, W.F.[Wei-Fang],
Yu, K.[Kai],
Wang, M.[Meng],
Peng, Y.Y.[Yuan-Yuan],
Zhou, Y.[Yi],
Guan, L.[Liling],
Chen, X.J.[Xin-Jian],
Graph Attention U-Net for Retinal Layer Surface Detection and Choroid
Neovascularization Segmentation in OCT Images,
MedImg(42), No. 11, November 2023, pp. 3140-3154.
IEEE DOI
2311
BibRef
Xu, X.[Xiayu],
Yang, P.W.[Pei-Wei],
Wang, H.L.[Hua-Lin],
Xiao, Z.F.[Zhan-Feng],
Xing, G.[Gang],
Zhang, X.L.[Xiu-Lan],
Wang, W.[Wei],
Xu, F.[Feng],
Zhang, J.[Jiong],
Lei, J.Q.[Jian-Qin],
AV-casNet: Fully Automatic Arteriole-Venule Segmentation and
Differentiation in OCT Angiography,
MedImg(42), No. 2, February 2023, pp. 481-492.
IEEE DOI
2302
Image segmentation, Retina, Deep learning,
Convolutional neural networks, Classification algorithms,
graph neural network
BibRef
Ni, S.B.[Shui-Bin],
Nguyen, T.T.P.[Thanh-Tin P.],
Ng, R.[Ringo],
Woodward, M.[Mani],
Ostmo, S.[Susan],
Jia, Y.[Yali],
Chiang, M.F.[Michael F.],
Huang, D.[David],
Skalet, A.H.[Alison H.],
Campbell, J.P.[J. Peter],
Jian, Y.F.[Yi-Fan],
Panretinal Optical Coherence Tomography,
MedImg(42), No. 11, November 2023, pp. 3219-3228.
IEEE DOI
2311
BibRef
Tan, Z.W.[Zhi-Wei],
Shi, F.[Fei],
Zhou, Y.[Yi],
Wang, J.C.[Jing-Cheng],
Wang, M.[Meng],
Peng, Y.Y.[Yuan-Yuan],
Xu, K.[Kai],
Liu, M.[Ming],
Chen, X.J.[Xin-Jian],
A Multi-Scale Fusion and Transformer Based Registration Guided
Speckle Noise Reduction for OCT Images,
MedImg(43), No. 1, January 2024, pp. 473-488.
IEEE DOI
2401
BibRef
Razavi, R.[Raha],
Plonka, G.[Gerlind],
Rabbani, H.[Hossein],
X-Let's Atom Combinations for Modeling and Denoising of OCT Images by
Modified Morphological Component Analysis,
MedImg(43), No. 2, February 2024, pp. 760-770.
IEEE DOI
2402
Transforms, Noise reduction, Mathematical models,
Discrete cosine transforms, Analytical models, X-let transforms
BibRef
Tan, Y.[Yubo],
Shen, W.D.[Wen-Da],
Wu, M.Y.[Ming-Yuan],
Liu, G.N.[Gui-Na],
Zhao, S.X.[Shi-Xuan],
Chen, Y.[Yang],
Yang, K.F.[Kai-Fu],
Li, Y.J.[Yong-Jie],
Retinal Layer Segmentation in OCT Images With Boundary Regression and
Feature Polarization,
MedImg(43), No. 2, February 2024, pp. 686-700.
IEEE DOI Code:
WWW Link.
2402
Image segmentation, Retina, Transformers,
Convolutional neural networks, Biomedical imaging, boundary regression
BibRef
Prabha, A.J.[A. Jeya],
Fathimal, M.S.[M. Sameera],
Meghana, G.R.,
Kirubha, S.P.A.[S. P. Angeline],
Application program interface for automatic segmentation of retinal
layers and fluids in Optical Coherence Tomography - Neovascular Age
related Macular degeneration retinal images using deep learning
models,
IJIST(34), No. 2, 2024, pp. e23002.
DOI Link
2402
AMD, deep learning, OCT, retinal fluids, retinal layers
BibRef
Fu, L.W.[Li-Wei],
Liu, C.H.[Chih-Hao],
Jain, M.[Manu],
Chen, C.S.J.[Chih-Shan Jason],
Wu, Y.H.[Yu-Hung],
Huang, S.L.[Sheng-Lung],
Chen, H.H.[Homer H.],
Training With Uncertain Annotations for Semantic Segmentation of
Basal Cell Carcinoma From Full-Field OCT Images,
MedImg(43), No. 3, March 2024, pp. 1060-1070.
IEEE DOI
2403
Annotations, Training, Biological system modeling,
Semantic segmentation, Data models, Biomedical imaging,
basal cell carcinoma segmentation
BibRef
Challoob, M.[Mohsin],
Gao, Y.S.[Yong-Sheng],
Busch, A.[Andrew],
Distinctive Phase Interdependency Model for Retinal Vasculature
Delineation in OCT-Angiography Images,
MedImg(43), No. 3, March 2024, pp. 1018-1032.
IEEE DOI
2403
Retina, Feature extraction, Image segmentation, Diseases, Faces,
Wavelet coefficients, Task analysis, Retinal microvasculature,
optical coherence tomography angiography
BibRef
Shen, H.L.[Hai-Lan],
Tang, Z.[Zheng],
Li, Y.J.[Ya-Jing],
Duan, X.C.[Xuan-Chu],
Chen, Z.L.[Zai-Liang],
HAIC-NET: Semi-supervised OCTA vessel segmentation with
self-supervised pretext task and dual consistency training,
PR(151), 2024, pp. 110429.
Elsevier DOI
2404
Optical Coherence Tomography Angiography.
Vessel segmentation, Semi-supervised learning, Self-supervised pretext task,
Consistency regularization, Topological connectivity
BibRef
Kreitner, L.[Linus],
Paetzold, J.C.[Johannes C.],
Rauch, N.[Nikolaus],
Chen, C.[Chen],
Hagag, A.M.[Ahmed M.],
Fayed, A.E.[Alaa E.],
Sivaprasad, S.[Sobha],
Rausch, S.[Sebastian],
Weichsel, J.[Julian],
Menze, B.H.[Bjoern H.],
Harders, M.[Matthias],
Knier, B.[Benjamin],
Rueckert, D.[Daniel],
Menten, M.J.[Martin J.],
Synthetic Optical Coherence Tomography Angiographs for Detailed
Retinal Vessel Segmentation Without Human Annotations,
MedImg(43), No. 6, June 2024, pp. 2061-2073.
IEEE DOI
2406
Image segmentation, Adaptation models, Data models, Annotations,
Biomedical imaging, Training, Pipelines, Blood vessels, transfer learning
BibRef
Ni, G.M.[Guang-Ming],
Wu, R.[Renxiong],
Zheng, F.[Fei],
Li, M.[Meixuan],
Huang, S.[Shaoyan],
Ge, X.[Xin],
Liu, L.[Linbo],
Liu, Y.[Yong],
Toward Ground-Truth Optical Coherence Tomography via
Three-Dimensional Unsupervised Deep Learning Processing and Data,
MedImg(43), No. 6, June 2024, pp. 2395-2407.
IEEE DOI Code:
WWW Link.
2406
Speckle, Imaging, Noise measurement, Deep learning,
Spatial resolution, Training, Optical coherence tomography, volumetric data
BibRef
Zhao, X.[Xin],
Zhang, J.[Jing],
Li, Q.[Qiaozhe],
Zhao, T.F.[Teng-Fei],
Li, Y.[Yi],
Wu, Z.[Zifeng],
Global and local multi-modal feature mutual learning for retinal
vessel segmentation,
PR(151), 2024, pp. 110376.
Elsevier DOI
2404
Mutual learning, Multi-modal learning, OCTA images, Retinal vessel segmentation
BibRef
Daneshmand, P.G.[Parisa Ghaderi],
Rabbani, H.[Hossein],
Tensor Ring Decomposition Guided Dictionary Learning for OCT Image
Denoising,
MedImg(43), No. 7, July 2024, pp. 2547-2562.
IEEE DOI
2407
Tensors, Noise reduction, Image denoising, Correlation,
Solid modeling, Dictionaries, Optical coherent tomography (OCT), denoising
BibRef
Miller, D.A.[David A.],
Grannonico, M.[Marta],
Liu, M.[Mingna],
Savier, E.[Elise],
McHaney, K.[Kara],
Erisir, A.[Alev],
Netland, P.A.[Peter A.],
Cang, J.H.[Jian-Hua],
Liu, X.R.[Xiao-Rong],
Zhang, H.F.[Hao F.],
Visible-Light Optical Coherence Tomography Fibergraphy of the Tree
Shrew Retinal Ganglion Cell Axon Bundles,
MedImg(43), No. 8, August 2024, pp. 2769-2777.
IEEE DOI
2408
Retina, Vegetation, Dispersion, Animals, Speckle, Imaging,
Optical imaging, Animal models, optical coherence tomography, tree shrew
BibRef
Wang, R.F.[Rui-Feng],
Zhang, G.[Guang],
Xi, X.M.[Xiao-Ming],
Xu, L.S.[Long-Sheng],
Nie, X.[Xiushan],
Nie, J.H.[Jian-Hua],
Meng, X.J.[Xian-Jing],
Zhang, Y.W.[Yan-Wei],
Chen, X.J.[Xin-Jian],
Yin, Y.L.[Yi-Long],
Discriminative atoms embedding relation dual network for
classification of choroidal neovascularization in OCT images,
PR(156), 2024, pp. 110757.
Elsevier DOI
2408
Choroidal neovascularization, Optical coherence tomography,
CNV classification, Semi-supervised learning
BibRef
Chakravarty, A.[Arunava],
Emre, T.[Taha],
Leingang, O.[Oliver],
Riedl, S.[Sophie],
Mai, J.[Julia],
Scholl, H.P.N.[Hendrik P. N.],
Sivaprasad, S.[Sobha],
Rueckert, D.[Daniel],
Lotery, A.[Andrew],
Schmidt-Erfurth, U.[Ursula],
Bogunovic, H.[Hrvoje],
Morph-SSL: Self-Supervision With Longitudinal Morphing for
Forecasting AMD Progression From OCT Volumes,
MedImg(43), No. 9, September 2024, pp. 3224-3239.
IEEE DOI
2409
Training, Retina, Task analysis, Feature extraction,
Biomedical imaging, Biomarkers, longitudinal OCT
BibRef
Emre, T.[Taha],
Chakravarty, A.[Arunava],
Rivail, A.[Antoine],
Lachinov, D.[Dmitrii],
Leingang, O.[Oliver],
Riedl, S.[Sophie],
Mai, J.[Julia],
Scholl, H.P.N.[Hendrik P. N.],
Sivaprasad, S.[Sobha],
Rueckert, D.[Daniel],
Lotery, A.[Andrew],
Schmidt-Erfurth, U.[Ursula],
Bogunovic, H.[Hrvoje],
3DTINC: Time-Equivariant Non-Contrastive Learning for Predicting
Disease Progression From Longitudinal OCTs,
MedImg(43), No. 9, September 2024, pp. 3200-3210.
IEEE DOI
2409
Task analysis, Diseases, Retina, Biomedical imaging,
Deep learning, Self-supervised learning, retina
BibRef
Yadav, M.K.S.[Mithilesh Kumar Singh],
Singh, N.P.[Nagendra Pratap],
A Survey on Optical Coherence Tomography,
ICCVMI23(1-6)
IEEE DOI
2403
Surveys, Optical interferometry, Statistical analysis,
Reviews, Optical coherence tomography, Emerging trends
BibRef
Liu, Z.J.[Zheng-Ji],
Law, T.K.[Tsz-Kin],
Li, J.Z.[Ji-Zhou],
To, C.H.[Chi-Ho],
Chun, R.K.M.[Rachel Ka-Man],
Self-Supervised Denoising of Optical Coherence Tomography with
Inter-Frame Representation,
ICIP23(3334-3338)
IEEE DOI
2312
BibRef
Wen, E.[Evan],
Sorenson, R.[ReBecca],
Ehrlich, M.[Max],
Relax: Retinal Layer Attribution for Guided Explanations of Automated
Optical Coherence Tomography Classification,
MCV22(236-251).
Springer DOI
2304
BibRef
Zhao, C.[Chen],
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Lumen Segmentation in OCT Images,
ICIP22(3106-3110)
IEEE DOI
2211
Geometry, Heart, Image segmentation, Shape,
Optical coherence tomography, Stars, High-resolution imaging, Mask-RCNN
BibRef
Liu, X.M.[Xiao-Ming],
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OCTA Retinal Vessel Segmentation Based on Vessel Thickness
Inconsistency Loss,
ICIP22(2676-2680)
IEEE DOI
2211
Image segmentation, Statistical analysis, Retinopathy,
Optical coherence tomography, Angiography, Blindness,
thickness inconsistency
BibRef
Liu, X.M.[Xiao-Ming],
Zhang, D.[Di],
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Tang, J.S.[Jin-Shan],
VCT-NET: An OCTA Retinal Vessel Segmentation Network Based on
Convolution and Transformer,
ICIP22(2656-2660)
IEEE DOI
2211
Deep learning, Image segmentation, Convolution,
Optical coherence tomography, Image edge detection, Transformers, transformer
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Rapp, J.,
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Orlik, P.,
Koike-Akino, T.,
Parsons, K.,
Maximum Likelihood Surface Profilometry Via Optical coherence
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ICIP22(1561-1565)
IEEE DOI
2211
Reflectivity, Maximum likelihood estimation,
Surface reconstruction, Interpolation,
maximum likelihood estimation
BibRef
Logan, Y.Y.[Yash-Yee],
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ICIP22(3908-3912)
IEEE DOI
2211
Learning systems, Uncertainty, Medical services, Robustness,
Classification algorithms, Medical diagnostic imaging,
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Razavi, R.[Raha],
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Plonka, G.[Gerlind],
Combining Non-Data-Adaptive Transforms for OCT Image Denoising by
Iterative Basis Pursuit,
ICIP22(2351-2355)
IEEE DOI
2211
TV, Optical coherence tomography, Noise reduction, Speckle,
Feature extraction, Discrete wavelet transforms,
Dual Basis Pursuit Denoising
BibRef
Wang, Y.Q.[Yi-Qian],
Galang, C.[Carlo],
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Joint Motion Correction and 3D Segmentation with Graph-Assisted
Neural Networks for Retinal OCT,
ICIP22(766-770)
IEEE DOI
2211
Image segmentation, Visualization, Motion segmentation,
Optical coherence tomography, Neural networks, Retina, retinal imaging
BibRef
Bhattarai, A.[Ashuta],
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Cu-Net: Towards Continuous Multi-Class Contour Detection for Retinal
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ICIP22(3833-3837)
IEEE DOI
2211
Image segmentation, Pediatrics, Pathology, Interpolation,
Optical coherence tomography, Coherence, Benchmark testing,
retinal layer segmentation
BibRef
Tian, X.[Xin],
Anantrasirichai, N.[Nantheera],
Nicholson, L.[Lindsay],
Achim, A.[Alin],
Optimal Transport-Based Graph Matching for 3D Retinal OCT Image
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ICIP22(2791-2795)
IEEE DOI
2211
Image segmentation, Image registration, Limiting,
Optical coherence tomography, Imaging, Retina,
mouse OCT
BibRef
Kokilepersaud, K.[Kiran],
Prabhushankar, M.[Mohit],
AlRegib, G.[Ghassan],
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Wykoff, C.[Charles],
Gradient-Based Severity Labeling for Biomarker Classification in OCT,
ICIP22(3416-3420)
IEEE DOI
2211
Training, Retinopathy, Biomarkers, Retina, Diabetes,
Classification algorithms, Retinal Biomarkers,
Gradients
BibRef
Ren, J.X.[Jia-Xiang],
Park, K.[Kicheon],
Pan, Y.[Yingtian],
Ling, H.B.[Hai-Bin],
Self-Supervised Bulk Motion Artifact Removal in Optical Coherence
Tomography Angiography,
CVPR22(20585-20593)
IEEE DOI
2210
Training, Image quality, Optical coherence tomography,
Brain modeling, Feature extraction, Mice, Noise measurement,
Medical, biological and cell microscopy
BibRef
Gende, M.[Mateo],
de Moura, J.[Joaquim],
Novo, J.[Jorge],
Ortega, M.[Marcos],
High/Low Quality Style Transfer for Mutual Conversion of OCT Images
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CIAP22(I:210-220).
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2205
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Jeihouni, P.[Paria],
Dehzangi, O.[Omid],
Amireskandari, A.[Annahita],
Rezai, A.[Ali],
Nasrabadi, N.M.[Nasser M.],
Gan-Based Super-Resolution and Segmentation of Retinal Layers in
Optical Coherence Tomography Scans,
ICIP21(46-50)
IEEE DOI
2201
Optical losses, Training, Image segmentation, Convolution,
Optical coherence tomography, Superresolution, Retina,
Dice loss
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Wang, Y.Q.[Yi-Qian],
Warter, A.[Alexandra],
Cavichini-Cordeiro, M.[Melina],
Freeman, W.R.[William R.],
Bartsch, D.U.G.[Dirk-Uwe G.],
Nguyen, T.Q.[Truong Q.],
An, C.[Cheolhong],
Learning to Correct Axial Motion in OCT for 3D Retinal Imaging,
ICIP21(126-130)
IEEE DOI
2201
Visualization, Image segmentation, Image resolution,
Optical coherence tomography, Motion segmentation, Imaging,
retinal imaging
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Lange, T.[Tyra],
Lake, S.[Stewart],
Reynolds, K.[Karen],
Bottema, M.[Murk],
Automated Computational Diagnosis of Peripheral Retinal Pathology in
Optical Coherence Tomography (OCT) Scans using Graph Theory,
DICTA20(1-3)
IEEE DOI
2201
Pathology, Shape, Optical coherence tomography, Tools, Retina,
Graph theory, Standards, Optical coherence tomography (OCT),
graph theory
BibRef
Viedma, I.A.[Ignacio A.],
Alonso-Caneiro, D.[David],
Read, S.A.[Scott A.],
Collins, M.J.[Michael J.],
OCT retinal image-to-image translation: Analysing the use of CycleGAN
to improve retinal boundary semantic segmentation,
DICTA21(01-08)
IEEE DOI
2201
Training, Image quality, Image segmentation, Instruments, Semantics,
Speckle, Retina, optical coherence tomography, denoising,
generative adversarial network
BibRef
Alonso-Caneiro, D.[David],
Kugelman, J.[Jason],
Tong, J.[Janelle],
Kalloniatis, M.[Michael],
Chen, F.K.[Fred K.],
Read, S.A.[Scott A.],
Collins, M.J.[Michael J.],
Use of uncertainty quantification as a surrogate for layer
segmentation error in Stargardt disease retinal OCT images,
DICTA21(1-8)
IEEE DOI
2201
Measurement, Image segmentation, Pathology, Uncertainty,
Monte Carlo methods, Optical coherence tomography, Semantics,
deep learning
BibRef
Hong, T.[Tae],
Mohammadi, F.[Farnaz],
Chatterjee, R.[Rohan],
Chan, E.[Eric],
Pourhomayoun, M.[Mohammad],
Mohammadzadeh, V.[Vahid],
Nouri-Mahdavi, K.[Kouros],
Amini, N.[Navid],
A Novel Similarity Measure for Retinal Optical Coherence Tomography
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ISVC21(II:276-286).
Springer DOI
2112
BibRef
Gurevich, I.[Igor],
Budzinskaya, M.[Maria],
Yashina, V.[Vera],
Tleubaev, A.[Adil],
Pavlov, V.[Vladislav],
Petrachkov, D.[Denis],
Automation of the Detection of Pathological Changes in the Morphometric
Characteristics of the Human Eye Fundus Based on the Data of Optical
Coherence Tomography Angiography,
IMTA20(253-265).
Springer DOI
2103
BibRef
Kamran, S.A.,
Tavakkoli, A.,
Zuckerbrod, S.L.,
Improving Robustness Using Joint Attention Network for Detecting
Retinal Degeneration From Optical Coherence Tomography Images,
ICIP20(2476-2480)
IEEE DOI
2011
Retina, Diseases, Robustness, Decoding, Training, Image segmentation,
Testing, Retinal Degeneration, SD-OCT,
Attention Map
BibRef
Dehzangi, O.[Omid],
Gheshlaghi, S.H.[Saba Heidari],
Amireskandari, A.[Annahita],
Nasrabadi, N.M.[Nasser M.],
Rezai, A.[Ali],
OCT Image Segmentation Using Neural Architecture Search and SRGAN,
ICPR21(6425-6430)
IEEE DOI
2105
Training, Image segmentation, Optical coherence tomography,
Microprocessors, Superresolution, Computer architecture, Object segmentation
BibRef
Gheshlaghi, S.H.,
Dehzangi, O.,
Dabouei, A.,
Amireskandari, A.,
Rezai, A.,
Nasrabadi, N.M.,
Efficient OCT Image Segmentation Using Neural Architecture Search,
ICIP20(428-432)
IEEE DOI
2011
Computer architecture, Image resolution, Image segmentation,
Microprocessors, Search problems, Retina, Biomedical imaging,
retinal layers
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Mahapatra, D.,
Bozorgtabar, B.,
Shao, L.,
Pathological Retinal Region Segmentation From OCT Images Using
Geometric Relation Based Augmentation,
CVPR20(9608-9617)
IEEE DOI
2008
Image segmentation, Shape, Diseases, Retina, Generators, Training,
Image generation
BibRef
Nunes, A.[Ana],
Serranho, P.[Pedro],
Quental, H.[Hugo],
Castelo-Branco, M.[Miguel],
Bernardes, R.[Rui],
The Effect of Menopause on the Sexual Dimorphism in the Human Retina:
Texture Analysis of Optical Coherence Tomography Data,
ICIAR20(II:344-357).
Springer DOI
2007
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Chetoui, M.[Mohamed],
Akhloufi, M.A.[Moulay A.],
Deep Retinal Diseases Detection and Explainability Using OCT Images,
ICIAR20(II:358-366).
Springer DOI
2007
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Wu, J.[Jun],
Zhang, Y.[Yao],
Wang, J.[Jie],
Zhao, J.[Jianchun],
Ding, D.[Dayong],
Chen, N.J.[Ning-Jiang],
Wang, L.L.[Ling-Ling],
Chen, X.[Xuan],
Jiang, C.H.[Chun-Hui],
Zou, X.[Xuan],
Liu, X.[Xing],
Xiao, H.[Hui],
Tian, Y.[Yuan],
Shang, Z.J.[Zong-Jiang],
Wang, K.W.[Kai-Wei],
Li, X.R.[Xi-Rong],
Yang, G.[Gang],
Fan, J.P.[Jian-Ping],
Attennet:
Deep Attention Based Retinal Disease Classification in OCT Images,
MMMod20(II:565-576).
Springer DOI
2003
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Fujii, G.,
Yoshida, Y.,
Muramatsu, S.,
Ono, S.,
Choi, S.,
Ota, T.,
Nin, F.,
Hibino, H.,
OCT Volumetric Data Restoration with Latent Distribution of
Refractive Index,
ICIP19(764-768)
IEEE DOI
1910
Refractive index distribution, primal-dual splitting method,
volumetric data, sparse modeling, MS en-face OCT
BibRef
Boroomand, A.[Ameneh],
Tan, B.Y.[Bing-Yao],
Shafiee, M.J.[Mohammad Javad],
Bizheva, K.[Kostadinka],
Wong, A.[Alexander],
A Random Field Computational Adaptive Optics Framework for Optical
Coherence Microscopy,
ICIAR19(II:283-294).
Springer DOI
1909
BibRef
Sleman, A.A.,
Eltanboly, A.,
Soliman, A.,
Ghazal, M.,
Sandhu, H.,
Schaal, S.,
Keynton, R.,
Elmaghraby, A.,
El-Baz, A.,
An Innovative 3D Adaptive Patient-Related Atlas for Automatic
Segmentation of Retina Layers from Oct Images,
ICIP18(729-733)
IEEE DOI
1809
Image segmentation, Retina, Shape,
Solid modeling, Adaptation models, OCT,
Atlas
BibRef
Kiaee, F.,
Fahimi, H.,
Rabbani, H.,
Intra-Retinal Layer Segmentation of Optical Coherence Tomography
Using 3D Fully Convolutional Networks,
ICIP18(2795-2799)
IEEE DOI
1809
Retina, Image segmentation, Decoding,
Training, Convolutional codes, Machine learning, Segmentation,
optical coherence tomography (OCT)
BibRef
Liu, X.,
Liu, D.,
Fu, T.,
Zhang, K.,
Liu, J.,
Chen, L.,
Shortest Path with Backtracking Based Automatic Layer Segmentation in
Pathological Retinal Optical Coherence Tomography,
ICIP18(2770-2774)
IEEE DOI
1809
Image segmentation, Retina, Pathology, Image edge detection,
Diseases, Transforms, Heuristic algorithms, OCT, layer segmentation,
direction consistency loss
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Liu, X.M.[Xiao-Ming],
Wang, S.[Shuo],
Zhang, Y.[Ying],
Meibomian Glands Segmentation In Near-Infrared Images With Weakly
Supervised Deep Learning,
ICIP21(16-20)
IEEE DOI
2201
glands at edge of eyelids.
Deep learning, Image segmentation, Filtering, Glands, Morphology,
Imaging, Near-infrared imaging, Meibomian gland dysfunction,
spatial attention
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Liu, X.M.[Xiao-Ming],
Liu, Z.P.[Zhi-Peng],
Zhang, Y.[Ying],
Wang, M.[Man],
Li, B.[Bo],
Tang, J.S.[Jin-Shan],
Weakly-Supervised Automatic Biomarkers Detection and Classification
of Retinal Optical Coherence Tomography Images,
ICIP21(71-75)
IEEE DOI
2201
Deep learning, Optical coherence tomography, Biomarkers, Retina,
Generative adversarial networks, Hybrid power systems, OCT,
biomarkers
BibRef
Liu, X.M.[Xiao-Ming],
Fu, T.Y.[Tian-Yu],
Pan, Z.F.[Zhi-Fang],
Liu, D.[Dong],
Hu, W.[Wei],
Li, B.[Bo],
Semi-Supervised Automatic Layer and Fluid Region Segmentation of
Retinal Optical Coherence Tomography Images Using Adversarial
Learning,
ICIP18(2780-2784)
IEEE DOI
1809
Image segmentation, Retina, Fluids, Convolution, Entropy, Training,
Biomedical imaging, OCT, image processing,
layer segmentation
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Katona, M.[Melinda],
Kovács, A.[Attila],
Varga, L.[László],
Grósz, T.[Tamás],
Dombi, J.[József],
Dégi, R.[Rózsa],
Nyúl, L.G.[László G.],
Automatic Detection and Characterization of Biomarkers in OCT Images,
ICIAR18(706-714).
Springer DOI
1807
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Chakravarty, A.[Arunava],
Gaddipati, D.J.[Divya Jyothi],
Sivaswamy, J.[Jayanthi],
Construction of a Retinal Atlas for Macular OCT Volumes,
ICIAR18(650-658).
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1807
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Rossant, F.[Florence],
Grieve, K.[Kate],
Zwillinger, S.[Stéphanie],
Paques, M.[Michel],
Detection and Tracking of the Pores of the Lamina Cribrosa in Three
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ACIVS17(651-663).
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1712
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Baamonde, S.[Sergio],
de Moura, J.[Joaquim],
Novo, J.[Jorge],
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Feature Definition and Selection for Epiretinal Membrane
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CIAP17(II:456-466).
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1711
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Rossant, F.[Florence],
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Automated Analysis of Directional Optical Coherence Tomography Images,
ICIAR17(524-532).
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1706
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El Tanboly, A.,
Ismail, M.,
Switala, A.,
Mahmoud, M.,
Soliman, A.,
Neyer, T.,
Palacio, A.,
Hadayer, A.,
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Schaal, S.,
El-Baz, A.,
A novel automatic segmentation of healthy and diseased retinal layers
from OCT scans,
ICIP16(116-120)
IEEE DOI
1610
Adaptation models
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de Moura, J.[Joaquim],
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3D Retinal Vessel Tree Segmentation and Reconstruction with OCT Images,
ICIAR16(716-726).
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1608
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Shalbaf, F.,
Turuwhenua, J.,
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Dokos, S.,
An image processing pipeline for segmenting the retinal layers from
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IVCNZ13(70-75)
IEEE DOI
1402
Gabor filters
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Guimarăes, P.[Pedro],
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3D Retinal Vascular Network from Optical Coherence Tomography Data,
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1206
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Tokayer, J.[Jason],
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Sparsity-based retinal layer segmentation of optical coherence
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ICIP11(449-452).
IEEE DOI
1201
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Gazarek, J.[Jiri],
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Retinal nerve fibre layer detection in fundus camera images compared to
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ICIIP11(1-5).
IEEE DOI
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Kolar, R.[Radim],
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Registration of 3D Retinal Optical Coherence Tomography Data and 2D
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WBIR10(72-82).
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Eichel, J.A.[Justin A.],
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Automated 3D Reconstruction and Segmentation from Optical Coherence
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1009
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Eichel, J.A.[Justin A.],
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A Novel Algorithm for Extraction of the Layers of the Cornea,
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0905
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Chapter on Medical Applications, CAT, MRI, Ultrasound, Heart Models, Brain Models continues in
Medical Applications -- Surgery .