Abstract

We report parallel-trained deep neural networks for automated endoscopic OCT image segmentation feasible even with a limited training data set. These U-Net-based deep neural networks were trained using a modified dice loss function and manual segmentations of ultrahigh-resolution cross-sectional images collected by an 800 nm OCT endoscopic system. The method was tested on in vivo guinea pig esophagus images. Results showed its robust layer segmentation capability with a boundary error of 1.4 µm insensitive to lay topology disorders. To further illustrate its clinical potential, the method was applied to differentiating in vivo OCT esophagus images from an eosinophilic esophagitis (EOE) model and its control group, and the results clearly demonstrated quantitative changes in the top esophageal layers’ thickness in the EOE model.

© 2019 Optical Society of America under the terms of the OSA Open Access Publishing Agreement

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2018 (9)

K. J. Han and Y. H. Lee, “Optical coherence tomography automated layer segmentation of macula after retinal detachment repair,” PLoS One 13(5), e0197058 (2018).
[Crossref] [PubMed]

S. K. Devalla, K. S. Chin, J.-M. Mari, T. A. Tun, N. G. Strouthidis, T. Aung, A. H. Thiéry, and M. J. A. Girard, “A deep learning approach to digitally stain optical coherence tomography images of the optic nerve head,” Invest. Ophthalmol. Vis. Sci. 59(1), 63–74 (2018).
[Crossref] [PubMed]

T. B. Dubose, D. Cunefare, E. Cole, P. Milanfar, J. A. Izatt, and S. Farsiu, “Statistical models of signal and noise and fundamental limits of segmentation accuracy in retinal optical coherence tomography,” IEEE Trans. Med. Imaging 37(9), 1978–1988 (2018).
[Crossref] [PubMed]

J. Hamwood, D. Alonso-Caneiro, S. A. Read, S. J. Vincent, and M. J. Collins, “Effect of patch size and network architecture on a convolutional neural network approach for automatic segmentation of OCT retinal layers,” Biomed. Opt. Express 9(7), 3049–3066 (2018).
[Crossref] [PubMed]

Y. Guo, A. Camino, M. Zhang, J. Wang, D. Huang, T. Hwang, and Y. Jia, “Automated segmentation of retinal layer boundaries and capillary plexuses in wide-field optical coherence tomographic angiography,” Biomed. Opt. Express 9(9), 4429–4442 (2018).
[Crossref] [PubMed]

M. Gan, C. Wang, T. Yang, N. Yang, M. Zhang, W. Yuan, X. Li, and L. Wang, “Robust layer segmentation of esophageal OCT images based on graph search using edge-enhanced weights,” Biomed. Opt. Express 9(9), 4481–4495 (2018).
[Crossref] [PubMed]

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] [PubMed]

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] [PubMed]

J. Kugelman, D. Alonso-Caneiro, S. A. Read, S. J. Vincent, and M. J. Collins, “Automatic segmentation of OCT retinal boundaries using recurrent neural networks and graph search,” Biomed. Opt. Express 9(11), 5759–5777 (2018).
[Crossref] [PubMed]

2017 (9)

A. Abdolmanafi, L. Duong, N. Dahdah, and F. Cheriet, “Deep feature learning for automatic tissue classification of coronary artery using optical coherence tomography,” Biomed. Opt. Express 8(2), 1203–1220 (2017).
[Crossref] [PubMed]

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]

M. J. Gora, M. J. Suter, G. J. Tearney, and X. Li, “Endoscopic optical coherence tomography: technologies and clinical applications [Invited],” Biomed. Opt. Express 8(5), 2405–2444 (2017).
[Crossref] [PubMed]

J. Zhang, W. Yuan, W. Liang, S. Yu, Y. Liang, Z. Xu, Y. Wei, and X. Li, “Automatic and robust segmentation of endoscopic OCT images and optical staining,” Biomed. Opt. Express 8(5), 2697–2708 (2017).
[Crossref] [PubMed]

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]

F. G. Venhuizen, B. van Ginneken, B. Liefers, M. J. J. P. van Grinsven, S. Fauser, C. Hoyng, T. Theelen, and C. I. Sánchez, “Robust total retina thickness segmentation in optical coherence tomography images using convolutional neural networks,” Biomed. Opt. Express 8(7), 3292–3316 (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 networks,” Biomed. Opt. Express 8(8), 3627–3642 (2017).
[Crossref] [PubMed]

G. Litjens, T. Kooi, B. E. Bejnordi, A. A. A. Setio, F. Ciompi, M. Ghafoorian, J. A. W. M. van der Laak, B. van Ginneken, and C. I. Sánchez, “A survey on deep learning in medical image analysis,” Med. Image Anal. 42, 60–88 (2017).
[Crossref] [PubMed]

A. Krizhevsky, I. Sutskever, and G. E. Hinton, “ImageNet Classification with Deep Convolutional Neural Networks,” Commun. ACM 60(6), 84–90 (2017).
[Crossref]

2016 (2)

H. C. Shin, H. R. Roth, M. Gao, L. Lu, Z. Xu, I. Nogues, J. Yao, D. Mollura, and R. M. Summers, “Deep convolutional neural networks for computer-aided detection: CNN architectures, dataset characteristics and transfer learning,” IEEE Trans. Med. Imaging 35(5), 1285–1298 (2016).
[Crossref] [PubMed]

G. J. Ughi, M. J. Gora, A.-F. Swager, A. Soomro, C. Grant, A. Tiernan, M. Rosenberg, J. S. Sauk, N. S. Nishioka, and G. J. Tearney, “Automated segmentation and characterization of esophageal wall in vivo by tethered capsule optical coherence tomography endomicroscopy,” Biomed. Opt. Express 7(2), 409–419 (2016).
[Crossref] [PubMed]

2015 (4)

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]

Z. Liu, Y. Hu, X. Yu, J. Xi, X. Fan, C. M. Tse, A. C. Myers, P. J. Pasricha, X. Li, and S. Yu, “Allergen challenge sensitizes TRPA1 in vagal sensory neurons and afferent C-fiber subtypes in guinea pig esophagus,” Am. J. Physiol. Gastrointest. Liver Physiol. 308(6), G482–G488 (2015).
[Crossref] [PubMed]

O. Ronneberger, P. Fischer, and T. Brox, “U-Net: Convolutional Networks for Biomedical Image Segmentation,” Lect. Notes Comput. Sci. 9351, 234–241 (2015).
[Crossref]

S. J. Chien, K. A. Silva, V. E. Kennedy, H. HogenEsch, and J. P. Sundberg, “The pathogenesis of chronic eosinophilic esophagitis in SHARPIN-deficient mice,” Exp. Mol. Pathol. 99(3), 460–467 (2015).
[Crossref] [PubMed]

2014 (1)

2012 (4)

S. Lefkimmiatis, A. Bourquard, and M. Unser, “Hessian-Based Norm Regularization for Image Restoration with Biomedical Applications,” IEEE Trans. Image Process. 21(3), 983–995 (2012).
[Crossref] [PubMed]

G. J. Ughi, T. Adriaenssens, K. Onsea, P. Kayaert, C. Dubois, P. Sinnaeve, M. Coosemans, W. Desmet, and J. D’hooge, “Automatic segmentation of in-vivo intra-coronary optical coherence tomography images to assess stent strut apposition and coverage,” Int. J. Cardiovasc. Imaging 28(2), 229–241 (2012).
[Crossref] [PubMed]

Z. Wang, D. Chamie, H. G. Bezerra, H. Yamamoto, J. Kanovsky, D. L. Wilson, M. A. Costa, and A. M. Rollins, “Volumetric quantification of fibrous caps using intravascular optical coherence tomography,” Biomed. Opt. Express 3(6), 1413–1426 (2012).
[Crossref] [PubMed]

H. Lu, M. Gargesha, Z. Wang, D. Chamie, G. F. Attizzani, T. Kanaya, S. Ray, M. A. Costa, A. M. Rollins, H. G. Bezerra, and D. L. Wilson, “Automatic stent detection in intravascular OCT images using bagged decision trees,” Biomed. Opt. Express 3(11), 2809–2824 (2012).
[Crossref] [PubMed]

2011 (2)

Q. Yang, C. A. Reisman, K. Chan, R. Ramachandran, A. Raza, and D. C. Hood, “Automated segmentation of outer retinal layers in macular OCT images of patients with retinitis pigmentosa,” Biomed. Opt. Express 2(9), 2493–2503 (2011).
[Crossref] [PubMed]

S. Tsantis, G. C. Kagadis, K. Katsanos, D. Karnabatidis, G. Bourantas, and G. C. Nikiforidis, “Automatic vessel lumen segmentation and stent strut detection in intravascular optical coherence tomography,” Med. Phys. 39(1), 503–513 (2011).
[Crossref] [PubMed]

2010 (2)

W. Hatta, K. Uno, T. Koike, S. Yokosawa, K. Iijima, A. Imatani, and T. Shimosegawa, “Optical coherence tomography for the staging of tumor infiltration in superficial esophageal squamous cell carcinoma,” Gastrointest. Endosc. 71(6), 899–906 (2010).
[Crossref] [PubMed]

S. J. Chiu, X. T. Li, P. Nicholas, C. A. Toth, J. A. Izatt, and S. Farsiu, “Automatic segmentation of seven retinal layers in SDOCT images congruent with expert manual segmentation,” Opt. Express 18(18), 19413–19428 (2010).
[Crossref] [PubMed]

2009 (2)

2008 (1)

A. M. Bagci, M. Shahidi, R. Ansari, M. Blair, N. P. Blair, and R. Zelkha, “Thickness profiles of retinal layers by optical coherence tomography image segmentation,” Am. J. Ophthalmol. 146(5), 679–687 (2008).
[Crossref] [PubMed]

2006 (1)

A. K. S. Jardine, D. M. Lin, and D. Banjevic, “A review on machinery diagnostics and prognostics implementing condition-based maintenance,” Mech. Syst. Signal Process. 20(7), 1483–1510 (2006).
[Crossref]

2005 (2)

2002 (1)

I.-K. Jang, B. E. Bouma, D.-H. Kang, S.-J. Park, S.-W. Park, K.-B. Seung, K.-B. Choi, M. Shishkov, K. Schlendorf, E. Pomerantsev, S. L. Houser, H. T. Aretz, and G. J. Tearney, “Visualization of coronary atherosclerotic plaques in patients using optical coherence tomography: comparison with intravascular ultrasound,” J. Am. Coll. Cardiol. 39(4), 604–609 (2002).
[Crossref] [PubMed]

1998 (1)

Y. LeCun, L. Bottou, G. B. Orr, and K. R. Muller, “Efficient backprop,” Neural Networks: Tricks of the Trade 1524, 9–50 (1998).

1995 (1)

C. M. Bishop, “Training with Noise Is Equivalent to Tikhonov Regularization,” Neural Comput. 7(1), 108–116 (1995).
[Crossref]

Abdolmanafi, A.

Abdulkadir, A.

Ö. Çiçek, A. Abdulkadir, S. S. Lienkamp, T. Brox, and O. Ronneberger, “3D U-Net: learning dense volumetric segmentation from sparse annotation,” in International Conference on Medical Image Computing and Computer-Assisted Intervention (Springer, 2016), 424–432.

Abrámoff, M. D.

Adler, D. C.

Adriaenssens, T.

G. J. Ughi, T. Adriaenssens, K. Onsea, P. Kayaert, C. Dubois, P. Sinnaeve, M. Coosemans, W. Desmet, and J. D’hooge, “Automatic segmentation of in-vivo intra-coronary optical coherence tomography images to assess stent strut apposition and coverage,” Int. J. Cardiovasc. Imaging 28(2), 229–241 (2012).
[Crossref] [PubMed]

Akkin, T.

Allingham, M. J.

Alonso-Caneiro, D.

Ansari, R.

A. M. Bagci, M. Shahidi, R. Ansari, M. Blair, N. P. Blair, and R. Zelkha, “Thickness profiles of retinal layers by optical coherence tomography image segmentation,” Am. J. Ophthalmol. 146(5), 679–687 (2008).
[Crossref] [PubMed]

Aretz, H. T.

I.-K. Jang, B. E. Bouma, D.-H. Kang, S.-J. Park, S.-W. Park, K.-B. Seung, K.-B. Choi, M. Shishkov, K. Schlendorf, E. Pomerantsev, S. L. Houser, H. T. Aretz, and G. J. Tearney, “Visualization of coronary atherosclerotic plaques in patients using optical coherence tomography: comparison with intravascular ultrasound,” J. Am. Coll. Cardiol. 39(4), 604–609 (2002).
[Crossref] [PubMed]

Attizzani, G. F.

Aung, T.

S. K. Devalla, K. S. Chin, J.-M. Mari, T. A. Tun, N. G. Strouthidis, T. Aung, A. H. Thiéry, and M. J. A. Girard, “A deep learning approach to digitally stain optical coherence tomography images of the optic nerve head,” Invest. Ophthalmol. Vis. Sci. 59(1), 63–74 (2018).
[Crossref] [PubMed]

Bagci, A. M.

A. M. Bagci, M. Shahidi, R. Ansari, M. Blair, N. P. Blair, and R. Zelkha, “Thickness profiles of retinal layers by optical coherence tomography image segmentation,” Am. J. Ophthalmol. 146(5), 679–687 (2008).
[Crossref] [PubMed]

Banjevic, D.

A. K. S. Jardine, D. M. Lin, and D. Banjevic, “A review on machinery diagnostics and prognostics implementing condition-based maintenance,” Mech. Syst. Signal Process. 20(7), 1483–1510 (2006).
[Crossref]

Bejnordi, B. E.

G. Litjens, T. Kooi, B. E. Bejnordi, A. A. A. Setio, F. Ciompi, M. Ghafoorian, J. A. W. M. van der Laak, B. van Ginneken, and C. I. Sánchez, “A survey on deep learning in medical image analysis,” Med. Image Anal. 42, 60–88 (2017).
[Crossref] [PubMed]

Bezerra, H. G.

Bishop, C. M.

C. M. Bishop, “Training with Noise Is Equivalent to Tikhonov Regularization,” Neural Comput. 7(1), 108–116 (1995).
[Crossref]

Bizheva, K.

Blair, M.

A. M. Bagci, M. Shahidi, R. Ansari, M. Blair, N. P. Blair, and R. Zelkha, “Thickness profiles of retinal layers by optical coherence tomography image segmentation,” Am. J. Ophthalmol. 146(5), 679–687 (2008).
[Crossref] [PubMed]

Blair, N. P.

A. M. Bagci, M. Shahidi, R. Ansari, M. Blair, N. P. Blair, and R. Zelkha, “Thickness profiles of retinal layers by optical coherence tomography image segmentation,” Am. J. Ophthalmol. 146(5), 679–687 (2008).
[Crossref] [PubMed]

Bogunovic, H.

Bottou, L.

Y. LeCun, L. Bottou, G. B. Orr, and K. R. Muller, “Efficient backprop,” Neural Networks: Tricks of the Trade 1524, 9–50 (1998).

Bouma, B. E.

I.-K. Jang, B. E. Bouma, D.-H. Kang, S.-J. Park, S.-W. Park, K.-B. Seung, K.-B. Choi, M. Shishkov, K. Schlendorf, E. Pomerantsev, S. L. Houser, H. T. Aretz, and G. J. Tearney, “Visualization of coronary atherosclerotic plaques in patients using optical coherence tomography: comparison with intravascular ultrasound,” J. Am. Coll. Cardiol. 39(4), 604–609 (2002).
[Crossref] [PubMed]

Bourantas, G.

S. Tsantis, G. C. Kagadis, K. Katsanos, D. Karnabatidis, G. Bourantas, and G. C. Nikiforidis, “Automatic vessel lumen segmentation and stent strut detection in intravascular optical coherence tomography,” Med. Phys. 39(1), 503–513 (2011).
[Crossref] [PubMed]

Bourquard, A.

S. Lefkimmiatis, A. Bourquard, and M. Unser, “Hessian-Based Norm Regularization for Image Restoration with Biomedical Applications,” IEEE Trans. Image Process. 21(3), 983–995 (2012).
[Crossref] [PubMed]

Brox, T.

O. Ronneberger, P. Fischer, and T. Brox, “U-Net: Convolutional Networks for Biomedical Image Segmentation,” Lect. Notes Comput. Sci. 9351, 234–241 (2015).
[Crossref]

Ö. Çiçek, A. Abdulkadir, S. S. Lienkamp, T. Brox, and O. Ronneberger, “3D U-Net: learning dense volumetric segmentation from sparse annotation,” in International Conference on Medical Image Computing and Computer-Assisted Intervention (Springer, 2016), 424–432.

Cabrera Fernández, D.

Camino, A.

Cense, B.

Chamie, D.

Chan, K.

Chan, R.

Chen, T.

Cheriet, F.

Chien, S. J.

S. J. Chien, K. A. Silva, V. E. Kennedy, H. HogenEsch, and J. P. Sundberg, “The pathogenesis of chronic eosinophilic esophagitis in SHARPIN-deficient mice,” Exp. Mol. Pathol. 99(3), 460–467 (2015).
[Crossref] [PubMed]

Chin, K. S.

S. K. Devalla, K. S. Chin, J.-M. Mari, T. A. Tun, N. G. Strouthidis, T. Aung, A. H. Thiéry, and M. J. A. Girard, “A deep learning approach to digitally stain optical coherence tomography images of the optic nerve head,” Invest. Ophthalmol. Vis. Sci. 59(1), 63–74 (2018).
[Crossref] [PubMed]

Chiu, S. J.

Choi, K.-B.

I.-K. Jang, B. E. Bouma, D.-H. Kang, S.-J. Park, S.-W. Park, K.-B. Seung, K.-B. Choi, M. Shishkov, K. Schlendorf, E. Pomerantsev, S. L. Houser, H. T. Aretz, and G. J. Tearney, “Visualization of coronary atherosclerotic plaques in patients using optical coherence tomography: comparison with intravascular ultrasound,” J. Am. Coll. Cardiol. 39(4), 604–609 (2002).
[Crossref] [PubMed]

Çiçek, Ö.

Ö. Çiçek, A. Abdulkadir, S. S. Lienkamp, T. Brox, and O. Ronneberger, “3D U-Net: learning dense volumetric segmentation from sparse annotation,” in International Conference on Medical Image Computing and Computer-Assisted Intervention (Springer, 2016), 424–432.

Ciompi, F.

G. Litjens, T. Kooi, B. E. Bejnordi, A. A. A. Setio, F. Ciompi, M. Ghafoorian, J. A. W. M. van der Laak, B. van Ginneken, and C. I. Sánchez, “A survey on deep learning in medical image analysis,” Med. Image Anal. 42, 60–88 (2017).
[Crossref] [PubMed]

Clausi, D. A.

Cole, E.

T. B. Dubose, D. Cunefare, E. Cole, P. Milanfar, J. A. Izatt, and S. Farsiu, “Statistical models of signal and noise and fundamental limits of segmentation accuracy in retinal optical coherence tomography,” IEEE Trans. Med. Imaging 37(9), 1978–1988 (2018).
[Crossref] [PubMed]

Collins, M. J.

Conjeti, S.

Coosemans, M.

G. J. Ughi, T. Adriaenssens, K. Onsea, P. Kayaert, C. Dubois, P. Sinnaeve, M. Coosemans, W. Desmet, and J. D’hooge, “Automatic segmentation of in-vivo intra-coronary optical coherence tomography images to assess stent strut apposition and coverage,” Int. J. Cardiovasc. Imaging 28(2), 229–241 (2012).
[Crossref] [PubMed]

Costa, M. A.

Cousins, S. W.

Cunefare, D.

T. B. Dubose, D. Cunefare, E. Cole, P. Milanfar, J. A. Izatt, and S. Farsiu, “Statistical models of signal and noise and fundamental limits of segmentation accuracy in retinal optical coherence tomography,” IEEE Trans. Med. Imaging 37(9), 1978–1988 (2018).
[Crossref] [PubMed]

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]

D’hooge, J.

G. J. Ughi, T. Adriaenssens, K. Onsea, P. Kayaert, C. Dubois, P. Sinnaeve, M. Coosemans, W. Desmet, and J. D’hooge, “Automatic segmentation of in-vivo intra-coronary optical coherence tomography images to assess stent strut apposition and coverage,” Int. J. Cardiovasc. Imaging 28(2), 229–241 (2012).
[Crossref] [PubMed]

Dahdah, N.

de Boer, J.

Desmet, W.

G. J. Ughi, T. Adriaenssens, K. Onsea, P. Kayaert, C. Dubois, P. Sinnaeve, M. Coosemans, W. Desmet, and J. D’hooge, “Automatic segmentation of in-vivo intra-coronary optical coherence tomography images to assess stent strut apposition and coverage,” Int. J. Cardiovasc. Imaging 28(2), 229–241 (2012).
[Crossref] [PubMed]

Devalla, S. K.

S. K. Devalla, K. S. Chin, J.-M. Mari, T. A. Tun, N. G. Strouthidis, T. Aung, A. H. Thiéry, and M. J. A. Girard, “A deep learning approach to digitally stain optical coherence tomography images of the optic nerve head,” Invest. Ophthalmol. Vis. Sci. 59(1), 63–74 (2018).
[Crossref] [PubMed]

Dubois, C.

G. J. Ughi, T. Adriaenssens, K. Onsea, P. Kayaert, C. Dubois, P. Sinnaeve, M. Coosemans, W. Desmet, and J. D’hooge, “Automatic segmentation of in-vivo intra-coronary optical coherence tomography images to assess stent strut apposition and coverage,” Int. J. Cardiovasc. Imaging 28(2), 229–241 (2012).
[Crossref] [PubMed]

Dubose, T. B.

T. B. Dubose, D. Cunefare, E. Cole, P. Milanfar, J. A. Izatt, and S. Farsiu, “Statistical models of signal and noise and fundamental limits of segmentation accuracy in retinal optical coherence tomography,” IEEE Trans. Med. Imaging 37(9), 1978–1988 (2018).
[Crossref] [PubMed]

Duong, L.

Fan, X.

Z. Liu, Y. Hu, X. Yu, J. Xi, X. Fan, C. M. Tse, A. C. Myers, P. J. Pasricha, X. Li, and S. Yu, “Allergen challenge sensitizes TRPA1 in vagal sensory neurons and afferent C-fiber subtypes in guinea pig esophagus,” Am. J. Physiol. Gastrointest. Liver Physiol. 308(6), G482–G488 (2015).
[Crossref] [PubMed]

Fang, L.

Farsiu, S.

Fauser, S.

Fischer, P.

O. Ronneberger, P. Fischer, and T. Brox, “U-Net: Convolutional Networks for Biomedical Image Segmentation,” Lect. Notes Comput. Sci. 9351, 234–241 (2015).
[Crossref]

Fujimoto, J. G.

Gan, M.

Gao, M.

H. C. Shin, H. R. Roth, M. Gao, L. Lu, Z. Xu, I. Nogues, J. Yao, D. Mollura, and R. M. Summers, “Deep convolutional neural networks for computer-aided detection: CNN architectures, dataset characteristics and transfer learning,” IEEE Trans. Med. Imaging 35(5), 1285–1298 (2016).
[Crossref] [PubMed]

Gargesha, M.

Gerendas, B. S.

Ghafoorian, M.

G. Litjens, T. Kooi, B. E. Bejnordi, A. A. A. Setio, F. Ciompi, M. Ghafoorian, J. A. W. M. van der Laak, B. van Ginneken, and C. I. Sánchez, “A survey on deep learning in medical image analysis,” Med. Image Anal. 42, 60–88 (2017).
[Crossref] [PubMed]

Gilhuijs, K. G.

P. Moeskops, J. M. Wolterink, B. H. van der Velden, K. G. Gilhuijs, T. Leiner, M. A. Viergever, and I. Išgum, “Deep learning for multi-task medical image segmentation in multiple modalities,” in International Conference on Medical Image Computing and Computer-Assisted Intervention, (Springer, 2016), 478–486.
[Crossref]

Girard, M. J. A.

S. K. Devalla, K. S. Chin, J.-M. Mari, T. A. Tun, N. G. Strouthidis, T. Aung, A. H. Thiéry, and M. J. A. Girard, “A deep learning approach to digitally stain optical coherence tomography images of the optic nerve head,” Invest. Ophthalmol. Vis. Sci. 59(1), 63–74 (2018).
[Crossref] [PubMed]

Gora, M. J.

Grant, C.

Guo, Y.

Guymer, R. H.

Hamwood, J.

Han, K. J.

K. J. Han and Y. H. Lee, “Optical coherence tomography automated layer segmentation of macula after retinal detachment repair,” PLoS One 13(5), e0197058 (2018).
[Crossref] [PubMed]

Hatta, W.

W. Hatta, K. Uno, T. Koike, S. Yokosawa, K. Iijima, A. Imatani, and T. Shimosegawa, “Optical coherence tomography for the staging of tumor infiltration in superficial esophageal squamous cell carcinoma,” Gastrointest. Endosc. 71(6), 899–906 (2010).
[Crossref] [PubMed]

Hinton, G. E.

A. Krizhevsky, I. Sutskever, and G. E. Hinton, “ImageNet Classification with Deep Convolutional Neural Networks,” Commun. ACM 60(6), 84–90 (2017).
[Crossref]

HogenEsch, H.

S. J. Chien, K. A. Silva, V. E. Kennedy, H. HogenEsch, and J. P. Sundberg, “The pathogenesis of chronic eosinophilic esophagitis in SHARPIN-deficient mice,” Exp. Mol. Pathol. 99(3), 460–467 (2015).
[Crossref] [PubMed]

Hood, D. C.

Houser, S. L.

I.-K. Jang, B. E. Bouma, D.-H. Kang, S.-J. Park, S.-W. Park, K.-B. Seung, K.-B. Choi, M. Shishkov, K. Schlendorf, E. Pomerantsev, S. L. Houser, H. T. Aretz, and G. J. Tearney, “Visualization of coronary atherosclerotic plaques in patients using optical coherence tomography: comparison with intravascular ultrasound,” J. Am. Coll. Cardiol. 39(4), 604–609 (2002).
[Crossref] [PubMed]

Hoyng, C.

Hu, Y.

Z. Liu, Y. Hu, X. Yu, J. Xi, X. Fan, C. M. Tse, A. C. Myers, P. J. Pasricha, X. Li, and S. Yu, “Allergen challenge sensitizes TRPA1 in vagal sensory neurons and afferent C-fiber subtypes in guinea pig esophagus,” Am. J. Physiol. Gastrointest. Liver Physiol. 308(6), G482–G488 (2015).
[Crossref] [PubMed]

Huang, D.

Huang, Q.

Hwang, T.

Iijima, K.

W. Hatta, K. Uno, T. Koike, S. Yokosawa, K. Iijima, A. Imatani, and T. Shimosegawa, “Optical coherence tomography for the staging of tumor infiltration in superficial esophageal squamous cell carcinoma,” Gastrointest. Endosc. 71(6), 899–906 (2010).
[Crossref] [PubMed]

Imatani, A.

W. Hatta, K. Uno, T. Koike, S. Yokosawa, K. Iijima, A. Imatani, and T. Shimosegawa, “Optical coherence tomography for the staging of tumor infiltration in superficial esophageal squamous cell carcinoma,” Gastrointest. Endosc. 71(6), 899–906 (2010).
[Crossref] [PubMed]

Išgum, I.

P. Moeskops, J. M. Wolterink, B. H. van der Velden, K. G. Gilhuijs, T. Leiner, M. A. Viergever, and I. Išgum, “Deep learning for multi-task medical image segmentation in multiple modalities,” in International Conference on Medical Image Computing and Computer-Assisted Intervention, (Springer, 2016), 478–486.
[Crossref]

Izatt, J. A.

Jang, I.-K.

I.-K. Jang, B. E. Bouma, D.-H. Kang, S.-J. Park, S.-W. Park, K.-B. Seung, K.-B. Choi, M. Shishkov, K. Schlendorf, E. Pomerantsev, S. L. Houser, H. T. Aretz, and G. J. Tearney, “Visualization of coronary atherosclerotic plaques in patients using optical coherence tomography: comparison with intravascular ultrasound,” J. Am. Coll. Cardiol. 39(4), 604–609 (2002).
[Crossref] [PubMed]

Jardine, A. K. S.

A. K. S. Jardine, D. M. Lin, and D. Banjevic, “A review on machinery diagnostics and prognostics implementing condition-based maintenance,” Mech. Syst. Signal Process. 20(7), 1483–1510 (2006).
[Crossref]

Jia, Y.

Joo, C.

Kagadis, G. C.

S. Tsantis, G. C. Kagadis, K. Katsanos, D. Karnabatidis, G. Bourantas, and G. C. Nikiforidis, “Automatic vessel lumen segmentation and stent strut detection in intravascular optical coherence tomography,” Med. Phys. 39(1), 503–513 (2011).
[Crossref] [PubMed]

Kanaya, T.

Kang, D.-H.

I.-K. Jang, B. E. Bouma, D.-H. Kang, S.-J. Park, S.-W. Park, K.-B. Seung, K.-B. Choi, M. Shishkov, K. Schlendorf, E. Pomerantsev, S. L. Houser, H. T. Aretz, and G. J. Tearney, “Visualization of coronary atherosclerotic plaques in patients using optical coherence tomography: comparison with intravascular ultrasound,” J. Am. Coll. Cardiol. 39(4), 604–609 (2002).
[Crossref] [PubMed]

Kanovsky, J.

Karnabatidis, D.

S. Tsantis, G. C. Kagadis, K. Katsanos, D. Karnabatidis, G. Bourantas, and G. C. Nikiforidis, “Automatic vessel lumen segmentation and stent strut detection in intravascular optical coherence tomography,” Med. Phys. 39(1), 503–513 (2011).
[Crossref] [PubMed]

Karri, S. P. K.

Katouzian, A.

Katsanos, K.

S. Tsantis, G. C. Kagadis, K. Katsanos, D. Karnabatidis, G. Bourantas, and G. C. Nikiforidis, “Automatic vessel lumen segmentation and stent strut detection in intravascular optical coherence tomography,” Med. Phys. 39(1), 503–513 (2011).
[Crossref] [PubMed]

Kayaert, P.

G. J. Ughi, T. Adriaenssens, K. Onsea, P. Kayaert, C. Dubois, P. Sinnaeve, M. Coosemans, W. Desmet, and J. D’hooge, “Automatic segmentation of in-vivo intra-coronary optical coherence tomography images to assess stent strut apposition and coverage,” Int. J. Cardiovasc. Imaging 28(2), 229–241 (2012).
[Crossref] [PubMed]

Kennedy, V. E.

S. J. Chien, K. A. Silva, V. E. Kennedy, H. HogenEsch, and J. P. Sundberg, “The pathogenesis of chronic eosinophilic esophagitis in SHARPIN-deficient mice,” Exp. Mol. Pathol. 99(3), 460–467 (2015).
[Crossref] [PubMed]

Koike, T.

W. Hatta, K. Uno, T. Koike, S. Yokosawa, K. Iijima, A. Imatani, and T. Shimosegawa, “Optical coherence tomography for the staging of tumor infiltration in superficial esophageal squamous cell carcinoma,” Gastrointest. Endosc. 71(6), 899–906 (2010).
[Crossref] [PubMed]

Kooi, T.

G. Litjens, T. Kooi, B. E. Bejnordi, A. A. A. Setio, F. Ciompi, M. Ghafoorian, J. A. W. M. van der Laak, B. van Ginneken, and C. I. Sánchez, “A survey on deep learning in medical image analysis,” Med. Image Anal. 42, 60–88 (2017).
[Crossref] [PubMed]

Krizhevsky, A.

A. Krizhevsky, I. Sutskever, and G. E. Hinton, “ImageNet Classification with Deep Convolutional Neural Networks,” Commun. ACM 60(6), 84–90 (2017).
[Crossref]

Kugelman, J.

LeCun, Y.

Y. LeCun, L. Bottou, G. B. Orr, and K. R. Muller, “Efficient backprop,” Neural Networks: Tricks of the Trade 1524, 9–50 (1998).

Lee, Y. H.

K. J. Han and Y. H. Lee, “Optical coherence tomography automated layer segmentation of macula after retinal detachment repair,” PLoS One 13(5), e0197058 (2018).
[Crossref] [PubMed]

Lefkimmiatis, S.

S. Lefkimmiatis, A. Bourquard, and M. Unser, “Hessian-Based Norm Regularization for Image Restoration with Biomedical Applications,” IEEE Trans. Image Process. 21(3), 983–995 (2012).
[Crossref] [PubMed]

Leiner, T.

P. Moeskops, J. M. Wolterink, B. H. van der Velden, K. G. Gilhuijs, T. Leiner, M. A. Viergever, and I. Išgum, “Deep learning for multi-task medical image segmentation in multiple modalities,” in International Conference on Medical Image Computing and Computer-Assisted Intervention, (Springer, 2016), 478–486.
[Crossref]

Li, S.

Li, X.

Li, X. T.

Liang, W.

Liang, Y.

Liefers, B.

Lienkamp, S. S.

Ö. Çiçek, A. Abdulkadir, S. S. Lienkamp, T. Brox, and O. Ronneberger, “3D U-Net: learning dense volumetric segmentation from sparse annotation,” in International Conference on Medical Image Computing and Computer-Assisted Intervention (Springer, 2016), 424–432.

Lin, D. M.

A. K. S. Jardine, D. M. Lin, and D. Banjevic, “A review on machinery diagnostics and prognostics implementing condition-based maintenance,” Mech. Syst. Signal Process. 20(7), 1483–1510 (2006).
[Crossref]

Lin, L. Y.

Litjens, G.

G. Litjens, T. Kooi, B. E. Bejnordi, A. A. A. Setio, F. Ciompi, M. Ghafoorian, J. A. W. M. van der Laak, B. van Ginneken, and C. I. Sánchez, “A survey on deep learning in medical image analysis,” Med. Image Anal. 42, 60–88 (2017).
[Crossref] [PubMed]

Liu, Z.

Z. Liu, Y. Hu, X. Yu, J. Xi, X. Fan, C. M. Tse, A. C. Myers, P. J. Pasricha, X. Li, and S. Yu, “Allergen challenge sensitizes TRPA1 in vagal sensory neurons and afferent C-fiber subtypes in guinea pig esophagus,” Am. J. Physiol. Gastrointest. Liver Physiol. 308(6), G482–G488 (2015).
[Crossref] [PubMed]

J. Xi, A. Zhang, Z. Liu, W. Liang, L. Y. Lin, S. Yu, and X. Li, “Diffractive catheter for ultrahigh-resolution spectral-domain volumetric OCT imaging,” Opt. Lett. 39(7), 2016–2019 (2014).
[Crossref] [PubMed]

Lu, H.

Lu, L.

H. C. Shin, H. R. Roth, M. Gao, L. Lu, Z. Xu, I. Nogues, J. Yao, D. Mollura, and R. M. Summers, “Deep convolutional neural networks for computer-aided detection: CNN architectures, dataset characteristics and transfer learning,” IEEE Trans. Med. Imaging 35(5), 1285–1298 (2016).
[Crossref] [PubMed]

Mari, J.-M.

S. K. Devalla, K. S. Chin, J.-M. Mari, T. A. Tun, N. G. Strouthidis, T. Aung, A. H. Thiéry, and M. J. A. Girard, “A deep learning approach to digitally stain optical coherence tomography images of the optic nerve head,” Invest. Ophthalmol. Vis. Sci. 59(1), 63–74 (2018).
[Crossref] [PubMed]

Mashimo, H.

Mettu, P. S.

Milanfar, P.

T. B. Dubose, D. Cunefare, E. Cole, P. Milanfar, J. A. Izatt, and S. Farsiu, “Statistical models of signal and noise and fundamental limits of segmentation accuracy in retinal optical coherence tomography,” IEEE Trans. Med. Imaging 37(9), 1978–1988 (2018).
[Crossref] [PubMed]

Mishra, A.

Moeskops, P.

P. Moeskops, J. M. Wolterink, B. H. van der Velden, K. G. Gilhuijs, T. Leiner, M. A. Viergever, and I. Išgum, “Deep learning for multi-task medical image segmentation in multiple modalities,” in International Conference on Medical Image Computing and Computer-Assisted Intervention, (Springer, 2016), 478–486.
[Crossref]

Mollura, D.

H. C. Shin, H. R. Roth, M. Gao, L. Lu, Z. Xu, I. Nogues, J. Yao, D. Mollura, and R. M. Summers, “Deep convolutional neural networks for computer-aided detection: CNN architectures, dataset characteristics and transfer learning,” IEEE Trans. Med. Imaging 35(5), 1285–1298 (2016).
[Crossref] [PubMed]

Montuoro, A.

Mujat, M.

Muller, K. R.

Y. LeCun, L. Bottou, G. B. Orr, and K. R. Muller, “Efficient backprop,” Neural Networks: Tricks of the Trade 1524, 9–50 (1998).

Myers, A. C.

Z. Liu, Y. Hu, X. Yu, J. Xi, X. Fan, C. M. Tse, A. C. Myers, P. J. Pasricha, X. Li, and S. Yu, “Allergen challenge sensitizes TRPA1 in vagal sensory neurons and afferent C-fiber subtypes in guinea pig esophagus,” Am. J. Physiol. Gastrointest. Liver Physiol. 308(6), G482–G488 (2015).
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Summers, R. M.

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S. J. Chien, K. A. Silva, V. E. Kennedy, H. HogenEsch, and J. P. Sundberg, “The pathogenesis of chronic eosinophilic esophagitis in SHARPIN-deficient mice,” Exp. Mol. Pathol. 99(3), 460–467 (2015).
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Tse, C. M.

Z. Liu, Y. Hu, X. Yu, J. Xi, X. Fan, C. M. Tse, A. C. Myers, P. J. Pasricha, X. Li, and S. Yu, “Allergen challenge sensitizes TRPA1 in vagal sensory neurons and afferent C-fiber subtypes in guinea pig esophagus,” Am. J. Physiol. Gastrointest. Liver Physiol. 308(6), G482–G488 (2015).
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G. J. Ughi, T. Adriaenssens, K. Onsea, P. Kayaert, C. Dubois, P. Sinnaeve, M. Coosemans, W. Desmet, and J. D’hooge, “Automatic segmentation of in-vivo intra-coronary optical coherence tomography images to assess stent strut apposition and coverage,” Int. J. Cardiovasc. Imaging 28(2), 229–241 (2012).
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Uno, K.

W. Hatta, K. Uno, T. Koike, S. Yokosawa, K. Iijima, A. Imatani, and T. Shimosegawa, “Optical coherence tomography for the staging of tumor infiltration in superficial esophageal squamous cell carcinoma,” Gastrointest. Endosc. 71(6), 899–906 (2010).
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S. Lefkimmiatis, A. Bourquard, and M. Unser, “Hessian-Based Norm Regularization for Image Restoration with Biomedical Applications,” IEEE Trans. Image Process. 21(3), 983–995 (2012).
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F. G. Venhuizen, B. van Ginneken, B. Liefers, M. J. J. P. van Grinsven, S. Fauser, C. Hoyng, T. Theelen, and C. I. Sánchez, “Robust total retina thickness segmentation in optical coherence tomography images using convolutional neural networks,” Biomed. Opt. Express 8(7), 3292–3316 (2017).
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H. C. Shin, H. R. Roth, M. Gao, L. Lu, Z. Xu, I. Nogues, J. Yao, D. Mollura, and R. M. Summers, “Deep convolutional neural networks for computer-aided detection: CNN architectures, dataset characteristics and transfer learning,” IEEE Trans. Med. Imaging 35(5), 1285–1298 (2016).
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W. Hatta, K. Uno, T. Koike, S. Yokosawa, K. Iijima, A. Imatani, and T. Shimosegawa, “Optical coherence tomography for the staging of tumor infiltration in superficial esophageal squamous cell carcinoma,” Gastrointest. Endosc. 71(6), 899–906 (2010).
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Z. Liu, Y. Hu, X. Yu, J. Xi, X. Fan, C. M. Tse, A. C. Myers, P. J. Pasricha, X. Li, and S. Yu, “Allergen challenge sensitizes TRPA1 in vagal sensory neurons and afferent C-fiber subtypes in guinea pig esophagus,” Am. J. Physiol. Gastrointest. Liver Physiol. 308(6), G482–G488 (2015).
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Zelkha, R.

A. M. Bagci, M. Shahidi, R. Ansari, M. Blair, N. P. Blair, and R. Zelkha, “Thickness profiles of retinal layers by optical coherence tomography image segmentation,” Am. J. Ophthalmol. 146(5), 679–687 (2008).
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Zhang, A.

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Am. J. Ophthalmol. (1)

A. M. Bagci, M. Shahidi, R. Ansari, M. Blair, N. P. Blair, and R. Zelkha, “Thickness profiles of retinal layers by optical coherence tomography image segmentation,” Am. J. Ophthalmol. 146(5), 679–687 (2008).
[Crossref] [PubMed]

Am. J. Physiol. Gastrointest. Liver Physiol. (1)

Z. Liu, Y. Hu, X. Yu, J. Xi, X. Fan, C. M. Tse, A. C. Myers, P. J. Pasricha, X. Li, and S. Yu, “Allergen challenge sensitizes TRPA1 in vagal sensory neurons and afferent C-fiber subtypes in guinea pig esophagus,” Am. J. Physiol. Gastrointest. Liver Physiol. 308(6), G482–G488 (2015).
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Biomed. Opt. Express (18)

M. Gan, C. Wang, T. Yang, N. Yang, M. Zhang, W. Yuan, X. Li, and L. Wang, “Robust layer segmentation of esophageal OCT images based on graph search using edge-enhanced weights,” Biomed. Opt. Express 9(9), 4481–4495 (2018).
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F. G. Venhuizen, B. van Ginneken, B. Liefers, M. J. J. P. van Grinsven, S. Fauser, C. Hoyng, T. Theelen, and C. I. Sánchez, “Robust total retina thickness segmentation in optical coherence tomography images using convolutional neural networks,” Biomed. Opt. Express 8(7), 3292–3316 (2017).
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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 networks,” Biomed. Opt. Express 8(8), 3627–3642 (2017).
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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).
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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).
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A. Abdolmanafi, L. Duong, N. Dahdah, and F. Cheriet, “Deep feature learning for automatic tissue classification of coronary artery using optical coherence tomography,” Biomed. Opt. Express 8(2), 1203–1220 (2017).
[Crossref] [PubMed]

J. Zhang, W. Yuan, W. Liang, S. Yu, Y. Liang, Z. Xu, Y. Wei, and X. Li, “Automatic and robust segmentation of endoscopic OCT images and optical staining,” Biomed. Opt. Express 8(5), 2697–2708 (2017).
[Crossref] [PubMed]

Q. Yang, C. A. Reisman, K. Chan, R. Ramachandran, A. Raza, and D. C. Hood, “Automated segmentation of outer retinal layers in macular OCT images of patients with retinitis pigmentosa,” Biomed. Opt. Express 2(9), 2493–2503 (2011).
[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).
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H. Lu, M. Gargesha, Z. Wang, D. Chamie, G. F. Attizzani, T. Kanaya, S. Ray, M. A. Costa, A. M. Rollins, H. G. Bezerra, and D. L. Wilson, “Automatic stent detection in intravascular OCT images using bagged decision trees,” Biomed. Opt. Express 3(11), 2809–2824 (2012).
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Z. Wang, D. Chamie, H. G. Bezerra, H. Yamamoto, J. Kanovsky, D. L. Wilson, M. A. Costa, and A. M. Rollins, “Volumetric quantification of fibrous caps using intravascular optical coherence tomography,” Biomed. Opt. Express 3(6), 1413–1426 (2012).
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G. J. Ughi, M. J. Gora, A.-F. Swager, A. Soomro, C. Grant, A. Tiernan, M. Rosenberg, J. S. Sauk, N. S. Nishioka, and G. J. Tearney, “Automated segmentation and characterization of esophageal wall in vivo by tethered capsule optical coherence tomography endomicroscopy,” Biomed. Opt. Express 7(2), 409–419 (2016).
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M. J. Gora, M. J. Suter, G. J. Tearney, and X. Li, “Endoscopic optical coherence tomography: technologies and clinical applications [Invited],” Biomed. Opt. Express 8(5), 2405–2444 (2017).
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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).
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J. Hamwood, D. Alonso-Caneiro, S. A. Read, S. J. Vincent, and M. J. Collins, “Effect of patch size and network architecture on a convolutional neural network approach for automatic segmentation of OCT retinal layers,” Biomed. Opt. Express 9(7), 3049–3066 (2018).
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J. Kugelman, D. Alonso-Caneiro, S. A. Read, S. J. Vincent, and M. J. Collins, “Automatic segmentation of OCT retinal boundaries using recurrent neural networks and graph search,” Biomed. Opt. Express 9(11), 5759–5777 (2018).
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Y. Guo, A. Camino, M. Zhang, J. Wang, D. Huang, T. Hwang, and Y. Jia, “Automated segmentation of retinal layer boundaries and capillary plexuses in wide-field optical coherence tomographic angiography,” Biomed. Opt. Express 9(9), 4429–4442 (2018).
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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).
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Commun. ACM (1)

A. Krizhevsky, I. Sutskever, and G. E. Hinton, “ImageNet Classification with Deep Convolutional Neural Networks,” Commun. ACM 60(6), 84–90 (2017).
[Crossref]

Exp. Mol. Pathol. (1)

S. J. Chien, K. A. Silva, V. E. Kennedy, H. HogenEsch, and J. P. Sundberg, “The pathogenesis of chronic eosinophilic esophagitis in SHARPIN-deficient mice,” Exp. Mol. Pathol. 99(3), 460–467 (2015).
[Crossref] [PubMed]

Gastrointest. Endosc. (1)

W. Hatta, K. Uno, T. Koike, S. Yokosawa, K. Iijima, A. Imatani, and T. Shimosegawa, “Optical coherence tomography for the staging of tumor infiltration in superficial esophageal squamous cell carcinoma,” Gastrointest. Endosc. 71(6), 899–906 (2010).
[Crossref] [PubMed]

IEEE Trans. Image Process. (1)

S. Lefkimmiatis, A. Bourquard, and M. Unser, “Hessian-Based Norm Regularization for Image Restoration with Biomedical Applications,” IEEE Trans. Image Process. 21(3), 983–995 (2012).
[Crossref] [PubMed]

IEEE Trans. Med. Imaging (2)

H. C. Shin, H. R. Roth, M. Gao, L. Lu, Z. Xu, I. Nogues, J. Yao, D. Mollura, and R. M. Summers, “Deep convolutional neural networks for computer-aided detection: CNN architectures, dataset characteristics and transfer learning,” IEEE Trans. Med. Imaging 35(5), 1285–1298 (2016).
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Figures (5)

Fig. 1
Fig. 1 (a) Representative in vivo ultrahigh-resolution circumferential OCT image of guinea pig esophagus. (b) Cut-open (rectangular) OCT image and cropped part. (c) Resized image. (d) Corresponding histology micrograph. SC: stratum corneum, EP: epithelium, LP: lamina propria, MM: muscularis mucosae, SM: submucosa, MP: muscularis propria.
Fig. 2
Fig. 2 (a) Schematic of the Net training process. (b) Schematic of a single U-net, which contains 64 kernels for preserving the image features [35]. Rectified Linear Unit (ReLU) induces nonlinearity for efficient training [36]. Max pooling reduces feature maps by a factor of 2 along each dimension [37]. Concatenation helps increase spatial resolution and training stability [38]. (c) Original OCT image with a low image contrast region (see the zoomed-in region indicated by “*”).
Fig. 3
Fig. 3 (a) Flow chart of our parallel training scheme. Three U-Nets (Net I, II, and III) were trained seperately and then combined for layer prediction. Net I was trained by the original images (OI) and the corresponding ground truth (GT); Net II was trained by the original images added with zero-mean Gaussian noise (σ = 1, GN1) and the corresponding ground truth; Net III was trained by the original images added with zero-mean Gaussian noise (σ = 2, GN2) and the corresponding ground truth. (b) Feature map predicted by the trained Net I. (c) Feature map predicted by the trained Net II. (d) Feature map predicted by the trained Net III. Dashed circles show the regions with layer topology disorders, which decrease from (b) to (d). Zoomed-in boxes show the predictions of layer boundaries, which become noisier from (b) to (d).
Fig. 4
Fig. 4 (a) Representative layer segmentation by the parallel-trained U-Nets (Net I, Net II and Net III) The empirical combination weights are [0.5, 0.3, 0.2]. (b) Layer thickness comparison between the ground truth and the prediction of the parallel-trained U-Nets. Error bars represent the standard deviation of layer thickness for all the images in the testing data set. SC: stratum corneum, EP: epithelium, LP: lamina propria, MM: muscularis mucosae, SM: submucosa. (c) Representative layer segmentation by a single U-Net trained by the original training data set. (d) Representative layer segmentation by a single U-Net trained by a noise-augmented data set which was the combination of the original training data set, the original training data set added with the first Gaussian noises (σ = 1), and the original training data set added with the second Gaussian noises (σ = 2).
Fig. 5
Fig. 5 Layer segmentation and statistics analysis of EOE esophagus model and normal control. (a) Layer segmentations with color-coded SC and EP layers. (b) EOE group layer segmentation result in detail (c) Control group layer segmentation result in detail. (d) Layer thickness comparison between the EOE model and the control group. SC: stratum corneum, EP: epithelium, LP: lamina propria, MM: muscularis mucosae, SM: submucosa, MP: muscularis propria.

Equations (1)

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J=1 2 x p l (x) g l (x) x p l 2 (x)+ x g l 2 (x) + W 2