Abstract

The tear meniscus contains most of the tear fluid and therefore is a good indicator for the state of the tear film. Previously, we used a custom-built optical coherence tomography (OCT) system to study the lower tear meniscus by automatically segmenting the image data with a thresholding-based segmentation algorithm (TBSA). In this report, we investigate whether the results of this image segmentation algorithm are suitable to train a neural network in order to obtain similar or better segmentation results with shorter processing times. Considering the class imbalance problem, we compare two approaches, one directly segmenting the tear meniscus (DSA), the other first localizing the region of interest and then segmenting within the higher resolution image section (LSA). A total of 6658 images labeled by the TBSA were used to train deep convolutional neural networks with supervised learning. Five-fold cross-validation reveals a sensitivity of 96.36% and 96.43%, a specificity of 99.98% and 99.86% and a Jaccard index of 93.24% and 93.16% for the DSA and LSA, respectively. Average segmentation times are up to 228 times faster than the TBSA. Additionally, we report the behavior of the DSA and LSA in cases challenging for the TBSA and further test the applicability to measurements acquired with a commercially available OCT system. The application of deep learning for the segmentation of the tear meniscus provides a powerful tool for the assessment of the tear film, supporting studies for the investigation of the pathophysiology of dry eye-related diseases.

© 2020 Optical Society of America under the terms of the OSA Open Access Publishing Agreement

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2019 (5)

2018 (3)

A. Shah, L. Zhou, M. D. Abrámoff, and X. Wu, “Multiple surface segmentation using convolution neural nets: application to retinal layer segmentation in OCT images,” Biomed. Opt. Express 9(9), 4509–4526 (2018).
[Crossref]

T. Schlegl, S. M. Waldstein, H. Bogunovic, F. Endstraßer, A. Sadeghipour, A.-M. Philip, D. Podkowinski, B. S. Gerendas, G. Langs, and U. Schmidt-Erfurth, “Fully automated detection and quantification of macular fluid in OCT using deep learning,” Ophthalmology 125(4), 549–558 (2018).
[Crossref]

M. Ang, M. Baskaran, R. M. Werkmeister, J. Chua, D. Schmidl, V. Aranha dos Santos, G. Garhöfer, J. S. Mehta, and L. Schmetterer, “Anterior segment optical coherence tomography,” Prog. Retinal Eye Res. 66, 132–156 (2018).
[Crossref]

2017 (5)

K. F. Farrand, M. Fridman, I. Özer Stillman, and D. A. Schaumberg, “Prevalence of diagnosed dry eye disease in the United States among adults aged 18 years and older,” Am. J. Ophthalmol. 182, 90–98 (2017).
[Crossref]

J. S. Wolffsohn, R. Arita, R. Chalmers, A. Djalilian, M. Dogru, K. Dumbleton, P. K. Gupta, P. Karpecki, S. Lazreg, H. Pult, B. D. Sullivan, A. Tomlinson, L. Tong, E. Villani, K. C. Yoon, L. Jones, and J. P. Craig, “TFOS DEWS II diagnostic methodology report TFOS International Dry Eye WorkShop (DEWS II),” The Ocular Surface 15(3), 539–574 (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]

C. S. Lee, A. J. Tyring, N. P. Deruyter, Y. Wu, A. Rokem, and A. Y. Lee, “Deep-learning based, automated segmentation of macular edema in optical coherence tomography,” Biomed. Opt. Express 8(7), 3440–3448 (2017).
[Crossref]

P. Arriola-Villalobos, J. I. Fernandez-Vigo, D. Diaz-Valle, J. Almendral-Gomez, C. Fernandez-Perez, and J. M. Benitez-Del-Castillo, “Lower tear meniscus measurements using a new anterior segment swept-source optical coherence tomography and agreement with Fourier-domain optical coherence tomography,” Cornea 36(2), 183–188 (2017).
[Crossref]

2015 (2)

2014 (1)

N. Srivastava, G. Hinton, A. Krizhevsky, I. Sutskever, and R. Salakhutdinov, “Dropout: A simple way to prevent neural networks from overfitting,” J. Mach. Learn. Res. 15, 1929–1958 (2014).

2013 (2)

R. M. Werkmeister, A. Alex, S. Kaya, A. Unterhuber, B. Hofer, J. Riedl, M. Bronhagl, M. Vietauer, D. Schmidl, T. Schmoll, G. Garhöfer, W. Drexler, R. A. Leitgeb, M. Groeschl, and L. Schmetterer, “Measurement of tear film thickness using ultrahigh-resolution optical coherence tomography,” Invest. Ophthalmol. Visual Sci. 54(8), 5578–5583 (2013).
[Crossref]

R. Fukuda, T. Usui, T. Miyai, S. Yamagami, and S. Amano, “Tear meniscus evaluation by anterior segment swept-source optical coherence tomography,” Am. J. Ophthalmol. 155(4), 620–624.e2 (2013).
[Crossref]

2012 (1)

D. I. Park, H. Lew, and S. Y. Lee, “Tear meniscus measurement in nasolacrimal duct obstruction patients with fourier-domain optical coherence tomography: novel three-point capture method,” Acta Ophthalmol. 90(8), 783–787 (2012).
[Crossref]

2009 (2)

S. Zhou, Y. Li, A. T. Lu, P. Liu, M. Tang, S. C. Yiu, and D. Huang, “Reproducibility of tear meniscus measurement by Fourier-domain optical coherence tomography: a pilot study,” Ophthalmic Surg Lasers Imaging 40(5), 442–447 (2009).
[Crossref]

J. L. Gayton, “Etiology, prevalence, and treatment of dry eye disease,” Clin. Ophthalmol. 3, 405–412 (2009).
[Crossref]

2007 (1)

A. Uchida, M. Uchino, E. Goto, E. Hosaka, Y. Kasuya, K. Fukagawa, M. Dogru, Y. Ogawa, and K. Tsubota, “Noninvasive interference tear meniscometry in dry eye patients with sjögren syndrome,” Am. J. Ophthalmol. 144(2), 232–237.e1 (2007).
[Crossref]

2004 (1)

N. Yokoi, A. J. Bron, J. M. Tiffany, K. Maruyama, A. Komuro, and S. Kinoshita, “Relationship Between Tear Volume and Tear Meniscus Curvature,” JAMA Ophthalmol. 122(9), 1265–1269 (2004).
[Crossref]

2000 (1)

H. Oguz, N. Yokoi, and S. Kinoshita, “The height and radius of the tear meniscus and methods for examining these parameters,” Cornea 19(4), 497–500 (2000).
[Crossref]

1996 (1)

J. C. Mainstone, A. S. Bruce, and T. R. Golding, “Tear meniscus measurement in the diagnosis of dry eye,” Curr. Eye Res. 15(6), 653–661 (1996).
[Crossref]

Abadi, M.

M. Abadi, A. Agarwal, P. Barham, E. Brevdo, Z. Chen, C. Citro, G. S. Corrado, A. Davis, J. Dean, M. Devin, S. Ghemawat, I. Goodfellow, A. Harp, G. Irving, M. Isard, Y. Jia, R. Jozefowicz, L. Kaiser, M. Kudlur, J. Levenberg, D. Mané, R. Monga, S. Moore, D. Murray, C. Olah, M. Schuster, J. Shlens, B. Steiner, I. Sutskever, K. Talwar, P. Tucker, V. Vanhoucke, V. Vasudevan, F. Viégas, O. Vinyals, P. Warden, M. Wattenberg, M. Wicke, Y. Yu, and X. Zheng, “TensorFlow: Large-scale machine learning on heterogeneous systems,” (2015).

Abrámoff, M. D.

Agarwal, A.

M. Abadi, A. Agarwal, P. Barham, E. Brevdo, Z. Chen, C. Citro, G. S. Corrado, A. Davis, J. Dean, M. Devin, S. Ghemawat, I. Goodfellow, A. Harp, G. Irving, M. Isard, Y. Jia, R. Jozefowicz, L. Kaiser, M. Kudlur, J. Levenberg, D. Mané, R. Monga, S. Moore, D. Murray, C. Olah, M. Schuster, J. Shlens, B. Steiner, I. Sutskever, K. Talwar, P. Tucker, V. Vanhoucke, V. Vasudevan, F. Viégas, O. Vinyals, P. Warden, M. Wattenberg, M. Wicke, Y. Yu, and X. Zheng, “TensorFlow: Large-scale machine learning on heterogeneous systems,” (2015).

Aguirre, A. D.

W. Drexler, Y. Chen, A. D. Aguirre, B. Považay, A. Unterhuber, and J. G. Fujimoto, “Ultrahigh resolution optical coherence tomography,” in Optical Coherence Tomography: Technology and Applications, W. Drexler and J. G. Fujimoto, eds. (Springer International Publishing, 2015), pp. 277–318.

Ahmaddy, F.

P. F. Christ, F. Ettlinger, F. Grün, M. E. A. Elshaer, J. Lipková, S. Schlecht, F. Ahmaddy, S. Tatavarty, M. Bickel, P. Bilic, M. Rempfler, F. Hofmann, M. D’Anastasi, S. Ahmadi, G. Kaissis, J. Holch, W. H. Sommer, R. Braren, V. Heinemann, and B. H. Menze, “Automatic liver and tumor segmentation of CT and MRI volumes using cascaded fully convolutional neural networks,” CoRR abs/1702.05970 (2017).

Ahmadi, S.

P. F. Christ, F. Ettlinger, F. Grün, M. E. A. Elshaer, J. Lipková, S. Schlecht, F. Ahmaddy, S. Tatavarty, M. Bickel, P. Bilic, M. Rempfler, F. Hofmann, M. D’Anastasi, S. Ahmadi, G. Kaissis, J. Holch, W. H. Sommer, R. Braren, V. Heinemann, and B. H. Menze, “Automatic liver and tumor segmentation of CT and MRI volumes using cascaded fully convolutional neural networks,” CoRR abs/1702.05970 (2017).

Alex, A.

R. M. Werkmeister, A. Alex, S. Kaya, A. Unterhuber, B. Hofer, J. Riedl, M. Bronhagl, M. Vietauer, D. Schmidl, T. Schmoll, G. Garhöfer, W. Drexler, R. A. Leitgeb, M. Groeschl, and L. Schmetterer, “Measurement of tear film thickness using ultrahigh-resolution optical coherence tomography,” Invest. Ophthalmol. Visual Sci. 54(8), 5578–5583 (2013).
[Crossref]

Almendral-Gomez, J.

P. Arriola-Villalobos, J. I. Fernandez-Vigo, D. Diaz-Valle, J. Almendral-Gomez, C. Fernandez-Perez, and J. M. Benitez-Del-Castillo, “Lower tear meniscus measurements using a new anterior segment swept-source optical coherence tomography and agreement with Fourier-domain optical coherence tomography,” Cornea 36(2), 183–188 (2017).
[Crossref]

Amano, S.

R. Fukuda, T. Usui, T. Miyai, S. Yamagami, and S. Amano, “Tear meniscus evaluation by anterior segment swept-source optical coherence tomography,” Am. J. Ophthalmol. 155(4), 620–624.e2 (2013).
[Crossref]

Ang, M.

M. Ang, M. Baskaran, R. M. Werkmeister, J. Chua, D. Schmidl, V. Aranha dos Santos, G. Garhöfer, J. S. Mehta, and L. Schmetterer, “Anterior segment optical coherence tomography,” Prog. Retinal Eye Res. 66, 132–156 (2018).
[Crossref]

Aranha dos Santos, V.

Arita, R.

J. S. Wolffsohn, R. Arita, R. Chalmers, A. Djalilian, M. Dogru, K. Dumbleton, P. K. Gupta, P. Karpecki, S. Lazreg, H. Pult, B. D. Sullivan, A. Tomlinson, L. Tong, E. Villani, K. C. Yoon, L. Jones, and J. P. Craig, “TFOS DEWS II diagnostic methodology report TFOS International Dry Eye WorkShop (DEWS II),” The Ocular Surface 15(3), 539–574 (2017).
[Crossref]

Arriola-Villalobos, P.

P. Arriola-Villalobos, J. I. Fernandez-Vigo, D. Diaz-Valle, J. Almendral-Gomez, C. Fernandez-Perez, and J. M. Benitez-Del-Castillo, “Lower tear meniscus measurements using a new anterior segment swept-source optical coherence tomography and agreement with Fourier-domain optical coherence tomography,” Cornea 36(2), 183–188 (2017).
[Crossref]

Barham, P.

M. Abadi, A. Agarwal, P. Barham, E. Brevdo, Z. Chen, C. Citro, G. S. Corrado, A. Davis, J. Dean, M. Devin, S. Ghemawat, I. Goodfellow, A. Harp, G. Irving, M. Isard, Y. Jia, R. Jozefowicz, L. Kaiser, M. Kudlur, J. Levenberg, D. Mané, R. Monga, S. Moore, D. Murray, C. Olah, M. Schuster, J. Shlens, B. Steiner, I. Sutskever, K. Talwar, P. Tucker, V. Vanhoucke, V. Vasudevan, F. Viégas, O. Vinyals, P. Warden, M. Wattenberg, M. Wicke, Y. Yu, and X. Zheng, “TensorFlow: Large-scale machine learning on heterogeneous systems,” (2015).

Baskaran, M.

M. Ang, M. Baskaran, R. M. Werkmeister, J. Chua, D. Schmidl, V. Aranha dos Santos, G. Garhöfer, J. S. Mehta, and L. Schmetterer, “Anterior segment optical coherence tomography,” Prog. Retinal Eye Res. 66, 132–156 (2018).
[Crossref]

Benitez-Del-Castillo, J. M.

P. Arriola-Villalobos, J. I. Fernandez-Vigo, D. Diaz-Valle, J. Almendral-Gomez, C. Fernandez-Perez, and J. M. Benitez-Del-Castillo, “Lower tear meniscus measurements using a new anterior segment swept-source optical coherence tomography and agreement with Fourier-domain optical coherence tomography,” Cornea 36(2), 183–188 (2017).
[Crossref]

BenTaieb, A.

A. BenTaieb and G. Hamarneh, “Topology aware fully convolutional networks for histology gland segmentation,” in Medical Image Computing and Computer-Assisted Intervention – MICCAI 2016, S. Ourselin, L. Joskowicz, M. R. Sabuncu, G. Unal, and W. Wells, eds. (Springer International Publishing, 2016), pp. 460–468.

Bickel, M.

P. F. Christ, F. Ettlinger, F. Grün, M. E. A. Elshaer, J. Lipková, S. Schlecht, F. Ahmaddy, S. Tatavarty, M. Bickel, P. Bilic, M. Rempfler, F. Hofmann, M. D’Anastasi, S. Ahmadi, G. Kaissis, J. Holch, W. H. Sommer, R. Braren, V. Heinemann, and B. H. Menze, “Automatic liver and tumor segmentation of CT and MRI volumes using cascaded fully convolutional neural networks,” CoRR abs/1702.05970 (2017).

Bilic, P.

P. F. Christ, F. Ettlinger, F. Grün, M. E. A. Elshaer, J. Lipková, S. Schlecht, F. Ahmaddy, S. Tatavarty, M. Bickel, P. Bilic, M. Rempfler, F. Hofmann, M. D’Anastasi, S. Ahmadi, G. Kaissis, J. Holch, W. H. Sommer, R. Braren, V. Heinemann, and B. H. Menze, “Automatic liver and tumor segmentation of CT and MRI volumes using cascaded fully convolutional neural networks,” CoRR abs/1702.05970 (2017).

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T. Lin, P. Goyal, R. Girshick, K. He, and P. Dollar, “Focal loss for dense object detection,” in 2017 IEEE International Conference on Computer Vision (ICCV), (IEEE Computer Society, Los Alamitos, CA, USA, 2017), pp. 2999–3007.

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K. He, G. Gkioxari, P. Dollár, and R. Girshick, “Mask R-CNN,” (2017).

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R. M. Werkmeister, A. Alex, S. Kaya, A. Unterhuber, B. Hofer, J. Riedl, M. Bronhagl, M. Vietauer, D. Schmidl, T. Schmoll, G. Garhöfer, W. Drexler, R. A. Leitgeb, M. Groeschl, and L. Schmetterer, “Measurement of tear film thickness using ultrahigh-resolution optical coherence tomography,” Invest. Ophthalmol. Visual Sci. 54(8), 5578–5583 (2013).
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J. S. Wolffsohn, R. Arita, R. Chalmers, A. Djalilian, M. Dogru, K. Dumbleton, P. K. Gupta, P. Karpecki, S. Lazreg, H. Pult, B. D. Sullivan, A. Tomlinson, L. Tong, E. Villani, K. C. Yoon, L. Jones, and J. P. Craig, “TFOS DEWS II diagnostic methodology report TFOS International Dry Eye WorkShop (DEWS II),” The Ocular Surface 15(3), 539–574 (2017).
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Vinyals, O.

M. Abadi, A. Agarwal, P. Barham, E. Brevdo, Z. Chen, C. Citro, G. S. Corrado, A. Davis, J. Dean, M. Devin, S. Ghemawat, I. Goodfellow, A. Harp, G. Irving, M. Isard, Y. Jia, R. Jozefowicz, L. Kaiser, M. Kudlur, J. Levenberg, D. Mané, R. Monga, S. Moore, D. Murray, C. Olah, M. Schuster, J. Shlens, B. Steiner, I. Sutskever, K. Talwar, P. Tucker, V. Vanhoucke, V. Vasudevan, F. Viégas, O. Vinyals, P. Warden, M. Wattenberg, M. Wicke, Y. Yu, and X. Zheng, “TensorFlow: Large-scale machine learning on heterogeneous systems,” (2015).

Waldstein, S. M.

T. Schlegl, S. M. Waldstein, H. Bogunovic, F. Endstraßer, A. Sadeghipour, A.-M. Philip, D. Podkowinski, B. S. Gerendas, G. Langs, and U. Schmidt-Erfurth, “Fully automated detection and quantification of macular fluid in OCT using deep learning,” Ophthalmology 125(4), 549–558 (2018).
[Crossref]

Wang, C.

Warden, P.

M. Abadi, A. Agarwal, P. Barham, E. Brevdo, Z. Chen, C. Citro, G. S. Corrado, A. Davis, J. Dean, M. Devin, S. Ghemawat, I. Goodfellow, A. Harp, G. Irving, M. Isard, Y. Jia, R. Jozefowicz, L. Kaiser, M. Kudlur, J. Levenberg, D. Mané, R. Monga, S. Moore, D. Murray, C. Olah, M. Schuster, J. Shlens, B. Steiner, I. Sutskever, K. Talwar, P. Tucker, V. Vanhoucke, V. Vasudevan, F. Viégas, O. Vinyals, P. Warden, M. Wattenberg, M. Wicke, Y. Yu, and X. Zheng, “TensorFlow: Large-scale machine learning on heterogeneous systems,” (2015).

Wattenberg, M.

M. Abadi, A. Agarwal, P. Barham, E. Brevdo, Z. Chen, C. Citro, G. S. Corrado, A. Davis, J. Dean, M. Devin, S. Ghemawat, I. Goodfellow, A. Harp, G. Irving, M. Isard, Y. Jia, R. Jozefowicz, L. Kaiser, M. Kudlur, J. Levenberg, D. Mané, R. Monga, S. Moore, D. Murray, C. Olah, M. Schuster, J. Shlens, B. Steiner, I. Sutskever, K. Talwar, P. Tucker, V. Vanhoucke, V. Vasudevan, F. Viégas, O. Vinyals, P. Warden, M. Wattenberg, M. Wicke, Y. Yu, and X. Zheng, “TensorFlow: Large-scale machine learning on heterogeneous systems,” (2015).

Werkmeister, R. M.

M. Pfister, K. Schützenberger, U. Pfeiffenberger, A. Messner, Z. Chen, V. A. dos Santos, S. Puchner, G. Garhöfer, L. Schmetterer, M. Gröschl, and R. M. Werkmeister, “Automated segmentation of dermal fillers in OCT images of mice using convolutional neural networks,” Biomed. Opt. Express 10(3), 1315–1328 (2019).
[Crossref]

H. Stegmann, V. Aranha dos Santos, A. Messner, A. Unterhuber, D. Schmidl, G. Garhöfer, L. Schmetterer, and R. M. Werkmeister, “Automatic assessment of tear film and tear meniscus parameters in healthy subjects using ultrahigh-resolution optical coherence tomography,” Biomed. Opt. Express 10(6), 2744–2756 (2019).
[Crossref]

V. Aranha dos Santos, L. Schmetterer, H. Stegmann, M. Pfister, A. Messner, G. Schmidinger, G. Garhöfer, and R. M. Werkmeister, “CorneaNet: fast segmentation of cornea OCT scans of healthy and keratoconic eyes using deep learning,” Biomed. Opt. Express 10(2), 622–641 (2019).
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M. Ang, M. Baskaran, R. M. Werkmeister, J. Chua, D. Schmidl, V. Aranha dos Santos, G. Garhöfer, J. S. Mehta, and L. Schmetterer, “Anterior segment optical coherence tomography,” Prog. Retinal Eye Res. 66, 132–156 (2018).
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V. Aranha dos Santos, L. Schmetterer, M. Gröschl, G. Garhöfer, D. Schmidl, M. Kucera, A. Unterhuber, J.-P. Hermand, and R. M. Werkmeister, “In vivo tear film thickness measurement and tear film dynamics visualization using spectral domain optical coherence tomography,” Opt. Express 23(16), 21043–21063 (2015).
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M. Abadi, A. Agarwal, P. Barham, E. Brevdo, Z. Chen, C. Citro, G. S. Corrado, A. Davis, J. Dean, M. Devin, S. Ghemawat, I. Goodfellow, A. Harp, G. Irving, M. Isard, Y. Jia, R. Jozefowicz, L. Kaiser, M. Kudlur, J. Levenberg, D. Mané, R. Monga, S. Moore, D. Murray, C. Olah, M. Schuster, J. Shlens, B. Steiner, I. Sutskever, K. Talwar, P. Tucker, V. Vanhoucke, V. Vasudevan, F. Viégas, O. Vinyals, P. Warden, M. Wattenberg, M. Wicke, Y. Yu, and X. Zheng, “TensorFlow: Large-scale machine learning on heterogeneous systems,” (2015).

Wolffsohn, J. S.

J. S. Wolffsohn, R. Arita, R. Chalmers, A. Djalilian, M. Dogru, K. Dumbleton, P. K. Gupta, P. Karpecki, S. Lazreg, H. Pult, B. D. Sullivan, A. Tomlinson, L. Tong, E. Villani, K. C. Yoon, L. Jones, and J. P. Craig, “TFOS DEWS II diagnostic methodology report TFOS International Dry Eye WorkShop (DEWS II),” The Ocular Surface 15(3), 539–574 (2017).
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J. S. Wolffsohn, R. Arita, R. Chalmers, A. Djalilian, M. Dogru, K. Dumbleton, P. K. Gupta, P. Karpecki, S. Lazreg, H. Pult, B. D. Sullivan, A. Tomlinson, L. Tong, E. Villani, K. C. Yoon, L. Jones, and J. P. Craig, “TFOS DEWS II diagnostic methodology report TFOS International Dry Eye WorkShop (DEWS II),” The Ocular Surface 15(3), 539–574 (2017).
[Crossref]

Yu, Y.

M. Abadi, A. Agarwal, P. Barham, E. Brevdo, Z. Chen, C. Citro, G. S. Corrado, A. Davis, J. Dean, M. Devin, S. Ghemawat, I. Goodfellow, A. Harp, G. Irving, M. Isard, Y. Jia, R. Jozefowicz, L. Kaiser, M. Kudlur, J. Levenberg, D. Mané, R. Monga, S. Moore, D. Murray, C. Olah, M. Schuster, J. Shlens, B. Steiner, I. Sutskever, K. Talwar, P. Tucker, V. Vanhoucke, V. Vasudevan, F. Viégas, O. Vinyals, P. Warden, M. Wattenberg, M. Wicke, Y. Yu, and X. Zheng, “TensorFlow: Large-scale machine learning on heterogeneous systems,” (2015).

Zhao, Y.

H. H. Chan, Y. Zhao, T. A. Tun, and L. Tong, “Repeatability of tear meniscus evaluation using spectral-domain Cirrusreg HD-OCT and time-domain Visantereg OCT,” Contact Lens and Anterior Eye 38(5), 368–372 (2015).
[Crossref]

Zheng, X.

M. Abadi, A. Agarwal, P. Barham, E. Brevdo, Z. Chen, C. Citro, G. S. Corrado, A. Davis, J. Dean, M. Devin, S. Ghemawat, I. Goodfellow, A. Harp, G. Irving, M. Isard, Y. Jia, R. Jozefowicz, L. Kaiser, M. Kudlur, J. Levenberg, D. Mané, R. Monga, S. Moore, D. Murray, C. Olah, M. Schuster, J. Shlens, B. Steiner, I. Sutskever, K. Talwar, P. Tucker, V. Vanhoucke, V. Vasudevan, F. Viégas, O. Vinyals, P. Warden, M. Wattenberg, M. Wicke, Y. Yu, and X. Zheng, “TensorFlow: Large-scale machine learning on heterogeneous systems,” (2015).

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[Crossref]

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M. Ang, M. Baskaran, R. M. Werkmeister, J. Chua, D. Schmidl, V. Aranha dos Santos, G. Garhöfer, J. S. Mehta, and L. Schmetterer, “Anterior segment optical coherence tomography,” Prog. Retinal Eye Res. 66, 132–156 (2018).
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Figures (6)

Fig. 1.
Fig. 1. Schematic of both investigated segmentation approaches. The DSA rescales and directly segments the initial image while the LSA first localizes the meniscus before segmentation. FCN512 and FCN128 have similar architectures and only differ in their image input dimensions (see also Fig. 3).
Fig. 2.
Fig. 2. Architecture of the neural network for the localization of the tear meniscus. The notation h x w x f represents the shape of the layer (h: height, w: width, f: number of feature channels). Conv2d 3x3: 2-dimensional convolution with a 3x3 kernel, L1 reg: L1 norm regularization, batchnorm: batch normalization, ReLU: rectified linear unit, maxpool: maximum pooling, FC: fully connected layer.
Fig. 3.
Fig. 3. Architecture of the two neural networks for the segmentation of the tear meniscus. The notation h x w x f represents the shape of the layer (h: height, w: width, f: number of feature channels). The DSA uses FCN512 with n = 512 and the LSA uses FCN128 with n = 128.
Fig. 4.
Fig. 4. Comparison between the thresholding-based segmentation algorithm (TBSA), the direct segmentation approach (DSA) and the localized segmentation approach (LSA) in an example case. The axes represent µm in tear fluid.
Fig. 5.
Fig. 5. Comparison between the TBSA (column 2), DSA (3) and LSA (4) in rare cases of challenging segmentation tasks (1). None of the images were part of the training dataset. (A) Not segmented lateral cavity (orange arrow), (B) irregular meniscus shape, (C) small debris and (D) larger debris cutting the tear meniscus area in two parts. Green arrows indicate example areas where the bordering pixels are included in or excluded from the tear meniscus area. The red arrow indicates a non-segmented region of the tear meniscus area. The blue arrows indicate holes in the segmented area, where debris is present. Images in columns 1-3 are cropped from a $512 \times 512$ pixel image, while images in column 4 are cropped from a $512 \times 128$ pixel image. The axes represent µm in tear fluid.
Fig. 6.
Fig. 6. Segmentation of a lower tear meniscus image acquired with a commercially available Cirrus HD-OCT system. A) The initial image has been scaled to 512x512 pixels and was segmented with the DSA (green). The result of the tear meniscus localization is shown as a yellow bounding box. B) Segmentation of the image by the LSA (yellow). Both networks had only been trained on UHR-OCT images. The axes represent µm in tear fluid.

Tables (5)

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Table 1. Five-fold cross-validation results of the proposed network for the tear meniscus localization.

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Table 2. Five-fold cross-validation results of YOLOv3 and Tiny YOLOv3 for the tear meniscus localization.

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Table 3. Cross-validated results for the tear meniscus segmentation. Values are averages over all folds weighted by the number of images in the fold. Std is the standard deviation.

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Table 4. Performance metrics of the DSA for individual subjects. Std is the standard deviation.

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Table 5. Performance metrics of the LSA for individual subjects. Std is the standard deviation.

Equations (1)

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p i n c = N b o x N i m a g e .

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