21.5.8 Retinal Images, Optical Coherence Tomography, OCT

Chapter Contents (Back)
Retinal Images. Optical Coherence Tomography. OCT. Eye. More general section:
See also Optical Tomography, Infrared Tomography.

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

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.[Ziyun], 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

Ni, S.[Shuibin], 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


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

Rapp, J., Mansour, H., Boufounos, P., Orlik, P., Koike-Akino, T., Parsons, K.,
Maximum Likelihood Surface Profilometry Via Optical coherence Tomography,
ICIP22(1561-1565)
IEEE DOI 2211
Reflectivity, Maximum likelihood estimation, Surface reconstruction, Interpolation, maximum likelihood estimation BibRef

Logan, Y.Y.[Yash-Yee], Benkert, R.[Ryan], Mustafa, A.[Ahmad], Al Regib, G.[Ghassan],
Patient Aware Active Learning for Fine-Grained OCT Classification,
ICIP22(3908-3912)
IEEE DOI 2211
Learning systems, Uncertainty, Medical services, Robustness, Classification algorithms, Medical diagnostic imaging, Personalized diagnosis BibRef

Razavi, R.[Raha], Rabbani, H.[Hossein], 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], Freeman, W.R.[William R.], Nguyen, T.Q.[Truong Q.], An, C.[Cheolhong],
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], Kambhamettu, C.[Chandra], Jin, J.[Jing],
Cu-Net: Towards Continuous Multi-Class Contour Detection for Retinal Layer Segmentation In OCT Images,
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 Registration,
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], Corona, S.T.[Stephanie Trejo], 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 Using Contrastive Unpaired Translation Generative Adversarial Networks,
CIAP22(I:210-220).
Springer DOI 2205
BibRef

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 BibRef

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 BibRef

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 Images,
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 BibRef

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
BibRef

Chetoui, M.[Mohamed], Akhloufi, M.A.[Moulay A.],
Deep Retinal Diseases Detection and Explainability Using OCT Images,
ICIAR20(II:358-366).
Springer DOI 2007
BibRef

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
BibRef

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 BibRef

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 BibRef

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 BibRef

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
BibRef

Chakravarty, A.[Arunava], Gaddipati, D.J.[Divya Jyothi], Sivaswamy, J.[Jayanthi],
Construction of a Retinal Atlas for Macular OCT Volumes,
ICIAR18(650-658).
Springer DOI 1807
BibRef

Rossant, F.[Florence], Grieve, K.[Kate], Zwillinger, S.[Stéphanie], Paques, M.[Michel],
Detection and Tracking of the Pores of the Lamina Cribrosa in Three Dimensional SD-OCT Data,
ACIVS17(651-663).
Springer DOI 1712
BibRef

Baamonde, S.[Sergio], de Moura, J.[Joaquim], Novo, J.[Jorge], Rouco, J.[José], Ortega, M.[Marcos],
Feature Definition and Selection for Epiretinal Membrane Characterization in Optical Coherence Tomography Images,
CIAP17(II:456-466).
Springer DOI 1711
BibRef

Rossant, F.[Florence], Grieve, K.[Kate], Paques, M.[Michel],
Automated Analysis of Directional Optical Coherence Tomography Images,
ICIAR17(524-532).
Springer DOI 1706
BibRef

El Tanboly, A., Ismail, M., Switala, A., Mahmoud, M., Soliman, A., Neyer, T., Palacio, A., Hadayer, A., El-Azab, M., 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 BibRef

Shalbaf, F., Turuwhenua, J., Vaghefi, E., Dokos, S.,
An image processing pipeline for segmenting the retinal layers from optical coherence tomography images,
IVCNZ13(70-75)
IEEE DOI 1402
Gabor filters BibRef

Guimarăes, P.[Pedro], Rodrigues, P.[Pedro], Serranho, P.[Pedro], Bernardes, R.[Rui],
3D Retinal Vascular Network from Optical Coherence Tomography Data,
ICIAR12(II: 339-346).
Springer DOI 1206
BibRef

Tokayer, J.[Jason], Ortega, A.[Antonio], Huang, D.[David],
Sparsity-based retinal layer segmentation of optical coherence tomography images,
ICIP11(449-452).
IEEE DOI 1201
BibRef

Gazarek, J.[Jiri], Jan, J.[Jiri], Kolar, R.[Radim], Odstrcilik, J.[Jan],
Retinal nerve fibre layer detection in fundus camera images compared to results from optical coherence tomography,
ICIIP11(1-5).
IEEE DOI 1112
BibRef

Kolar, R.[Radim], Tasevsky, P.[Pavel],
Registration of 3D Retinal Optical Coherence Tomography Data and 2D Fundus Images,
WBIR10(72-82).
Springer DOI 1007
BibRef

Eichel, J.A.[Justin A.], Bizheva, K.K.[Kostadinka K.], Clausi, D.A.[David A.], Fieguth, P.W.[Paul W.],
Automated 3D Reconstruction and Segmentation from Optical Coherence Tomography,
ECCV10(III: 44-57).
Springer DOI 1009
BibRef

Eichel, J.A.[Justin A.], Mishra, A.K.[Akshaya K.], Fieguth, P.W.[Paul W.], Clausi, D.A.[David A.], Bizheva, K.K.[Kostadinka K.],
A Novel Algorithm for Extraction of the Layers of the Cornea,
CRV09(313-320).
IEEE DOI 0905
BibRef

Chapter on Medical Applications, CAT, MRI, Ultrasound, Heart Models, Brain Models continues in
Medical Applications -- Surgery .


Last update:Mar 16, 2024 at 20:36:19