Abstract

Assessment of serous retinal detachment plays an important role in the diagnosis of central serous chorioretinopathy (CSC). In this paper, we propose an automatic, three-dimensional segmentation method to detect both neurosensory retinal detachment (NRD) and pigment epithelial detachment (PED) in spectral domain optical coherence tomography (SD-OCT) images. The proposed method involves constructing a probability map from training samples using random forest classification. The probability map is constructed from a linear combination of structural texture, intensity, and layer thickness information. Then, a continuous max flow optimization algorithm is applied to the probability map to segment the retinal detachment-associated fluid regions. Experimental results from 37 retinal SD-OCT volumes from cases of CSC demonstrate the proposed method can achieve a true positive volume fraction (TPVF), false positive volume fraction (FPVF), positive predicative value (PPV), and dice similarity coefficient (DSC) of 92.1%, 0.53%, 94.7%, and 93.3%, respectively, for NRD segmentation and 92.5%, 0.14%, 80.9%, and 84.6%, respectively, for PED segmentation. The proposed method can be an automatic tool to evaluate serous retinal detachment and has the potential to improve the clinical evaluation of CSC.

© 2017 Optical Society of America

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References

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2017 (4)

2016 (7)

K. K. Dansingani, C. Balaratnasingam, S. Mrejen, M. Inoue, K. B. Freund, J. M. Klancnik, and L. A. Yannuzzi, “Annular lesions and catenary corms in chronic central serous chorioretinopathy,” Am. J. Ophthalmol. 166, 60–67 (2016).
[Crossref] [PubMed]

Y. Kuroda, S. Ooto, K. Yamashiro, A. Oishi, H. Nakanishi, H. Tamura, N. Ueda-Arakawa, and N. Yoshimura, “Increased choroidal vascularity in central serous chorioretinopathy quantified using swept-source optical coherence tomography,” Am. J. Ophthalmol. 169, 199–207 (2016).
[Crossref] [PubMed]

P. Roberts, B. Baumann, J. Lammer, B. Gerendas, J. Kroisamer, W. Bühl, M. Pircher, C. K. Hitzenberger, U. Schmidt-Erfurth, and S. Sacu, “Retinal Pigment Epithelial Features in Central Serous Chorioretinopathy Identified by Polarization-Sensitive Optical Coherence Tomography,” Invest. Ophthalmol. Vis. Sci. 57(4), 1595–1603 (2016).
[Crossref] [PubMed]

T. Wang, Z. Ji, Q. Sun, Q. Chen, S. Yu, W. Fan, S. Yuan, and Q. Liu, “Label propagation and higher-order constraint-based segmentation of fluid-associated regions in retinal SD-OCT images,” Inf. Sci. 358, 92–111 (2016).
[Crossref]

J. Wang, M. Zhang, A. D. Pechauer, L. Liu, T. S. Hwang, D. J. Wilson, D. Li, and Y. Jia, “Automated volumetric segmentation of retinal fluid on optical coherence tomography,” Biomed. Opt. Express 7(4), 1577–1589 (2016).
[Crossref] [PubMed]

T. Hassan, M. U. Akram, B. Hassan, A. M. Syed, and S. A. Bazaz, “Automated segmentation of subretinal layers for the detection of macular edema,” Appl. Opt. 55(3), 454–461 (2016).
[Crossref] [PubMed]

Z. Sun, H. Chen, F. Shi, L. Wang, W. Zhu, D. Xiang, C. Yan, L. Li, and X. Chen, “An automated framework for 3D serous pigment epithelium detachment segmentation in SD-OCT images,” Sci. Rep. 6(1), 21739 (2016).
[Crossref] [PubMed]

2015 (5)

X. Xu, K. Lee, L. Zhang, M. Sonka, and M. Abramoff, “Stratified sampling voxel classification for segmentation of intraretinal and subretinal fluid in longitudinal clinical OCT data,” IEEE Trans. Med. Imaging 34(7), 1616–1623 (2015).
[Crossref] [PubMed]

F. Shi, X. Chen, H. Zhao, W. Zhu, D. Xiang, E. Gao, M. Sonka, and H. Chen, “Automated 3-D retinal layer segmentation of macular optical coherence tomography images with serous pigment epithelial detachments,” IEEE Trans. Med. Imaging 34(2), 441–452 (2015).
[Crossref] [PubMed]

A. Lang, A. Carass, E. K. Swingle, O. Al-Louzi, P. Bhargava, S. Saidha, H. S. Ying, P. A. Calabresi, and J. L. Prince, “Automatic segmentation of microcystic macular edema in OCT,” Biomed. Opt. Express 6(1), 155–169 (2015).
[Crossref] [PubMed]

S. J. Chiu, M. J. Allingham, P. S. Mettu, S. W. Cousins, J. A. Izatt, and S. Farsiu, “Kernel regression based segmentation of optical coherence tomography images with diabetic macular edema,” Biomed. Opt. Express 6(4), 1172–1194 (2015).
[Crossref] [PubMed]

A. Daruich, A. Matet, A. Dirani, E. Bousquet, M. Zhao, N. Farman, F. Jaisser, and F. Behar-Cohen, “Central serous chorioretinopathy: recent findings and new physiopathology hypothesis,” Prog. Retin. Eye Res. 48, 82–118 (2015).
[Crossref] [PubMed]

2014 (3)

R. Hua, L. Liu, C. Li, and L. Chen, “Evaluation of the effects of photodynamic therapy on chronic central serous chorioretinopathy based on the mean choroidal thickness and the lumen area of abnormal choroidal vessels,” Photodiagn. Photodyn. Ther. 11(4), 519–525 (2014).
[Crossref] [PubMed]

M. Rajchl, J. Yuan, J. A. White, E. Ukwatta, J. Stirrat, C. M. Nambakhsh, F. P. Li, and T. M. Peters, “Interactive hierarchical-flow segmentation of scar tissue from late-enhancement cardiac MR images,” IEEE Trans. Med. Imaging 33(1), 159–172 (2014).
[Crossref] [PubMed]

W. Qiu, J. Yuan, E. Ukwatta, Y. Sun, M. Rajchl, and A. Fenster, “Prostate segmentation: An efficient convex optimization approach with axial symmetry using 3-D TRUS and MR images,” IEEE Trans. Med. Imaging 33(4), 947–960 (2014).
[Crossref] [PubMed]

2013 (2)

Q. Chen, T. Leng, L. Zheng, L. Kutzscher, J. Ma, L. de Sisternes, and D. L. Rubin, “Automated drusen segmentation and quantification in SD-OCT images,” Med. Image Anal. 17(8), 1058–1072 (2013).
[Crossref] [PubMed]

P. A. Dufour, L. Ceklic, H. Abdillahi, S. Schröder, S. De Dzanet, U. Wolf-Schnurrbusch, and J. Kowal, “Graph-based multi-surface segmentation of OCT data using trained hard and soft constraints,” IEEE Trans. Med. Imaging 32(3), 531–543 (2013).
[Crossref] [PubMed]

2012 (3)

X. Chen, M. Niemeijer, L. Zhang, K. Lee, M. D. Abramoff, and M. Sonka, “Three-dimensional segmentation of fluid-associated abnormalities in retinal OCT: probability constrained graph-search-graph-cut,” IEEE Trans. Med. Imaging 31(8), 1521–1531 (2012).
[Crossref] [PubMed]

S. J. Ahn, T. W. Kim, J. W. Huh, H. G. Yu, and H. Chung, “Comparison of features on SD-OCT between acute central serous chorioretinopathy and exudative age-related macular degeneration,” Ophthalmic Surg. Lasers Imaging 43(5), 374–382 (2012).
[Crossref] [PubMed]

F. M. Penha, P. J. Rosenfeld, G. Gregori, M. Falcão, Z. Yehoshua, F. Wang, and W. J. Feuer, “Quantitative imaging of retinal pigment epithelial detachments using spectral-domain optical coherence tomography,” Am. J. Ophthalmol. 153(3), 515–523 (2012).
[Crossref] [PubMed]

2010 (3)

K. Lee, M. Niemeijer, M. K. Garvin, Y. H. Kwon, M. Sonka, and M. D. Abramoff, “Segmentation of the optic disc in 3-D OCT scans of the optic nerve head,” IEEE Trans. Med. Imaging 29(1), 159–168 (2010).
[Crossref] [PubMed]

M. D. Abràmoff, M. K. Garvin, and M. Sonka, “Retinal imaging and image analysis,” IEEE Rev. Biomed. Eng. 3, 169–208 (2010).
[Crossref] [PubMed]

G. Quellec, K. Lee, M. Dolejsi, M. K. Garvin, M. D. Abràmoff, and M. Sonka, “Three-dimensional analysis of retinal layer texture: identification of fluid-filled regions in SD-OCT of the macula,” IEEE Trans. Med. Imaging 29(6), 1321–1330 (2010).
[Crossref] [PubMed]

2009 (1)

M. K. Garvin, M. D. Abràmoff, X. Wu, S. R. Russell, T. L. Burns, and M. Sonka, “Automated 3-D intraretinal layer segmentation of macular spectral-domain optical coherence tomography images,” IEEE Trans. Med. Imaging 28(9), 1436–1447 (2009).
[Crossref] [PubMed]

2008 (1)

S. Farsiu, S. J. Chiu, J. A. Izatt, and C. A. Toth, “Fast detection and segmentation of drusen in retinal optical coherence tomography images,” Proc. SPIE 6844, 68440D (2008).
[Crossref]

2006 (2)

K. Li, X. Wu, D. Z. Chen, and M. Sonka, “Optimal surface segmentation in volumetric images-a graph-theoretic approach,” IEEE Trans. Pattern Anal. Mach. Intell. 28(1), 119–134 (2006).
[Crossref] [PubMed]

J. K. Udupa, V. R. Leblanc, Y. Zhuge, C. Imielinska, H. Schmidt, L. M. Currie, B. E. Hirsch, and J. Woodburn, “A framework for evaluating image segmentation algorithms,” Comput. Med. Imaging Graph. 30(2), 75–87 (2006).
[Crossref] [PubMed]

2005 (1)

D. C. Fernández, “Delineating fluid-filled region boundaries in optical coherence tomography images of the retina,” IEEE Trans. Med. Imaging 24(8), 929–945 (2005).
[Crossref] [PubMed]

2004 (1)

A. Chambolle, “An algorithm for total variation minimization and applications,” J. Math. Imaging Vis. 20(1), 89–97 (2004).

2000 (1)

J. Rogowska and M. E. Brezinski, “Evaluation of the adaptive speckle suppression filter for coronary optical coherence tomography imaging,” IEEE Trans. Med. Imaging 19(12), 1261–1266 (2000).
[Crossref] [PubMed]

Abdillahi, H.

P. A. Dufour, L. Ceklic, H. Abdillahi, S. Schröder, S. De Dzanet, U. Wolf-Schnurrbusch, and J. Kowal, “Graph-based multi-surface segmentation of OCT data using trained hard and soft constraints,” IEEE Trans. Med. Imaging 32(3), 531–543 (2013).
[Crossref] [PubMed]

Abramoff, M.

X. Xu, K. Lee, L. Zhang, M. Sonka, and M. Abramoff, “Stratified sampling voxel classification for segmentation of intraretinal and subretinal fluid in longitudinal clinical OCT data,” IEEE Trans. Med. Imaging 34(7), 1616–1623 (2015).
[Crossref] [PubMed]

Abramoff, M. D.

X. Chen, M. Niemeijer, L. Zhang, K. Lee, M. D. Abramoff, and M. Sonka, “Three-dimensional segmentation of fluid-associated abnormalities in retinal OCT: probability constrained graph-search-graph-cut,” IEEE Trans. Med. Imaging 31(8), 1521–1531 (2012).
[Crossref] [PubMed]

K. Lee, M. Niemeijer, M. K. Garvin, Y. H. Kwon, M. Sonka, and M. D. Abramoff, “Segmentation of the optic disc in 3-D OCT scans of the optic nerve head,” IEEE Trans. Med. Imaging 29(1), 159–168 (2010).
[Crossref] [PubMed]

Abràmoff, M. D.

M. D. Abràmoff, M. K. Garvin, and M. Sonka, “Retinal imaging and image analysis,” IEEE Rev. Biomed. Eng. 3, 169–208 (2010).
[Crossref] [PubMed]

G. Quellec, K. Lee, M. Dolejsi, M. K. Garvin, M. D. Abràmoff, and M. Sonka, “Three-dimensional analysis of retinal layer texture: identification of fluid-filled regions in SD-OCT of the macula,” IEEE Trans. Med. Imaging 29(6), 1321–1330 (2010).
[Crossref] [PubMed]

M. K. Garvin, M. D. Abràmoff, X. Wu, S. R. Russell, T. L. Burns, and M. Sonka, “Automated 3-D intraretinal layer segmentation of macular spectral-domain optical coherence tomography images,” IEEE Trans. Med. Imaging 28(9), 1436–1447 (2009).
[Crossref] [PubMed]

Ahn, S. J.

S. J. Ahn, T. W. Kim, J. W. Huh, H. G. Yu, and H. Chung, “Comparison of features on SD-OCT between acute central serous chorioretinopathy and exudative age-related macular degeneration,” Ophthalmic Surg. Lasers Imaging 43(5), 374–382 (2012).
[Crossref] [PubMed]

Akram, M. U.

Allingham, M. J.

Al-Louzi, O.

Bae, E.

J. Yuan, E. Bae, and X. C. Tai, “A study on continuous max-flow and min-cut approaches,” inProceeding of IEEE Conference on Computer Vision and Pattern Recognition (IEEE, 2010), pp. 2217–2224.
[Crossref]

Balaratnasingam, C.

K. K. Dansingani, C. Balaratnasingam, S. Mrejen, M. Inoue, K. B. Freund, J. M. Klancnik, and L. A. Yannuzzi, “Annular lesions and catenary corms in chronic central serous chorioretinopathy,” Am. J. Ophthalmol. 166, 60–67 (2016).
[Crossref] [PubMed]

Baumann, B.

P. Roberts, B. Baumann, J. Lammer, B. Gerendas, J. Kroisamer, W. Bühl, M. Pircher, C. K. Hitzenberger, U. Schmidt-Erfurth, and S. Sacu, “Retinal Pigment Epithelial Features in Central Serous Chorioretinopathy Identified by Polarization-Sensitive Optical Coherence Tomography,” Invest. Ophthalmol. Vis. Sci. 57(4), 1595–1603 (2016).
[Crossref] [PubMed]

Bazaz, S. A.

Behar-Cohen, F.

A. Daruich, A. Matet, A. Dirani, E. Bousquet, M. Zhao, N. Farman, F. Jaisser, and F. Behar-Cohen, “Central serous chorioretinopathy: recent findings and new physiopathology hypothesis,” Prog. Retin. Eye Res. 48, 82–118 (2015).
[Crossref] [PubMed]

Bhargava, P.

Bogunovic, H.

Bousquet, E.

A. Daruich, A. Matet, A. Dirani, E. Bousquet, M. Zhao, N. Farman, F. Jaisser, and F. Behar-Cohen, “Central serous chorioretinopathy: recent findings and new physiopathology hypothesis,” Prog. Retin. Eye Res. 48, 82–118 (2015).
[Crossref] [PubMed]

Brezinski, M. E.

J. Rogowska and M. E. Brezinski, “Evaluation of the adaptive speckle suppression filter for coronary optical coherence tomography imaging,” IEEE Trans. Med. Imaging 19(12), 1261–1266 (2000).
[Crossref] [PubMed]

Bühl, W.

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Chen, H.

Z. Sun, H. Chen, F. Shi, L. Wang, W. Zhu, D. Xiang, C. Yan, L. Li, and X. Chen, “An automated framework for 3D serous pigment epithelium detachment segmentation in SD-OCT images,” Sci. Rep. 6(1), 21739 (2016).
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F. Shi, X. Chen, H. Zhao, W. Zhu, D. Xiang, E. Gao, M. Sonka, and H. Chen, “Automated 3-D retinal layer segmentation of macular optical coherence tomography images with serous pigment epithelial detachments,” IEEE Trans. Med. Imaging 34(2), 441–452 (2015).
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R. Hua, L. Liu, C. Li, and L. Chen, “Evaluation of the effects of photodynamic therapy on chronic central serous chorioretinopathy based on the mean choroidal thickness and the lumen area of abnormal choroidal vessels,” Photodiagn. Photodyn. Ther. 11(4), 519–525 (2014).
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Z. Sun, H. Chen, F. Shi, L. Wang, W. Zhu, D. Xiang, C. Yan, L. Li, and X. Chen, “An automated framework for 3D serous pigment epithelium detachment segmentation in SD-OCT images,” Sci. Rep. 6(1), 21739 (2016).
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X. Chen, M. Niemeijer, L. Zhang, K. Lee, M. D. Abramoff, and M. Sonka, “Three-dimensional segmentation of fluid-associated abnormalities in retinal OCT: probability constrained graph-search-graph-cut,” IEEE Trans. Med. Imaging 31(8), 1521–1531 (2012).
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J. Novosel, K. A. Vermeer, J. H. de Jong, Ziyuan Wang, and L. J. van Vliet, “Joint Segmentation of Retinal Layers and Focal Lesions in 3-D OCT Data of Topologically Disrupted Retinas,” IEEE Trans. Med. Imaging 36(6), 1276–1286 (2017).
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Q. Chen, T. Leng, L. Zheng, L. Kutzscher, J. Ma, L. de Sisternes, and D. L. Rubin, “Automated drusen segmentation and quantification in SD-OCT images,” Med. Image Anal. 17(8), 1058–1072 (2013).
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P. A. Dufour, L. Ceklic, H. Abdillahi, S. Schröder, S. De Dzanet, U. Wolf-Schnurrbusch, and J. Kowal, “Graph-based multi-surface segmentation of OCT data using trained hard and soft constraints,” IEEE Trans. Med. Imaging 32(3), 531–543 (2013).
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T. Wang, Z. Ji, Q. Sun, Q. Chen, S. Yu, W. Fan, S. Yuan, and Q. Liu, “Label propagation and higher-order constraint-based segmentation of fluid-associated regions in retinal SD-OCT images,” Inf. Sci. 358, 92–111 (2016).
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Farman, N.

A. Daruich, A. Matet, A. Dirani, E. Bousquet, M. Zhao, N. Farman, F. Jaisser, and F. Behar-Cohen, “Central serous chorioretinopathy: recent findings and new physiopathology hypothesis,” Prog. Retin. Eye Res. 48, 82–118 (2015).
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K. K. Dansingani, C. Balaratnasingam, S. Mrejen, M. Inoue, K. B. Freund, J. M. Klancnik, and L. A. Yannuzzi, “Annular lesions and catenary corms in chronic central serous chorioretinopathy,” Am. J. Ophthalmol. 166, 60–67 (2016).
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Gao, E.

F. Shi, X. Chen, H. Zhao, W. Zhu, D. Xiang, E. Gao, M. Sonka, and H. Chen, “Automated 3-D retinal layer segmentation of macular optical coherence tomography images with serous pigment epithelial detachments,” IEEE Trans. Med. Imaging 34(2), 441–452 (2015).
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G. Quellec, K. Lee, M. Dolejsi, M. K. Garvin, M. D. Abràmoff, and M. Sonka, “Three-dimensional analysis of retinal layer texture: identification of fluid-filled regions in SD-OCT of the macula,” IEEE Trans. Med. Imaging 29(6), 1321–1330 (2010).
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M. D. Abràmoff, M. K. Garvin, and M. Sonka, “Retinal imaging and image analysis,” IEEE Rev. Biomed. Eng. 3, 169–208 (2010).
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K. Lee, M. Niemeijer, M. K. Garvin, Y. H. Kwon, M. Sonka, and M. D. Abramoff, “Segmentation of the optic disc in 3-D OCT scans of the optic nerve head,” IEEE Trans. Med. Imaging 29(1), 159–168 (2010).
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M. K. Garvin, M. D. Abràmoff, X. Wu, S. R. Russell, T. L. Burns, and M. Sonka, “Automated 3-D intraretinal layer segmentation of macular spectral-domain optical coherence tomography images,” IEEE Trans. Med. Imaging 28(9), 1436–1447 (2009).
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P. Roberts, B. Baumann, J. Lammer, B. Gerendas, J. Kroisamer, W. Bühl, M. Pircher, C. K. Hitzenberger, U. Schmidt-Erfurth, and S. Sacu, “Retinal Pigment Epithelial Features in Central Serous Chorioretinopathy Identified by Polarization-Sensitive Optical Coherence Tomography,” Invest. Ophthalmol. Vis. Sci. 57(4), 1595–1603 (2016).
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Gregori, G.

F. M. Penha, P. J. Rosenfeld, G. Gregori, M. Falcão, Z. Yehoshua, F. Wang, and W. J. Feuer, “Quantitative imaging of retinal pigment epithelial detachments using spectral-domain optical coherence tomography,” Am. J. Ophthalmol. 153(3), 515–523 (2012).
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Hassan, B.

Hassan, T.

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J. K. Udupa, V. R. Leblanc, Y. Zhuge, C. Imielinska, H. Schmidt, L. M. Currie, B. E. Hirsch, and J. Woodburn, “A framework for evaluating image segmentation algorithms,” Comput. Med. Imaging Graph. 30(2), 75–87 (2006).
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P. Roberts, B. Baumann, J. Lammer, B. Gerendas, J. Kroisamer, W. Bühl, M. Pircher, C. K. Hitzenberger, U. Schmidt-Erfurth, and S. Sacu, “Retinal Pigment Epithelial Features in Central Serous Chorioretinopathy Identified by Polarization-Sensitive Optical Coherence Tomography,” Invest. Ophthalmol. Vis. Sci. 57(4), 1595–1603 (2016).
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R. Hua, L. Liu, C. Li, and L. Chen, “Evaluation of the effects of photodynamic therapy on chronic central serous chorioretinopathy based on the mean choroidal thickness and the lumen area of abnormal choroidal vessels,” Photodiagn. Photodyn. Ther. 11(4), 519–525 (2014).
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S. J. Ahn, T. W. Kim, J. W. Huh, H. G. Yu, and H. Chung, “Comparison of features on SD-OCT between acute central serous chorioretinopathy and exudative age-related macular degeneration,” Ophthalmic Surg. Lasers Imaging 43(5), 374–382 (2012).
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Imielinska, C.

J. K. Udupa, V. R. Leblanc, Y. Zhuge, C. Imielinska, H. Schmidt, L. M. Currie, B. E. Hirsch, and J. Woodburn, “A framework for evaluating image segmentation algorithms,” Comput. Med. Imaging Graph. 30(2), 75–87 (2006).
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K. K. Dansingani, C. Balaratnasingam, S. Mrejen, M. Inoue, K. B. Freund, J. M. Klancnik, and L. A. Yannuzzi, “Annular lesions and catenary corms in chronic central serous chorioretinopathy,” Am. J. Ophthalmol. 166, 60–67 (2016).
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S. J. Chiu, M. J. Allingham, P. S. Mettu, S. W. Cousins, J. A. Izatt, and S. Farsiu, “Kernel regression based segmentation of optical coherence tomography images with diabetic macular edema,” Biomed. Opt. Express 6(4), 1172–1194 (2015).
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S. Farsiu, S. J. Chiu, J. A. Izatt, and C. A. Toth, “Fast detection and segmentation of drusen in retinal optical coherence tomography images,” Proc. SPIE 6844, 68440D (2008).
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A. Daruich, A. Matet, A. Dirani, E. Bousquet, M. Zhao, N. Farman, F. Jaisser, and F. Behar-Cohen, “Central serous chorioretinopathy: recent findings and new physiopathology hypothesis,” Prog. Retin. Eye Res. 48, 82–118 (2015).
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T. Wang, Z. Ji, Q. Sun, Q. Chen, S. Yu, W. Fan, S. Yuan, and Q. Liu, “Label propagation and higher-order constraint-based segmentation of fluid-associated regions in retinal SD-OCT images,” Inf. Sci. 358, 92–111 (2016).
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Karri, S. P. K.

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Kim, T. W.

S. J. Ahn, T. W. Kim, J. W. Huh, H. G. Yu, and H. Chung, “Comparison of features on SD-OCT between acute central serous chorioretinopathy and exudative age-related macular degeneration,” Ophthalmic Surg. Lasers Imaging 43(5), 374–382 (2012).
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K. K. Dansingani, C. Balaratnasingam, S. Mrejen, M. Inoue, K. B. Freund, J. M. Klancnik, and L. A. Yannuzzi, “Annular lesions and catenary corms in chronic central serous chorioretinopathy,” Am. J. Ophthalmol. 166, 60–67 (2016).
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P. A. Dufour, L. Ceklic, H. Abdillahi, S. Schröder, S. De Dzanet, U. Wolf-Schnurrbusch, and J. Kowal, “Graph-based multi-surface segmentation of OCT data using trained hard and soft constraints,” IEEE Trans. Med. Imaging 32(3), 531–543 (2013).
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P. Roberts, B. Baumann, J. Lammer, B. Gerendas, J. Kroisamer, W. Bühl, M. Pircher, C. K. Hitzenberger, U. Schmidt-Erfurth, and S. Sacu, “Retinal Pigment Epithelial Features in Central Serous Chorioretinopathy Identified by Polarization-Sensitive Optical Coherence Tomography,” Invest. Ophthalmol. Vis. Sci. 57(4), 1595–1603 (2016).
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Y. Kuroda, S. Ooto, K. Yamashiro, A. Oishi, H. Nakanishi, H. Tamura, N. Ueda-Arakawa, and N. Yoshimura, “Increased choroidal vascularity in central serous chorioretinopathy quantified using swept-source optical coherence tomography,” Am. J. Ophthalmol. 169, 199–207 (2016).
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Q. Chen, T. Leng, L. Zheng, L. Kutzscher, J. Ma, L. de Sisternes, and D. L. Rubin, “Automated drusen segmentation and quantification in SD-OCT images,” Med. Image Anal. 17(8), 1058–1072 (2013).
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Kwon, Y. H.

K. Lee, M. Niemeijer, M. K. Garvin, Y. H. Kwon, M. Sonka, and M. D. Abramoff, “Segmentation of the optic disc in 3-D OCT scans of the optic nerve head,” IEEE Trans. Med. Imaging 29(1), 159–168 (2010).
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P. Roberts, B. Baumann, J. Lammer, B. Gerendas, J. Kroisamer, W. Bühl, M. Pircher, C. K. Hitzenberger, U. Schmidt-Erfurth, and S. Sacu, “Retinal Pigment Epithelial Features in Central Serous Chorioretinopathy Identified by Polarization-Sensitive Optical Coherence Tomography,” Invest. Ophthalmol. Vis. Sci. 57(4), 1595–1603 (2016).
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J. K. Udupa, V. R. Leblanc, Y. Zhuge, C. Imielinska, H. Schmidt, L. M. Currie, B. E. Hirsch, and J. Woodburn, “A framework for evaluating image segmentation algorithms,” Comput. Med. Imaging Graph. 30(2), 75–87 (2006).
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X. Xu, K. Lee, L. Zhang, M. Sonka, and M. Abramoff, “Stratified sampling voxel classification for segmentation of intraretinal and subretinal fluid in longitudinal clinical OCT data,” IEEE Trans. Med. Imaging 34(7), 1616–1623 (2015).
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X. Chen, M. Niemeijer, L. Zhang, K. Lee, M. D. Abramoff, and M. Sonka, “Three-dimensional segmentation of fluid-associated abnormalities in retinal OCT: probability constrained graph-search-graph-cut,” IEEE Trans. Med. Imaging 31(8), 1521–1531 (2012).
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G. Quellec, K. Lee, M. Dolejsi, M. K. Garvin, M. D. Abràmoff, and M. Sonka, “Three-dimensional analysis of retinal layer texture: identification of fluid-filled regions in SD-OCT of the macula,” IEEE Trans. Med. Imaging 29(6), 1321–1330 (2010).
[Crossref] [PubMed]

K. Lee, M. Niemeijer, M. K. Garvin, Y. H. Kwon, M. Sonka, and M. D. Abramoff, “Segmentation of the optic disc in 3-D OCT scans of the optic nerve head,” IEEE Trans. Med. Imaging 29(1), 159–168 (2010).
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Q. Chen, T. Leng, L. Zheng, L. Kutzscher, J. Ma, L. de Sisternes, and D. L. Rubin, “Automated drusen segmentation and quantification in SD-OCT images,” Med. Image Anal. 17(8), 1058–1072 (2013).
[Crossref] [PubMed]

Li, C.

R. Hua, L. Liu, C. Li, and L. Chen, “Evaluation of the effects of photodynamic therapy on chronic central serous chorioretinopathy based on the mean choroidal thickness and the lumen area of abnormal choroidal vessels,” Photodiagn. Photodyn. Ther. 11(4), 519–525 (2014).
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Li, F. P.

M. Rajchl, J. Yuan, J. A. White, E. Ukwatta, J. Stirrat, C. M. Nambakhsh, F. P. Li, and T. M. Peters, “Interactive hierarchical-flow segmentation of scar tissue from late-enhancement cardiac MR images,” IEEE Trans. Med. Imaging 33(1), 159–172 (2014).
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Li, K.

K. Li, X. Wu, D. Z. Chen, and M. Sonka, “Optimal surface segmentation in volumetric images-a graph-theoretic approach,” IEEE Trans. Pattern Anal. Mach. Intell. 28(1), 119–134 (2006).
[Crossref] [PubMed]

Li, L.

Z. Sun, H. Chen, F. Shi, L. Wang, W. Zhu, D. Xiang, C. Yan, L. Li, and X. Chen, “An automated framework for 3D serous pigment epithelium detachment segmentation in SD-OCT images,” Sci. Rep. 6(1), 21739 (2016).
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Liu, L.

J. Wang, M. Zhang, A. D. Pechauer, L. Liu, T. S. Hwang, D. J. Wilson, D. Li, and Y. Jia, “Automated volumetric segmentation of retinal fluid on optical coherence tomography,” Biomed. Opt. Express 7(4), 1577–1589 (2016).
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R. Hua, L. Liu, C. Li, and L. Chen, “Evaluation of the effects of photodynamic therapy on chronic central serous chorioretinopathy based on the mean choroidal thickness and the lumen area of abnormal choroidal vessels,” Photodiagn. Photodyn. Ther. 11(4), 519–525 (2014).
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T. Wang, Z. Ji, Q. Sun, Q. Chen, S. Yu, W. Fan, S. Yuan, and Q. Liu, “Label propagation and higher-order constraint-based segmentation of fluid-associated regions in retinal SD-OCT images,” Inf. Sci. 358, 92–111 (2016).
[Crossref]

Ma, J.

Q. Chen, T. Leng, L. Zheng, L. Kutzscher, J. Ma, L. de Sisternes, and D. L. Rubin, “Automated drusen segmentation and quantification in SD-OCT images,” Med. Image Anal. 17(8), 1058–1072 (2013).
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A. Daruich, A. Matet, A. Dirani, E. Bousquet, M. Zhao, N. Farman, F. Jaisser, and F. Behar-Cohen, “Central serous chorioretinopathy: recent findings and new physiopathology hypothesis,” Prog. Retin. Eye Res. 48, 82–118 (2015).
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Mettu, P. S.

Montuoro, A.

Mrejen, S.

K. K. Dansingani, C. Balaratnasingam, S. Mrejen, M. Inoue, K. B. Freund, J. M. Klancnik, and L. A. Yannuzzi, “Annular lesions and catenary corms in chronic central serous chorioretinopathy,” Am. J. Ophthalmol. 166, 60–67 (2016).
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Nakanishi, H.

Y. Kuroda, S. Ooto, K. Yamashiro, A. Oishi, H. Nakanishi, H. Tamura, N. Ueda-Arakawa, and N. Yoshimura, “Increased choroidal vascularity in central serous chorioretinopathy quantified using swept-source optical coherence tomography,” Am. J. Ophthalmol. 169, 199–207 (2016).
[Crossref] [PubMed]

Nambakhsh, C. M.

M. Rajchl, J. Yuan, J. A. White, E. Ukwatta, J. Stirrat, C. M. Nambakhsh, F. P. Li, and T. M. Peters, “Interactive hierarchical-flow segmentation of scar tissue from late-enhancement cardiac MR images,” IEEE Trans. Med. Imaging 33(1), 159–172 (2014).
[Crossref] [PubMed]

Navab, N.

Niemeijer, M.

X. Chen, M. Niemeijer, L. Zhang, K. Lee, M. D. Abramoff, and M. Sonka, “Three-dimensional segmentation of fluid-associated abnormalities in retinal OCT: probability constrained graph-search-graph-cut,” IEEE Trans. Med. Imaging 31(8), 1521–1531 (2012).
[Crossref] [PubMed]

K. Lee, M. Niemeijer, M. K. Garvin, Y. H. Kwon, M. Sonka, and M. D. Abramoff, “Segmentation of the optic disc in 3-D OCT scans of the optic nerve head,” IEEE Trans. Med. Imaging 29(1), 159–168 (2010).
[Crossref] [PubMed]

Novosel, J.

J. Novosel, K. A. Vermeer, J. H. de Jong, Ziyuan Wang, and L. J. van Vliet, “Joint Segmentation of Retinal Layers and Focal Lesions in 3-D OCT Data of Topologically Disrupted Retinas,” IEEE Trans. Med. Imaging 36(6), 1276–1286 (2017).
[Crossref] [PubMed]

J. Novosel, Z. Wang, H. de Jong, M. van Velthoven, K. A. Vermeer, and L. J. van Vliet, “Locally-adaptive loosely-coupled level sets for retinal layer and fluid segmentation in subjects with central serous retinopathy,” in Proceedings of IEEE International Symposium on Biomedical Imaging (IEEE, 2016), pp. 702–705.
[Crossref]

Oishi, A.

Y. Kuroda, S. Ooto, K. Yamashiro, A. Oishi, H. Nakanishi, H. Tamura, N. Ueda-Arakawa, and N. Yoshimura, “Increased choroidal vascularity in central serous chorioretinopathy quantified using swept-source optical coherence tomography,” Am. J. Ophthalmol. 169, 199–207 (2016).
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Ooto, S.

Y. Kuroda, S. Ooto, K. Yamashiro, A. Oishi, H. Nakanishi, H. Tamura, N. Ueda-Arakawa, and N. Yoshimura, “Increased choroidal vascularity in central serous chorioretinopathy quantified using swept-source optical coherence tomography,” Am. J. Ophthalmol. 169, 199–207 (2016).
[Crossref] [PubMed]

Pechauer, A. D.

Penha, F. M.

F. M. Penha, P. J. Rosenfeld, G. Gregori, M. Falcão, Z. Yehoshua, F. Wang, and W. J. Feuer, “Quantitative imaging of retinal pigment epithelial detachments using spectral-domain optical coherence tomography,” Am. J. Ophthalmol. 153(3), 515–523 (2012).
[Crossref] [PubMed]

Peters, T. M.

M. Rajchl, J. Yuan, J. A. White, E. Ukwatta, J. Stirrat, C. M. Nambakhsh, F. P. Li, and T. M. Peters, “Interactive hierarchical-flow segmentation of scar tissue from late-enhancement cardiac MR images,” IEEE Trans. Med. Imaging 33(1), 159–172 (2014).
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Pircher, M.

P. Roberts, B. Baumann, J. Lammer, B. Gerendas, J. Kroisamer, W. Bühl, M. Pircher, C. K. Hitzenberger, U. Schmidt-Erfurth, and S. Sacu, “Retinal Pigment Epithelial Features in Central Serous Chorioretinopathy Identified by Polarization-Sensitive Optical Coherence Tomography,” Invest. Ophthalmol. Vis. Sci. 57(4), 1595–1603 (2016).
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Prince, J. L.

Qiu, W.

W. Qiu, J. Yuan, E. Ukwatta, Y. Sun, M. Rajchl, and A. Fenster, “Prostate segmentation: An efficient convex optimization approach with axial symmetry using 3-D TRUS and MR images,” IEEE Trans. Med. Imaging 33(4), 947–960 (2014).
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Quellec, G.

G. Quellec, K. Lee, M. Dolejsi, M. K. Garvin, M. D. Abràmoff, and M. Sonka, “Three-dimensional analysis of retinal layer texture: identification of fluid-filled regions in SD-OCT of the macula,” IEEE Trans. Med. Imaging 29(6), 1321–1330 (2010).
[Crossref] [PubMed]

Rajchl, M.

M. Rajchl, J. Yuan, J. A. White, E. Ukwatta, J. Stirrat, C. M. Nambakhsh, F. P. Li, and T. M. Peters, “Interactive hierarchical-flow segmentation of scar tissue from late-enhancement cardiac MR images,” IEEE Trans. Med. Imaging 33(1), 159–172 (2014).
[Crossref] [PubMed]

W. Qiu, J. Yuan, E. Ukwatta, Y. Sun, M. Rajchl, and A. Fenster, “Prostate segmentation: An efficient convex optimization approach with axial symmetry using 3-D TRUS and MR images,” IEEE Trans. Med. Imaging 33(4), 947–960 (2014).
[Crossref] [PubMed]

Roberts, P.

P. Roberts, B. Baumann, J. Lammer, B. Gerendas, J. Kroisamer, W. Bühl, M. Pircher, C. K. Hitzenberger, U. Schmidt-Erfurth, and S. Sacu, “Retinal Pigment Epithelial Features in Central Serous Chorioretinopathy Identified by Polarization-Sensitive Optical Coherence Tomography,” Invest. Ophthalmol. Vis. Sci. 57(4), 1595–1603 (2016).
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Rogowska, J.

J. Rogowska and M. E. Brezinski, “Evaluation of the adaptive speckle suppression filter for coronary optical coherence tomography imaging,” IEEE Trans. Med. Imaging 19(12), 1261–1266 (2000).
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Rosenfeld, P. J.

F. M. Penha, P. J. Rosenfeld, G. Gregori, M. Falcão, Z. Yehoshua, F. Wang, and W. J. Feuer, “Quantitative imaging of retinal pigment epithelial detachments using spectral-domain optical coherence tomography,” Am. J. Ophthalmol. 153(3), 515–523 (2012).
[Crossref] [PubMed]

Roy, A. G.

Rubin, D. L.

Q. Chen, T. Leng, L. Zheng, L. Kutzscher, J. Ma, L. de Sisternes, and D. L. Rubin, “Automated drusen segmentation and quantification in SD-OCT images,” Med. Image Anal. 17(8), 1058–1072 (2013).
[Crossref] [PubMed]

Russell, S. R.

M. K. Garvin, M. D. Abràmoff, X. Wu, S. R. Russell, T. L. Burns, and M. Sonka, “Automated 3-D intraretinal layer segmentation of macular spectral-domain optical coherence tomography images,” IEEE Trans. Med. Imaging 28(9), 1436–1447 (2009).
[Crossref] [PubMed]

Sacu, S.

P. Roberts, B. Baumann, J. Lammer, B. Gerendas, J. Kroisamer, W. Bühl, M. Pircher, C. K. Hitzenberger, U. Schmidt-Erfurth, and S. Sacu, “Retinal Pigment Epithelial Features in Central Serous Chorioretinopathy Identified by Polarization-Sensitive Optical Coherence Tomography,” Invest. Ophthalmol. Vis. Sci. 57(4), 1595–1603 (2016).
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Saidha, S.

Schmidt, H.

J. K. Udupa, V. R. Leblanc, Y. Zhuge, C. Imielinska, H. Schmidt, L. M. Currie, B. E. Hirsch, and J. Woodburn, “A framework for evaluating image segmentation algorithms,” Comput. Med. Imaging Graph. 30(2), 75–87 (2006).
[Crossref] [PubMed]

Schmidt-Erfurth, U.

A. Montuoro, S. M. Waldstein, B. S. Gerendas, U. Schmidt-Erfurth, and H. Bogunović, “Joint retinal layer and fluid segmentation in OCT scans of eyes with severe macular edema using unsupervised representation and auto-context,” Biomed. Opt. Express 8(3), 1874–1888 (2017).
[Crossref] [PubMed]

P. Roberts, B. Baumann, J. Lammer, B. Gerendas, J. Kroisamer, W. Bühl, M. Pircher, C. K. Hitzenberger, U. Schmidt-Erfurth, and S. Sacu, “Retinal Pigment Epithelial Features in Central Serous Chorioretinopathy Identified by Polarization-Sensitive Optical Coherence Tomography,” Invest. Ophthalmol. Vis. Sci. 57(4), 1595–1603 (2016).
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P. A. Dufour, L. Ceklic, H. Abdillahi, S. Schröder, S. De Dzanet, U. Wolf-Schnurrbusch, and J. Kowal, “Graph-based multi-surface segmentation of OCT data using trained hard and soft constraints,” IEEE Trans. Med. Imaging 32(3), 531–543 (2013).
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Sheet, D.

Shi, F.

Z. Sun, H. Chen, F. Shi, L. Wang, W. Zhu, D. Xiang, C. Yan, L. Li, and X. Chen, “An automated framework for 3D serous pigment epithelium detachment segmentation in SD-OCT images,” Sci. Rep. 6(1), 21739 (2016).
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F. Shi, X. Chen, H. Zhao, W. Zhu, D. Xiang, E. Gao, M. Sonka, and H. Chen, “Automated 3-D retinal layer segmentation of macular optical coherence tomography images with serous pigment epithelial detachments,” IEEE Trans. Med. Imaging 34(2), 441–452 (2015).
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Sonka, M.

F. Shi, X. Chen, H. Zhao, W. Zhu, D. Xiang, E. Gao, M. Sonka, and H. Chen, “Automated 3-D retinal layer segmentation of macular optical coherence tomography images with serous pigment epithelial detachments,” IEEE Trans. Med. Imaging 34(2), 441–452 (2015).
[Crossref] [PubMed]

X. Xu, K. Lee, L. Zhang, M. Sonka, and M. Abramoff, “Stratified sampling voxel classification for segmentation of intraretinal and subretinal fluid in longitudinal clinical OCT data,” IEEE Trans. Med. Imaging 34(7), 1616–1623 (2015).
[Crossref] [PubMed]

X. Chen, M. Niemeijer, L. Zhang, K. Lee, M. D. Abramoff, and M. Sonka, “Three-dimensional segmentation of fluid-associated abnormalities in retinal OCT: probability constrained graph-search-graph-cut,” IEEE Trans. Med. Imaging 31(8), 1521–1531 (2012).
[Crossref] [PubMed]

G. Quellec, K. Lee, M. Dolejsi, M. K. Garvin, M. D. Abràmoff, and M. Sonka, “Three-dimensional analysis of retinal layer texture: identification of fluid-filled regions in SD-OCT of the macula,” IEEE Trans. Med. Imaging 29(6), 1321–1330 (2010).
[Crossref] [PubMed]

K. Lee, M. Niemeijer, M. K. Garvin, Y. H. Kwon, M. Sonka, and M. D. Abramoff, “Segmentation of the optic disc in 3-D OCT scans of the optic nerve head,” IEEE Trans. Med. Imaging 29(1), 159–168 (2010).
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M. D. Abràmoff, M. K. Garvin, and M. Sonka, “Retinal imaging and image analysis,” IEEE Rev. Biomed. Eng. 3, 169–208 (2010).
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M. K. Garvin, M. D. Abràmoff, X. Wu, S. R. Russell, T. L. Burns, and M. Sonka, “Automated 3-D intraretinal layer segmentation of macular spectral-domain optical coherence tomography images,” IEEE Trans. Med. Imaging 28(9), 1436–1447 (2009).
[Crossref] [PubMed]

K. Li, X. Wu, D. Z. Chen, and M. Sonka, “Optimal surface segmentation in volumetric images-a graph-theoretic approach,” IEEE Trans. Pattern Anal. Mach. Intell. 28(1), 119–134 (2006).
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Stirrat, J.

M. Rajchl, J. Yuan, J. A. White, E. Ukwatta, J. Stirrat, C. M. Nambakhsh, F. P. Li, and T. M. Peters, “Interactive hierarchical-flow segmentation of scar tissue from late-enhancement cardiac MR images,” IEEE Trans. Med. Imaging 33(1), 159–172 (2014).
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T. Wang, Z. Ji, Q. Sun, Q. Chen, S. Yu, W. Fan, S. Yuan, and Q. Liu, “Label propagation and higher-order constraint-based segmentation of fluid-associated regions in retinal SD-OCT images,” Inf. Sci. 358, 92–111 (2016).
[Crossref]

Sun, Y.

W. Qiu, J. Yuan, E. Ukwatta, Y. Sun, M. Rajchl, and A. Fenster, “Prostate segmentation: An efficient convex optimization approach with axial symmetry using 3-D TRUS and MR images,” IEEE Trans. Med. Imaging 33(4), 947–960 (2014).
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Sun, Z.

Z. Sun, H. Chen, F. Shi, L. Wang, W. Zhu, D. Xiang, C. Yan, L. Li, and X. Chen, “An automated framework for 3D serous pigment epithelium detachment segmentation in SD-OCT images,” Sci. Rep. 6(1), 21739 (2016).
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Syed, A. M.

Tai, X. C.

J. Yuan, E. Bae, and X. C. Tai, “A study on continuous max-flow and min-cut approaches,” inProceeding of IEEE Conference on Computer Vision and Pattern Recognition (IEEE, 2010), pp. 2217–2224.
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Tamura, H.

Y. Kuroda, S. Ooto, K. Yamashiro, A. Oishi, H. Nakanishi, H. Tamura, N. Ueda-Arakawa, and N. Yoshimura, “Increased choroidal vascularity in central serous chorioretinopathy quantified using swept-source optical coherence tomography,” Am. J. Ophthalmol. 169, 199–207 (2016).
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S. Farsiu, S. J. Chiu, J. A. Izatt, and C. A. Toth, “Fast detection and segmentation of drusen in retinal optical coherence tomography images,” Proc. SPIE 6844, 68440D (2008).
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J. K. Udupa, V. R. Leblanc, Y. Zhuge, C. Imielinska, H. Schmidt, L. M. Currie, B. E. Hirsch, and J. Woodburn, “A framework for evaluating image segmentation algorithms,” Comput. Med. Imaging Graph. 30(2), 75–87 (2006).
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Ueda-Arakawa, N.

Y. Kuroda, S. Ooto, K. Yamashiro, A. Oishi, H. Nakanishi, H. Tamura, N. Ueda-Arakawa, and N. Yoshimura, “Increased choroidal vascularity in central serous chorioretinopathy quantified using swept-source optical coherence tomography,” Am. J. Ophthalmol. 169, 199–207 (2016).
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Ukwatta, E.

W. Qiu, J. Yuan, E. Ukwatta, Y. Sun, M. Rajchl, and A. Fenster, “Prostate segmentation: An efficient convex optimization approach with axial symmetry using 3-D TRUS and MR images,” IEEE Trans. Med. Imaging 33(4), 947–960 (2014).
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M. Rajchl, J. Yuan, J. A. White, E. Ukwatta, J. Stirrat, C. M. Nambakhsh, F. P. Li, and T. M. Peters, “Interactive hierarchical-flow segmentation of scar tissue from late-enhancement cardiac MR images,” IEEE Trans. Med. Imaging 33(1), 159–172 (2014).
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van Velthoven, M.

J. Novosel, Z. Wang, H. de Jong, M. van Velthoven, K. A. Vermeer, and L. J. van Vliet, “Locally-adaptive loosely-coupled level sets for retinal layer and fluid segmentation in subjects with central serous retinopathy,” in Proceedings of IEEE International Symposium on Biomedical Imaging (IEEE, 2016), pp. 702–705.
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van Vliet, L. J.

J. Novosel, K. A. Vermeer, J. H. de Jong, Ziyuan Wang, and L. J. van Vliet, “Joint Segmentation of Retinal Layers and Focal Lesions in 3-D OCT Data of Topologically Disrupted Retinas,” IEEE Trans. Med. Imaging 36(6), 1276–1286 (2017).
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J. Novosel, Z. Wang, H. de Jong, M. van Velthoven, K. A. Vermeer, and L. J. van Vliet, “Locally-adaptive loosely-coupled level sets for retinal layer and fluid segmentation in subjects with central serous retinopathy,” in Proceedings of IEEE International Symposium on Biomedical Imaging (IEEE, 2016), pp. 702–705.
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Vermeer, K. A.

J. Novosel, K. A. Vermeer, J. H. de Jong, Ziyuan Wang, and L. J. van Vliet, “Joint Segmentation of Retinal Layers and Focal Lesions in 3-D OCT Data of Topologically Disrupted Retinas,” IEEE Trans. Med. Imaging 36(6), 1276–1286 (2017).
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J. Novosel, Z. Wang, H. de Jong, M. van Velthoven, K. A. Vermeer, and L. J. van Vliet, “Locally-adaptive loosely-coupled level sets for retinal layer and fluid segmentation in subjects with central serous retinopathy,” in Proceedings of IEEE International Symposium on Biomedical Imaging (IEEE, 2016), pp. 702–705.
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Wachinger, C.

Waldstein, S. M.

Wang, C.

Wang, F.

F. M. Penha, P. J. Rosenfeld, G. Gregori, M. Falcão, Z. Yehoshua, F. Wang, and W. J. Feuer, “Quantitative imaging of retinal pigment epithelial detachments using spectral-domain optical coherence tomography,” Am. J. Ophthalmol. 153(3), 515–523 (2012).
[Crossref] [PubMed]

Wang, J.

Wang, L.

Z. Sun, H. Chen, F. Shi, L. Wang, W. Zhu, D. Xiang, C. Yan, L. Li, and X. Chen, “An automated framework for 3D serous pigment epithelium detachment segmentation in SD-OCT images,” Sci. Rep. 6(1), 21739 (2016).
[Crossref] [PubMed]

Wang, T.

T. Wang, Z. Ji, Q. Sun, Q. Chen, S. Yu, W. Fan, S. Yuan, and Q. Liu, “Label propagation and higher-order constraint-based segmentation of fluid-associated regions in retinal SD-OCT images,” Inf. Sci. 358, 92–111 (2016).
[Crossref]

Wang, Z.

J. Novosel, Z. Wang, H. de Jong, M. van Velthoven, K. A. Vermeer, and L. J. van Vliet, “Locally-adaptive loosely-coupled level sets for retinal layer and fluid segmentation in subjects with central serous retinopathy,” in Proceedings of IEEE International Symposium on Biomedical Imaging (IEEE, 2016), pp. 702–705.
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White, J. A.

M. Rajchl, J. Yuan, J. A. White, E. Ukwatta, J. Stirrat, C. M. Nambakhsh, F. P. Li, and T. M. Peters, “Interactive hierarchical-flow segmentation of scar tissue from late-enhancement cardiac MR images,” IEEE Trans. Med. Imaging 33(1), 159–172 (2014).
[Crossref] [PubMed]

Wilson, D. J.

Wolf-Schnurrbusch, U.

P. A. Dufour, L. Ceklic, H. Abdillahi, S. Schröder, S. De Dzanet, U. Wolf-Schnurrbusch, and J. Kowal, “Graph-based multi-surface segmentation of OCT data using trained hard and soft constraints,” IEEE Trans. Med. Imaging 32(3), 531–543 (2013).
[Crossref] [PubMed]

Woodburn, J.

J. K. Udupa, V. R. Leblanc, Y. Zhuge, C. Imielinska, H. Schmidt, L. M. Currie, B. E. Hirsch, and J. Woodburn, “A framework for evaluating image segmentation algorithms,” Comput. Med. Imaging Graph. 30(2), 75–87 (2006).
[Crossref] [PubMed]

Wu, X.

M. K. Garvin, M. D. Abràmoff, X. Wu, S. R. Russell, T. L. Burns, and M. Sonka, “Automated 3-D intraretinal layer segmentation of macular spectral-domain optical coherence tomography images,” IEEE Trans. Med. Imaging 28(9), 1436–1447 (2009).
[Crossref] [PubMed]

K. Li, X. Wu, D. Z. Chen, and M. Sonka, “Optimal surface segmentation in volumetric images-a graph-theoretic approach,” IEEE Trans. Pattern Anal. Mach. Intell. 28(1), 119–134 (2006).
[Crossref] [PubMed]

Xiang, D.

Z. Sun, H. Chen, F. Shi, L. Wang, W. Zhu, D. Xiang, C. Yan, L. Li, and X. Chen, “An automated framework for 3D serous pigment epithelium detachment segmentation in SD-OCT images,” Sci. Rep. 6(1), 21739 (2016).
[Crossref] [PubMed]

F. Shi, X. Chen, H. Zhao, W. Zhu, D. Xiang, E. Gao, M. Sonka, and H. Chen, “Automated 3-D retinal layer segmentation of macular optical coherence tomography images with serous pigment epithelial detachments,” IEEE Trans. Med. Imaging 34(2), 441–452 (2015).
[Crossref] [PubMed]

Xu, X.

X. Xu, K. Lee, L. Zhang, M. Sonka, and M. Abramoff, “Stratified sampling voxel classification for segmentation of intraretinal and subretinal fluid in longitudinal clinical OCT data,” IEEE Trans. Med. Imaging 34(7), 1616–1623 (2015).
[Crossref] [PubMed]

Yamashiro, K.

Y. Kuroda, S. Ooto, K. Yamashiro, A. Oishi, H. Nakanishi, H. Tamura, N. Ueda-Arakawa, and N. Yoshimura, “Increased choroidal vascularity in central serous chorioretinopathy quantified using swept-source optical coherence tomography,” Am. J. Ophthalmol. 169, 199–207 (2016).
[Crossref] [PubMed]

Yan, C.

Z. Sun, H. Chen, F. Shi, L. Wang, W. Zhu, D. Xiang, C. Yan, L. Li, and X. Chen, “An automated framework for 3D serous pigment epithelium detachment segmentation in SD-OCT images,” Sci. Rep. 6(1), 21739 (2016).
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Yannuzzi, L. A.

K. K. Dansingani, C. Balaratnasingam, S. Mrejen, M. Inoue, K. B. Freund, J. M. Klancnik, and L. A. Yannuzzi, “Annular lesions and catenary corms in chronic central serous chorioretinopathy,” Am. J. Ophthalmol. 166, 60–67 (2016).
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Yehoshua, Z.

F. M. Penha, P. J. Rosenfeld, G. Gregori, M. Falcão, Z. Yehoshua, F. Wang, and W. J. Feuer, “Quantitative imaging of retinal pigment epithelial detachments using spectral-domain optical coherence tomography,” Am. J. Ophthalmol. 153(3), 515–523 (2012).
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Ying, H. S.

Yoshimura, N.

Y. Kuroda, S. Ooto, K. Yamashiro, A. Oishi, H. Nakanishi, H. Tamura, N. Ueda-Arakawa, and N. Yoshimura, “Increased choroidal vascularity in central serous chorioretinopathy quantified using swept-source optical coherence tomography,” Am. J. Ophthalmol. 169, 199–207 (2016).
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Yu, H. G.

S. J. Ahn, T. W. Kim, J. W. Huh, H. G. Yu, and H. Chung, “Comparison of features on SD-OCT between acute central serous chorioretinopathy and exudative age-related macular degeneration,” Ophthalmic Surg. Lasers Imaging 43(5), 374–382 (2012).
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T. Wang, Z. Ji, Q. Sun, Q. Chen, S. Yu, W. Fan, S. Yuan, and Q. Liu, “Label propagation and higher-order constraint-based segmentation of fluid-associated regions in retinal SD-OCT images,” Inf. Sci. 358, 92–111 (2016).
[Crossref]

Yuan, J.

W. Qiu, J. Yuan, E. Ukwatta, Y. Sun, M. Rajchl, and A. Fenster, “Prostate segmentation: An efficient convex optimization approach with axial symmetry using 3-D TRUS and MR images,” IEEE Trans. Med. Imaging 33(4), 947–960 (2014).
[Crossref] [PubMed]

M. Rajchl, J. Yuan, J. A. White, E. Ukwatta, J. Stirrat, C. M. Nambakhsh, F. P. Li, and T. M. Peters, “Interactive hierarchical-flow segmentation of scar tissue from late-enhancement cardiac MR images,” IEEE Trans. Med. Imaging 33(1), 159–172 (2014).
[Crossref] [PubMed]

J. Yuan, E. Bae, and X. C. Tai, “A study on continuous max-flow and min-cut approaches,” inProceeding of IEEE Conference on Computer Vision and Pattern Recognition (IEEE, 2010), pp. 2217–2224.
[Crossref]

Yuan, S.

T. Wang, Z. Ji, Q. Sun, Q. Chen, S. Yu, W. Fan, S. Yuan, and Q. Liu, “Label propagation and higher-order constraint-based segmentation of fluid-associated regions in retinal SD-OCT images,” Inf. Sci. 358, 92–111 (2016).
[Crossref]

Zhang, L.

X. Xu, K. Lee, L. Zhang, M. Sonka, and M. Abramoff, “Stratified sampling voxel classification for segmentation of intraretinal and subretinal fluid in longitudinal clinical OCT data,” IEEE Trans. Med. Imaging 34(7), 1616–1623 (2015).
[Crossref] [PubMed]

X. Chen, M. Niemeijer, L. Zhang, K. Lee, M. D. Abramoff, and M. Sonka, “Three-dimensional segmentation of fluid-associated abnormalities in retinal OCT: probability constrained graph-search-graph-cut,” IEEE Trans. Med. Imaging 31(8), 1521–1531 (2012).
[Crossref] [PubMed]

Zhang, M.

Zhao, H.

F. Shi, X. Chen, H. Zhao, W. Zhu, D. Xiang, E. Gao, M. Sonka, and H. Chen, “Automated 3-D retinal layer segmentation of macular optical coherence tomography images with serous pigment epithelial detachments,” IEEE Trans. Med. Imaging 34(2), 441–452 (2015).
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Zhao, M.

A. Daruich, A. Matet, A. Dirani, E. Bousquet, M. Zhao, N. Farman, F. Jaisser, and F. Behar-Cohen, “Central serous chorioretinopathy: recent findings and new physiopathology hypothesis,” Prog. Retin. Eye Res. 48, 82–118 (2015).
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Zheng, L.

Q. Chen, T. Leng, L. Zheng, L. Kutzscher, J. Ma, L. de Sisternes, and D. L. Rubin, “Automated drusen segmentation and quantification in SD-OCT images,” Med. Image Anal. 17(8), 1058–1072 (2013).
[Crossref] [PubMed]

Zhu, W.

Z. Sun, H. Chen, F. Shi, L. Wang, W. Zhu, D. Xiang, C. Yan, L. Li, and X. Chen, “An automated framework for 3D serous pigment epithelium detachment segmentation in SD-OCT images,” Sci. Rep. 6(1), 21739 (2016).
[Crossref] [PubMed]

F. Shi, X. Chen, H. Zhao, W. Zhu, D. Xiang, E. Gao, M. Sonka, and H. Chen, “Automated 3-D retinal layer segmentation of macular optical coherence tomography images with serous pigment epithelial detachments,” IEEE Trans. Med. Imaging 34(2), 441–452 (2015).
[Crossref] [PubMed]

Zhuge, Y.

J. K. Udupa, V. R. Leblanc, Y. Zhuge, C. Imielinska, H. Schmidt, L. M. Currie, B. E. Hirsch, and J. Woodburn, “A framework for evaluating image segmentation algorithms,” Comput. Med. Imaging Graph. 30(2), 75–87 (2006).
[Crossref] [PubMed]

Ziyuan Wang,

J. Novosel, K. A. Vermeer, J. H. de Jong, Ziyuan Wang, and L. J. van Vliet, “Joint Segmentation of Retinal Layers and Focal Lesions in 3-D OCT Data of Topologically Disrupted Retinas,” IEEE Trans. Med. Imaging 36(6), 1276–1286 (2017).
[Crossref] [PubMed]

Am. J. Ophthalmol. (3)

K. K. Dansingani, C. Balaratnasingam, S. Mrejen, M. Inoue, K. B. Freund, J. M. Klancnik, and L. A. Yannuzzi, “Annular lesions and catenary corms in chronic central serous chorioretinopathy,” Am. J. Ophthalmol. 166, 60–67 (2016).
[Crossref] [PubMed]

Y. Kuroda, S. Ooto, K. Yamashiro, A. Oishi, H. Nakanishi, H. Tamura, N. Ueda-Arakawa, and N. Yoshimura, “Increased choroidal vascularity in central serous chorioretinopathy quantified using swept-source optical coherence tomography,” Am. J. Ophthalmol. 169, 199–207 (2016).
[Crossref] [PubMed]

F. M. Penha, P. J. Rosenfeld, G. Gregori, M. Falcão, Z. Yehoshua, F. Wang, and W. J. Feuer, “Quantitative imaging of retinal pigment epithelial detachments using spectral-domain optical coherence tomography,” Am. J. Ophthalmol. 153(3), 515–523 (2012).
[Crossref] [PubMed]

Appl. Opt. (1)

Biomed. Opt. Express (6)

A. Montuoro, S. M. Waldstein, B. S. Gerendas, U. Schmidt-Erfurth, and H. Bogunović, “Joint retinal layer and fluid segmentation in OCT scans of eyes with severe macular edema using unsupervised representation and auto-context,” Biomed. Opt. Express 8(3), 1874–1888 (2017).
[Crossref] [PubMed]

A. G. Roy, S. Conjeti, S. P. K. Karri, D. Sheet, A. Katouzian, C. Wachinger, and N. Navab, “ReLayNet: Retinal Layer and Fluid Segmentation of Macular Optical Coherence Tomography using Fully Convolutional Network,” Biomed. Opt. Express 8(8), 3627–3642 (2017).
[Crossref]

L. Fang, D. Cunefare, C. Wang, R. H. Guymer, S. Li, and S. Farsiu, “Automatic segmentation of nine retinal layer boundaries in OCT images of non-exudative AMD patients using deep learning and graph search,” Biomed. Opt. Express 8(5), 2732–2744 (2017).
[Crossref] [PubMed]

J. Wang, M. Zhang, A. D. Pechauer, L. Liu, T. S. Hwang, D. J. Wilson, D. Li, and Y. Jia, “Automated volumetric segmentation of retinal fluid on optical coherence tomography,” Biomed. Opt. Express 7(4), 1577–1589 (2016).
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Figures (11)

Fig. 1
Fig. 1 (a) SD-OCT B-scan with PED- and NRD-associated fluid. The red curve denotes the internal limiting membrane (ILM), the green curve represents the elevated RPE floor, and the blue curve indicates the interpolated normal RPE. (b) The en face projection between the ILM and RPE floor. (c) The en face projection between the RPE floor and interpolated RPE. The dark dashed green line indicates the position of the B-scan presented in (a).
Fig. 2
Fig. 2 The flowchart of the proposed segmentation framework.
Fig. 3
Fig. 3 Probability map construction. (a) The SD-OCT volume. (b) The thickness map generated between the ILM and RPE for NRD detection, and (c) the thickness map generated between the RPE and interpolated RPE for PED detection. The black dashed curve denotes the contour of the fluid region in the thickness map. (d - f) The structural texture score, intensity score, and probability map for NRD detection, respectively, in the current OCT B-scan. (g - i) The structural texture score, intensity score, and probability map for PED detection, respectively, in the current OCT B-scan. The yellow dash line indicates the restricted region in B-scan.
Fig. 4
Fig. 4 Segmenting the probability map using continuous max flow optimization. (a) The probability map for NRD segmentation. (b) The probability map for PED segmentation. (c) The continuous max flow model. (d) The NRD and PED 3-D segmentation results. The purple and green regions denote NRD and PED, respectively. The blue region indicates the interpolated RPE.
Fig. 5
Fig. 5 Examples of fluid segmentation of CSC cases with only NRD. (a) Results in the B-scan. The results of the reference standard (first column), initial classification (second column), the fuzzy level set method (third column), and the proposed method (fourth column) are shown. The blue and yellow arrows indicate the false positives and false negatives, respectively. (b) The corresponding 3-D segmentation results of the proposed method. The blue and purple regions denote the RPE floor and area of NRD, respectively. The dashed red rectangle shows the fluid region with a weak contour.
Fig. 6
Fig. 6 Examples of fluid segmentation of CSC cases with only PED. (a) Results in the B-scan. The results of the reference standard (first column), initial classification (second column), the layer segmentation method (third column), and the proposed method (fourth column) are shown. The yellow arrow indicates the false negatives. (b) The corresponding 3-D segmentation results of the proposed method. The blue and green regions denote the interpolated RPE floor and area of PED, respectively.
Fig. 7
Fig. 7 Examples of fluid segmentation of CSC cases with both NRD and PED. (a) The results of the reference standard (first column), initial classification (second column), and the proposed method (third column) are shown. The blue and yellow arrows indicate the false positives and false negatives, respectively. (b) The corresponding 3-D segmentation results of the proposed method. The blue, purple and green regions denote the interpolated RPE, area of NRD and area of PED, respectively.
Fig. 8
Fig. 8 Statistical correlation analysis for NRD volume evaluation. (a) Correlation analysis between the proposed method and the specialist 1. (b) Bland-Altman plot for the proposed method. The blue and purple rectangles denote the CSC subjects with only NRD and those with both NRD and PED, respectively.
Fig. 9
Fig. 9 Statistical correlation analysis for PED volume evaluation. (a) Correlation analysis between the proposed method and the specialist 1. (b) Bland-Altman plot for the proposed method. The blue and purple rectangles denote the CSC subjects with only PED and those with both NRD and PED, respectively.
Fig. 10
Fig. 10 Statistical correlation analysis of reproducibility comparing manual tracings of specialist 1 and specialist 2 for NRD volume. (a) Correlation analysis between the specialist 1 and the specialist 2. (b) Bland-Altman plot.
Fig. 11
Fig. 11 Statistical correlation analysis of reproducibility comparing manual tracings of specialist 1 and specialist 2 for PED volume. (a) Correlation analysis between the specialist 1 and the specialist 2. (b) Bland-Altman plot.

Tables (3)

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Table 1 List of features for classification

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Table 2 Comparison of results from the initial classification, fuzzy level set approach, and the proposed method with respect to the reference standard for NRD segmentation

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Table 3 Comparison of results from the initial classification, layer segmentation approach and the proposed method with respect to the reference standard for PED segmentation

Equations (15)

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S p = λ 1 S t + λ 2 S m + λ 3 S i
u ( x ) = { 1 , f o r x R o 0 , f o r x R b
min u ( x ) { 0 , 1 } E ( u ) = Ω ( 1 u ) C s d x + Ω u C t d x + Ω g ( x ) | u | d x
min u ( x ) [ 0 , 1 ] E ( u ) = Ω ( 1 u ) C s d x + Ω u C t d x + Ω g ( x ) | u | d x
max p s , p t , p Ω p s ( x ) d x s . t . | p ( x ) | g ( x ) p s ( x ) C s ( x ) , p t ( x ) C t ( x ) d i v p ( x ) p s ( x ) + p t ( x ) = 0
C s ( x ) = log ( S p ( x ) ) C t ( x ) = log ( 1 S p ( x ) ) .
L ( p s , p t , p , u ) = Ω p s d x + Ω u ( d i v p p s + p t ) d x μ 2 | | d i v p p s + p t | | 2
p k + 1 = arg max | p ( x ) | < g ( x ) μ 2 | | d i v p + p t k p s k u k / μ | | 2
p s k + 1 = arg max p s ( x ) C s ( x ) Ω p s d x μ 2 | | p s ( p t k + d i v p k + 1 ) + u k / μ | | 2
p t k + 1 = arg max p t ( x ) C t ( x ) μ 2 | | p t + d i v p k + 1 p s k u k / μ | | 2
u k + 1 = u k μ ( d i v p k + 1 p s k + 1 + p t k + 1 )
TPVF = | V A | | V T | | V T |
FPVF = | V A | | V A | | V T | | V | | V T |
PPV = | V A | | V T | | V A |
DSC = 2 × | V A V T | | V A | + | V T |

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