Abstract

The increasing prevalence of myopia has attracted global attention recently. Linear lesions including lacquer cracks and myopic stretch lines are the main signs in high myopia retinas, and can be revealed by indocyanine green angiography (ICGA). Automatic linear lesion segmentation in ICGA images can help doctors diagnose and analyze high myopia quantitatively. To achieve accurate segmentation of linear lesions, an improved conditional generative adversarial network (cGAN) based method is proposed. A new partial densely connected network is adopted as the generator of cGAN to encourage the reuse of features and make the network time-saving. Dice loss and weighted binary cross-entropy loss are added to solve the data imbalance problem. Experiments on our data set indicated that the proposed network achieved better performance compared to other networks.

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

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2019 (1)

Y. Rong, D. Xiang, W. Zhu, K. Yu, F. Shi, Z. Fan, and X. Chen, “Surrogate-assisted retinal OCT image classification based on convolutional neural networks,” IEEE J. Biomed. Health Inform. 23(1), 253–263 (2019).
[Crossref] [PubMed]

2018 (3)

D. Xiang, H. Tian, X. Yang, F. Shi, W. Zhu, H. Chen, and X. Chen, “Automatic segmentation of retinal layer in OCT images with choroidal neovascularization,” IEEE Trans. Image Process. 27(12), 5880–5891 (2018).
[Crossref] [PubMed]

Y. Ma, X. Chen, W. Zhu, X. Cheng, D. Xiang, and F. Shi, “Speckle noise reduction in optical coherence tomography images based on edge-sensitive cGAN,” Biomed. Opt. Express 9(11), 5129–5146 (2018).
[Crossref] [PubMed]

K. C. Hung, M. S. Chen, C. M. Yang, S. W. Wang, and T. C. Ho, “Multimodal imaging of linear lesions in the fundus of pathologic myopic eyes with macular lesions,” Graefes Arch. Clin. Exp. Ophthalmol. 256(1), 71–81 (2018).
[Crossref] [PubMed]

2017 (3)

M. Suga, K. Shinohara, and K. Ohno-Matsui, “Lacquer cracks observed in peripheral fundus of eyes with high myopia,” Int. Med. Case Rep. J. 10, 127–130 (2017).
[Crossref] [PubMed]

V. Badrinarayanan, A. Kendall, and R. Cipolla, “Segnet: A deep convolutional encoder-decoder architecture for image segmentation,” IEEE Trans. Pattern Anal. Mach. Intell. 39(12), 2481–2495 (2017).
[Crossref] [PubMed]

Jingyun Guo, Weifang Zhu, Fei Shi, Dehui Xiang, Haoyu Chen, and Xinjian Chen, “A framework for classification and segmentation of branch retinal artery occlusion in SD-OCT,” IEEE Trans. Image Process. 26(7), 3518–3527 (2017).
[PubMed]

2016 (1)

P. Liskowski and K. Krawiec, “Segmenting retinal blood vessels with deep neural networks,” IEEE Trans. Med. Imaging 35(11), 2369–2380 (2016).
[Crossref] [PubMed]

2015 (1)

W. Ju, D. Xiang, B. Zhang, L. Wang, I. Kopriva, and X. Chen, “Random walk and graph cut for co-segmentation of lung tumor on PET-CT images,” IEEE Trans. Image Process. 24(12), 5854–5867 (2015).
[Crossref] [PubMed]

2014 (1)

K. Shinohara, M. Moriyama, N. Shimada, Y. Tanaka, and K. Ohno-Matsui, “Myopic stretch lines: linear lesions in fundus of eyes with pathologic myopia that differ from lacquer cracks,” Retina 34(3), 461–469 (2014).
[Crossref] [PubMed]

2013 (1)

N. K. Wang, C. C. Lai, C. L. Chou, Y. P. Chen, L. H. Chuang, A. N. Chao, H. J. Tseng, C. J. Chang, W. C. Wu, K. J. Chen, and S. H. Tsang, “Choroidal thickness and biometric markers for the screening of lacquer cracks in patients with high myopia,” PLoS One 8(1), e53660 (2013).
[Crossref] [PubMed]

2009 (1)

H. H. Chang, A. H. Zhuang, D. J. Valentino, and W. C. Chu, “Performance measure characterization for evaluating neuroimage segmentation algorithms,” Neuroimage 47(1), 122–135 (2009).
[Crossref] [PubMed]

2008 (1)

Y. Ikuno, K. Sayanagi, K. Soga, M. Sawa, F. Gomi, M. Tsujikawa, and Y. Tano, “Lacquer crack formation and choroidal neovascularization in pathologic myopia,” Retina 28(8), 1124–1131 (2008).
[Crossref] [PubMed]

2003 (1)

K. Ohno-Matsui, T. Yoshida, S. Futagami, K. Yasuzumi, N. Shimada, A. Kojima, T. Tokoro, and M. Mochizuki, “Patchy atrophy and lacquer cracks predispose to the development of choroidal neovascularisation in pathological myopia,” Br. J. Ophthalmol. 87(5), 570–573 (2003).
[Crossref] [PubMed]

1998 (2)

K. Ohno-Matsui, N. Morishima, M. Ito, and T. Tokoro, “Indocyanine green angiographic findings of lacquer cracks in pathologic myopia,” Jpn. J. Ophthalmol. 42(4), 293–299 (1998).
[Crossref] [PubMed]

A. Hirata and A. Negi, “Lacquer crack lesions in experimental chick myopia,” Graefes Arch. Clin. Exp. Ophthalmol. 236(2), 138–145 (1998).
[Crossref] [PubMed]

1996 (2)

K. Ohno-Matsui and T. Tokoro, “The progression of lacquer cracks in pathologic myopia,” Retina 16(1), 29–37 (1996).
[Crossref] [PubMed]

M. Quaranta, J. Arnold, G. Coscas, C. Français, G. Quentel, D. Kuhn, and G. Soubrane, “Indocyanine green angiographic features of pathologic myopia,” Am. J. Ophthalmol. 122(5), 663–671 (1996).
[Crossref] [PubMed]

Arnold, J.

M. Quaranta, J. Arnold, G. Coscas, C. Français, G. Quentel, D. Kuhn, and G. Soubrane, “Indocyanine green angiographic features of pathologic myopia,” Am. J. Ophthalmol. 122(5), 663–671 (1996).
[Crossref] [PubMed]

Badrinarayanan, V.

V. Badrinarayanan, A. Kendall, and R. Cipolla, “Segnet: A deep convolutional encoder-decoder architecture for image segmentation,” IEEE Trans. Pattern Anal. Mach. Intell. 39(12), 2481–2495 (2017).
[Crossref] [PubMed]

Brox, T.

O. Ronneberger, P. Fischer, and T. Brox, “U-net: Convolutional networks for biomedical image segmentation,” In Proceedings of International Conference on Medical Image Computing and Computer-Assisted Intervention (2015), pp. 234–241.
[Crossref]

Buty, M.

A. Wu, Z. Xu, M. Gao, M. Buty, and D. J. Mollura, “Deep vessel tracking: a generalized probabilistic approach via deep learning,” In Proceedings of International Symposium on Biomedical Imaging (2016), pp. 1363–1367.
[Crossref]

Chang, C. J.

N. K. Wang, C. C. Lai, C. L. Chou, Y. P. Chen, L. H. Chuang, A. N. Chao, H. J. Tseng, C. J. Chang, W. C. Wu, K. J. Chen, and S. H. Tsang, “Choroidal thickness and biometric markers for the screening of lacquer cracks in patients with high myopia,” PLoS One 8(1), e53660 (2013).
[Crossref] [PubMed]

Chang, H. H.

H. H. Chang, A. H. Zhuang, D. J. Valentino, and W. C. Chu, “Performance measure characterization for evaluating neuroimage segmentation algorithms,” Neuroimage 47(1), 122–135 (2009).
[Crossref] [PubMed]

Chao, A. N.

N. K. Wang, C. C. Lai, C. L. Chou, Y. P. Chen, L. H. Chuang, A. N. Chao, H. J. Tseng, C. J. Chang, W. C. Wu, K. J. Chen, and S. H. Tsang, “Choroidal thickness and biometric markers for the screening of lacquer cracks in patients with high myopia,” PLoS One 8(1), e53660 (2013).
[Crossref] [PubMed]

Chen, H.

D. Xiang, H. Tian, X. Yang, F. Shi, W. Zhu, H. Chen, and X. Chen, “Automatic segmentation of retinal layer in OCT images with choroidal neovascularization,” IEEE Trans. Image Process. 27(12), 5880–5891 (2018).
[Crossref] [PubMed]

Chen, K. J.

N. K. Wang, C. C. Lai, C. L. Chou, Y. P. Chen, L. H. Chuang, A. N. Chao, H. J. Tseng, C. J. Chang, W. C. Wu, K. J. Chen, and S. H. Tsang, “Choroidal thickness and biometric markers for the screening of lacquer cracks in patients with high myopia,” PLoS One 8(1), e53660 (2013).
[Crossref] [PubMed]

Chen, M. S.

K. C. Hung, M. S. Chen, C. M. Yang, S. W. Wang, and T. C. Ho, “Multimodal imaging of linear lesions in the fundus of pathologic myopic eyes with macular lesions,” Graefes Arch. Clin. Exp. Ophthalmol. 256(1), 71–81 (2018).
[Crossref] [PubMed]

Chen, X.

Y. Rong, D. Xiang, W. Zhu, K. Yu, F. Shi, Z. Fan, and X. Chen, “Surrogate-assisted retinal OCT image classification based on convolutional neural networks,” IEEE J. Biomed. Health Inform. 23(1), 253–263 (2019).
[Crossref] [PubMed]

D. Xiang, H. Tian, X. Yang, F. Shi, W. Zhu, H. Chen, and X. Chen, “Automatic segmentation of retinal layer in OCT images with choroidal neovascularization,” IEEE Trans. Image Process. 27(12), 5880–5891 (2018).
[Crossref] [PubMed]

Y. Ma, X. Chen, W. Zhu, X. Cheng, D. Xiang, and F. Shi, “Speckle noise reduction in optical coherence tomography images based on edge-sensitive cGAN,” Biomed. Opt. Express 9(11), 5129–5146 (2018).
[Crossref] [PubMed]

W. Ju, D. Xiang, B. Zhang, L. Wang, I. Kopriva, and X. Chen, “Random walk and graph cut for co-segmentation of lung tumor on PET-CT images,” IEEE Trans. Image Process. 24(12), 5854–5867 (2015).
[Crossref] [PubMed]

Chen, Y. P.

N. K. Wang, C. C. Lai, C. L. Chou, Y. P. Chen, L. H. Chuang, A. N. Chao, H. J. Tseng, C. J. Chang, W. C. Wu, K. J. Chen, and S. H. Tsang, “Choroidal thickness and biometric markers for the screening of lacquer cracks in patients with high myopia,” PLoS One 8(1), e53660 (2013).
[Crossref] [PubMed]

Cheng, X.

Chou, C. L.

N. K. Wang, C. C. Lai, C. L. Chou, Y. P. Chen, L. H. Chuang, A. N. Chao, H. J. Tseng, C. J. Chang, W. C. Wu, K. J. Chen, and S. H. Tsang, “Choroidal thickness and biometric markers for the screening of lacquer cracks in patients with high myopia,” PLoS One 8(1), e53660 (2013).
[Crossref] [PubMed]

Chu, W. C.

H. H. Chang, A. H. Zhuang, D. J. Valentino, and W. C. Chu, “Performance measure characterization for evaluating neuroimage segmentation algorithms,” Neuroimage 47(1), 122–135 (2009).
[Crossref] [PubMed]

Chuang, L. H.

N. K. Wang, C. C. Lai, C. L. Chou, Y. P. Chen, L. H. Chuang, A. N. Chao, H. J. Tseng, C. J. Chang, W. C. Wu, K. J. Chen, and S. H. Tsang, “Choroidal thickness and biometric markers for the screening of lacquer cracks in patients with high myopia,” PLoS One 8(1), e53660 (2013).
[Crossref] [PubMed]

Cipolla, R.

V. Badrinarayanan, A. Kendall, and R. Cipolla, “Segnet: A deep convolutional encoder-decoder architecture for image segmentation,” IEEE Trans. Pattern Anal. Mach. Intell. 39(12), 2481–2495 (2017).
[Crossref] [PubMed]

Coscas, G.

M. Quaranta, J. Arnold, G. Coscas, C. Français, G. Quentel, D. Kuhn, and G. Soubrane, “Indocyanine green angiographic features of pathologic myopia,” Am. J. Ophthalmol. 122(5), 663–671 (1996).
[Crossref] [PubMed]

Darrell, T.

J. Long, E. Shelhamer, and T. Darrell, “Fully convolutional networks for semantic segmentation,” In Proceedings of IEEE Conference on Computer Vision and Pattern Recognition (2015), pp. 3431–3440.

D. Pathak, P. Krahenbuhl, J. Donahue, T. Darrell, and A. A. Efros, “Context encoders: Feature learning by inpainting,” In Proceedings of IEEE Conference on Computer Vision and Pattern Recognition (2016), pp. 2536–2544.

Dehui Xiang,

Jingyun Guo, Weifang Zhu, Fei Shi, Dehui Xiang, Haoyu Chen, and Xinjian Chen, “A framework for classification and segmentation of branch retinal artery occlusion in SD-OCT,” IEEE Trans. Image Process. 26(7), 3518–3527 (2017).
[PubMed]

Divvala, S.

J. Redmon, S. Divvala, R. Girshick, and A. Farhadi, “You only look once: Unified, real-time object detection,” In Proceedings of IEEE Conference on Computer Vision and Pattern Recognition (2016), pp. 779–788.
[Crossref]

Donahue, J.

D. Pathak, P. Krahenbuhl, J. Donahue, T. Darrell, and A. A. Efros, “Context encoders: Feature learning by inpainting,” In Proceedings of IEEE Conference on Computer Vision and Pattern Recognition (2016), pp. 2536–2544.

Efros, A. A.

P. Isola, J. Y. Zhu, T. Zhou, and A. A. Efros, “Image-to-image translation with conditional adversarial networks,” In Proceedings of IEEE Conference on Computer Vision and Pattern Recognition (2017), pp. 5967–5976.

D. Pathak, P. Krahenbuhl, J. Donahue, T. Darrell, and A. A. Efros, “Context encoders: Feature learning by inpainting,” In Proceedings of IEEE Conference on Computer Vision and Pattern Recognition (2016), pp. 2536–2544.

J. Y. Zhu, P. Krahenbuhl, E. Shechtman, and A. A. Efros, “Generative visual manipulation on the natural image manifold,” In Proceedings of European Conference on Computer Vision (2016), pp. 597–613.
[Crossref]

Fan, Z.

Y. Rong, D. Xiang, W. Zhu, K. Yu, F. Shi, Z. Fan, and X. Chen, “Surrogate-assisted retinal OCT image classification based on convolutional neural networks,” IEEE J. Biomed. Health Inform. 23(1), 253–263 (2019).
[Crossref] [PubMed]

Farhadi, A.

J. Redmon, S. Divvala, R. Girshick, and A. Farhadi, “You only look once: Unified, real-time object detection,” In Proceedings of IEEE Conference on Computer Vision and Pattern Recognition (2016), pp. 779–788.
[Crossref]

Fei Shi,

Jingyun Guo, Weifang Zhu, Fei Shi, Dehui Xiang, Haoyu Chen, and Xinjian Chen, “A framework for classification and segmentation of branch retinal artery occlusion in SD-OCT,” IEEE Trans. Image Process. 26(7), 3518–3527 (2017).
[PubMed]

Fischer, P.

O. Ronneberger, P. Fischer, and T. Brox, “U-net: Convolutional networks for biomedical image segmentation,” In Proceedings of International Conference on Medical Image Computing and Computer-Assisted Intervention (2015), pp. 234–241.
[Crossref]

Français, C.

M. Quaranta, J. Arnold, G. Coscas, C. Français, G. Quentel, D. Kuhn, and G. Soubrane, “Indocyanine green angiographic features of pathologic myopia,” Am. J. Ophthalmol. 122(5), 663–671 (1996).
[Crossref] [PubMed]

Fu, H.

H. Fu, Y. Xu, D. W. K. Wong, and J. Liu, “Retinal vessel segmentation via deep learning network and fully-connected conditional random fields,” In Proceedings of International Symposium on Biomedical Imaging (2016), pp. 698–701.
[Crossref]

Futagami, S.

K. Ohno-Matsui, T. Yoshida, S. Futagami, K. Yasuzumi, N. Shimada, A. Kojima, T. Tokoro, and M. Mochizuki, “Patchy atrophy and lacquer cracks predispose to the development of choroidal neovascularisation in pathological myopia,” Br. J. Ophthalmol. 87(5), 570–573 (2003).
[Crossref] [PubMed]

Gao, M.

A. Wu, Z. Xu, M. Gao, M. Buty, and D. J. Mollura, “Deep vessel tracking: a generalized probabilistic approach via deep learning,” In Proceedings of International Symposium on Biomedical Imaging (2016), pp. 1363–1367.
[Crossref]

Girshick, R.

J. Redmon, S. Divvala, R. Girshick, and A. Farhadi, “You only look once: Unified, real-time object detection,” In Proceedings of IEEE Conference on Computer Vision and Pattern Recognition (2016), pp. 779–788.
[Crossref]

Gomi, F.

Y. Ikuno, K. Sayanagi, K. Soga, M. Sawa, F. Gomi, M. Tsujikawa, and Y. Tano, “Lacquer crack formation and choroidal neovascularization in pathologic myopia,” Retina 28(8), 1124–1131 (2008).
[Crossref] [PubMed]

Gupta, A.

X. Wang and A. Gupta, “Generative image modeling using style and structure adversarial networks,” In Proceedings of European Conference on Computer Vision (2016), pp. 318–335.
[Crossref]

Han, B.

H. Noh, S. Hong, and B. Han, “Learning deconvolution network for semantic segmentation,” In Proceedings of IEEE International Conference on Computer Vision (2015), pp. 1520–1528.

Haoyu Chen,

Jingyun Guo, Weifang Zhu, Fei Shi, Dehui Xiang, Haoyu Chen, and Xinjian Chen, “A framework for classification and segmentation of branch retinal artery occlusion in SD-OCT,” IEEE Trans. Image Process. 26(7), 3518–3527 (2017).
[PubMed]

He, K.

K. He, X. Zhang, S. Ren, and J. Sun, “Deep residual learning for image recognition,” In Proceedings of IEEE Conference on Computer Vision and Pattern Recognition (2016), pp. 770–778.

Hirata, A.

A. Hirata and A. Negi, “Lacquer crack lesions in experimental chick myopia,” Graefes Arch. Clin. Exp. Ophthalmol. 236(2), 138–145 (1998).
[Crossref] [PubMed]

Ho, T. C.

K. C. Hung, M. S. Chen, C. M. Yang, S. W. Wang, and T. C. Ho, “Multimodal imaging of linear lesions in the fundus of pathologic myopic eyes with macular lesions,” Graefes Arch. Clin. Exp. Ophthalmol. 256(1), 71–81 (2018).
[Crossref] [PubMed]

Hong, S.

H. Noh, S. Hong, and B. Han, “Learning deconvolution network for semantic segmentation,” In Proceedings of IEEE International Conference on Computer Vision (2015), pp. 1520–1528.

Huang, G.

G. Huang, Z. Liu, L. van der Maaten, and K. Q. Weinberger, “Densely connected convolutional networks,” In Proceedings of IEEE Conference on Computer Vision and Pattern Recognition (2017), pp. 2261–2269.

G. Huang, S. Liu, L. van der Maaten, and K. Q. Weinberger, “CondenseNet: An efficient densenet using learned group convolutions,” In Proceedings of IEEE Conference on Computer Vision and Pattern Recognition (2018), pp. 2752–2761.
[Crossref]

Hung, K. C.

K. C. Hung, M. S. Chen, C. M. Yang, S. W. Wang, and T. C. Ho, “Multimodal imaging of linear lesions in the fundus of pathologic myopic eyes with macular lesions,” Graefes Arch. Clin. Exp. Ophthalmol. 256(1), 71–81 (2018).
[Crossref] [PubMed]

Ikuno, Y.

Y. Ikuno, K. Sayanagi, K. Soga, M. Sawa, F. Gomi, M. Tsujikawa, and Y. Tano, “Lacquer crack formation and choroidal neovascularization in pathologic myopia,” Retina 28(8), 1124–1131 (2008).
[Crossref] [PubMed]

Isola, P.

P. Isola, J. Y. Zhu, T. Zhou, and A. A. Efros, “Image-to-image translation with conditional adversarial networks,” In Proceedings of IEEE Conference on Computer Vision and Pattern Recognition (2017), pp. 5967–5976.

Ito, M.

K. Ohno-Matsui, N. Morishima, M. Ito, and T. Tokoro, “Indocyanine green angiographic findings of lacquer cracks in pathologic myopia,” Jpn. J. Ophthalmol. 42(4), 293–299 (1998).
[Crossref] [PubMed]

Jia, J.

H. Zhao, J. Shi, X. Qi, X. Wang, and J. Jia, “Pyramid scene parsing network,” In Proceedings of IEEE Conference on Computer Vision and Pattern Recognition (2017), pp. 2881–2890.

Jingyun Guo,

Jingyun Guo, Weifang Zhu, Fei Shi, Dehui Xiang, Haoyu Chen, and Xinjian Chen, “A framework for classification and segmentation of branch retinal artery occlusion in SD-OCT,” IEEE Trans. Image Process. 26(7), 3518–3527 (2017).
[PubMed]

Ju, W.

W. Ju, D. Xiang, B. Zhang, L. Wang, I. Kopriva, and X. Chen, “Random walk and graph cut for co-segmentation of lung tumor on PET-CT images,” IEEE Trans. Image Process. 24(12), 5854–5867 (2015).
[Crossref] [PubMed]

Kendall, A.

V. Badrinarayanan, A. Kendall, and R. Cipolla, “Segnet: A deep convolutional encoder-decoder architecture for image segmentation,” IEEE Trans. Pattern Anal. Mach. Intell. 39(12), 2481–2495 (2017).
[Crossref] [PubMed]

Kojima, A.

K. Ohno-Matsui, T. Yoshida, S. Futagami, K. Yasuzumi, N. Shimada, A. Kojima, T. Tokoro, and M. Mochizuki, “Patchy atrophy and lacquer cracks predispose to the development of choroidal neovascularisation in pathological myopia,” Br. J. Ophthalmol. 87(5), 570–573 (2003).
[Crossref] [PubMed]

Kopriva, I.

W. Ju, D. Xiang, B. Zhang, L. Wang, I. Kopriva, and X. Chen, “Random walk and graph cut for co-segmentation of lung tumor on PET-CT images,” IEEE Trans. Image Process. 24(12), 5854–5867 (2015).
[Crossref] [PubMed]

Krahenbuhl, P.

J. Y. Zhu, P. Krahenbuhl, E. Shechtman, and A. A. Efros, “Generative visual manipulation on the natural image manifold,” In Proceedings of European Conference on Computer Vision (2016), pp. 597–613.
[Crossref]

D. Pathak, P. Krahenbuhl, J. Donahue, T. Darrell, and A. A. Efros, “Context encoders: Feature learning by inpainting,” In Proceedings of IEEE Conference on Computer Vision and Pattern Recognition (2016), pp. 2536–2544.

Krawiec, K.

P. Liskowski and K. Krawiec, “Segmenting retinal blood vessels with deep neural networks,” IEEE Trans. Med. Imaging 35(11), 2369–2380 (2016).
[Crossref] [PubMed]

Kuhn, D.

M. Quaranta, J. Arnold, G. Coscas, C. Français, G. Quentel, D. Kuhn, and G. Soubrane, “Indocyanine green angiographic features of pathologic myopia,” Am. J. Ophthalmol. 122(5), 663–671 (1996).
[Crossref] [PubMed]

Lai, C. C.

N. K. Wang, C. C. Lai, C. L. Chou, Y. P. Chen, L. H. Chuang, A. N. Chao, H. J. Tseng, C. J. Chang, W. C. Wu, K. J. Chen, and S. H. Tsang, “Choroidal thickness and biometric markers for the screening of lacquer cracks in patients with high myopia,” PLoS One 8(1), e53660 (2013).
[Crossref] [PubMed]

Li, C.

C. Li and M. Wand, “Precomputed real-time texture synthesis with markovian generative adversarial networks,” In Proceedings of European Conference on Computer Vision (2016), pp. 702–716.
[Crossref]

Liskowski, P.

P. Liskowski and K. Krawiec, “Segmenting retinal blood vessels with deep neural networks,” IEEE Trans. Med. Imaging 35(11), 2369–2380 (2016).
[Crossref] [PubMed]

Liu, J.

H. Fu, Y. Xu, D. W. K. Wong, and J. Liu, “Retinal vessel segmentation via deep learning network and fully-connected conditional random fields,” In Proceedings of International Symposium on Biomedical Imaging (2016), pp. 698–701.
[Crossref]

Liu, S.

G. Huang, S. Liu, L. van der Maaten, and K. Q. Weinberger, “CondenseNet: An efficient densenet using learned group convolutions,” In Proceedings of IEEE Conference on Computer Vision and Pattern Recognition (2018), pp. 2752–2761.
[Crossref]

Liu, Z.

G. Huang, Z. Liu, L. van der Maaten, and K. Q. Weinberger, “Densely connected convolutional networks,” In Proceedings of IEEE Conference on Computer Vision and Pattern Recognition (2017), pp. 2261–2269.

Long, J.

J. Long, E. Shelhamer, and T. Darrell, “Fully convolutional networks for semantic segmentation,” In Proceedings of IEEE Conference on Computer Vision and Pattern Recognition (2015), pp. 3431–3440.

Ma, Y.

Mochizuki, M.

K. Ohno-Matsui, T. Yoshida, S. Futagami, K. Yasuzumi, N. Shimada, A. Kojima, T. Tokoro, and M. Mochizuki, “Patchy atrophy and lacquer cracks predispose to the development of choroidal neovascularisation in pathological myopia,” Br. J. Ophthalmol. 87(5), 570–573 (2003).
[Crossref] [PubMed]

Mollura, D. J.

A. Wu, Z. Xu, M. Gao, M. Buty, and D. J. Mollura, “Deep vessel tracking: a generalized probabilistic approach via deep learning,” In Proceedings of International Symposium on Biomedical Imaging (2016), pp. 1363–1367.
[Crossref]

Morishima, N.

K. Ohno-Matsui, N. Morishima, M. Ito, and T. Tokoro, “Indocyanine green angiographic findings of lacquer cracks in pathologic myopia,” Jpn. J. Ophthalmol. 42(4), 293–299 (1998).
[Crossref] [PubMed]

Moriyama, M.

K. Shinohara, M. Moriyama, N. Shimada, Y. Tanaka, and K. Ohno-Matsui, “Myopic stretch lines: linear lesions in fundus of eyes with pathologic myopia that differ from lacquer cracks,” Retina 34(3), 461–469 (2014).
[Crossref] [PubMed]

Negi, A.

A. Hirata and A. Negi, “Lacquer crack lesions in experimental chick myopia,” Graefes Arch. Clin. Exp. Ophthalmol. 236(2), 138–145 (1998).
[Crossref] [PubMed]

Noh, H.

H. Noh, S. Hong, and B. Han, “Learning deconvolution network for semantic segmentation,” In Proceedings of IEEE International Conference on Computer Vision (2015), pp. 1520–1528.

Ohno-Matsui, K.

M. Suga, K. Shinohara, and K. Ohno-Matsui, “Lacquer cracks observed in peripheral fundus of eyes with high myopia,” Int. Med. Case Rep. J. 10, 127–130 (2017).
[Crossref] [PubMed]

K. Shinohara, M. Moriyama, N. Shimada, Y. Tanaka, and K. Ohno-Matsui, “Myopic stretch lines: linear lesions in fundus of eyes with pathologic myopia that differ from lacquer cracks,” Retina 34(3), 461–469 (2014).
[Crossref] [PubMed]

K. Ohno-Matsui, T. Yoshida, S. Futagami, K. Yasuzumi, N. Shimada, A. Kojima, T. Tokoro, and M. Mochizuki, “Patchy atrophy and lacquer cracks predispose to the development of choroidal neovascularisation in pathological myopia,” Br. J. Ophthalmol. 87(5), 570–573 (2003).
[Crossref] [PubMed]

K. Ohno-Matsui, N. Morishima, M. Ito, and T. Tokoro, “Indocyanine green angiographic findings of lacquer cracks in pathologic myopia,” Jpn. J. Ophthalmol. 42(4), 293–299 (1998).
[Crossref] [PubMed]

K. Ohno-Matsui and T. Tokoro, “The progression of lacquer cracks in pathologic myopia,” Retina 16(1), 29–37 (1996).
[Crossref] [PubMed]

Pathak, D.

D. Pathak, P. Krahenbuhl, J. Donahue, T. Darrell, and A. A. Efros, “Context encoders: Feature learning by inpainting,” In Proceedings of IEEE Conference on Computer Vision and Pattern Recognition (2016), pp. 2536–2544.

Qi, X.

H. Zhao, J. Shi, X. Qi, X. Wang, and J. Jia, “Pyramid scene parsing network,” In Proceedings of IEEE Conference on Computer Vision and Pattern Recognition (2017), pp. 2881–2890.

Quaranta, M.

M. Quaranta, J. Arnold, G. Coscas, C. Français, G. Quentel, D. Kuhn, and G. Soubrane, “Indocyanine green angiographic features of pathologic myopia,” Am. J. Ophthalmol. 122(5), 663–671 (1996).
[Crossref] [PubMed]

Quentel, G.

M. Quaranta, J. Arnold, G. Coscas, C. Français, G. Quentel, D. Kuhn, and G. Soubrane, “Indocyanine green angiographic features of pathologic myopia,” Am. J. Ophthalmol. 122(5), 663–671 (1996).
[Crossref] [PubMed]

Redmon, J.

J. Redmon, S. Divvala, R. Girshick, and A. Farhadi, “You only look once: Unified, real-time object detection,” In Proceedings of IEEE Conference on Computer Vision and Pattern Recognition (2016), pp. 779–788.
[Crossref]

Ren, S.

K. He, X. Zhang, S. Ren, and J. Sun, “Deep residual learning for image recognition,” In Proceedings of IEEE Conference on Computer Vision and Pattern Recognition (2016), pp. 770–778.

Rong, Y.

Y. Rong, D. Xiang, W. Zhu, K. Yu, F. Shi, Z. Fan, and X. Chen, “Surrogate-assisted retinal OCT image classification based on convolutional neural networks,” IEEE J. Biomed. Health Inform. 23(1), 253–263 (2019).
[Crossref] [PubMed]

Ronneberger, O.

O. Ronneberger, P. Fischer, and T. Brox, “U-net: Convolutional networks for biomedical image segmentation,” In Proceedings of International Conference on Medical Image Computing and Computer-Assisted Intervention (2015), pp. 234–241.
[Crossref]

Sawa, M.

Y. Ikuno, K. Sayanagi, K. Soga, M. Sawa, F. Gomi, M. Tsujikawa, and Y. Tano, “Lacquer crack formation and choroidal neovascularization in pathologic myopia,” Retina 28(8), 1124–1131 (2008).
[Crossref] [PubMed]

Sayanagi, K.

Y. Ikuno, K. Sayanagi, K. Soga, M. Sawa, F. Gomi, M. Tsujikawa, and Y. Tano, “Lacquer crack formation and choroidal neovascularization in pathologic myopia,” Retina 28(8), 1124–1131 (2008).
[Crossref] [PubMed]

Shechtman, E.

J. Y. Zhu, P. Krahenbuhl, E. Shechtman, and A. A. Efros, “Generative visual manipulation on the natural image manifold,” In Proceedings of European Conference on Computer Vision (2016), pp. 597–613.
[Crossref]

Shelhamer, E.

J. Long, E. Shelhamer, and T. Darrell, “Fully convolutional networks for semantic segmentation,” In Proceedings of IEEE Conference on Computer Vision and Pattern Recognition (2015), pp. 3431–3440.

Shi, F.

Y. Rong, D. Xiang, W. Zhu, K. Yu, F. Shi, Z. Fan, and X. Chen, “Surrogate-assisted retinal OCT image classification based on convolutional neural networks,” IEEE J. Biomed. Health Inform. 23(1), 253–263 (2019).
[Crossref] [PubMed]

D. Xiang, H. Tian, X. Yang, F. Shi, W. Zhu, H. Chen, and X. Chen, “Automatic segmentation of retinal layer in OCT images with choroidal neovascularization,” IEEE Trans. Image Process. 27(12), 5880–5891 (2018).
[Crossref] [PubMed]

Y. Ma, X. Chen, W. Zhu, X. Cheng, D. Xiang, and F. Shi, “Speckle noise reduction in optical coherence tomography images based on edge-sensitive cGAN,” Biomed. Opt. Express 9(11), 5129–5146 (2018).
[Crossref] [PubMed]

Shi, J.

H. Zhao, J. Shi, X. Qi, X. Wang, and J. Jia, “Pyramid scene parsing network,” In Proceedings of IEEE Conference on Computer Vision and Pattern Recognition (2017), pp. 2881–2890.

Shimada, N.

K. Shinohara, M. Moriyama, N. Shimada, Y. Tanaka, and K. Ohno-Matsui, “Myopic stretch lines: linear lesions in fundus of eyes with pathologic myopia that differ from lacquer cracks,” Retina 34(3), 461–469 (2014).
[Crossref] [PubMed]

K. Ohno-Matsui, T. Yoshida, S. Futagami, K. Yasuzumi, N. Shimada, A. Kojima, T. Tokoro, and M. Mochizuki, “Patchy atrophy and lacquer cracks predispose to the development of choroidal neovascularisation in pathological myopia,” Br. J. Ophthalmol. 87(5), 570–573 (2003).
[Crossref] [PubMed]

Shinohara, K.

M. Suga, K. Shinohara, and K. Ohno-Matsui, “Lacquer cracks observed in peripheral fundus of eyes with high myopia,” Int. Med. Case Rep. J. 10, 127–130 (2017).
[Crossref] [PubMed]

K. Shinohara, M. Moriyama, N. Shimada, Y. Tanaka, and K. Ohno-Matsui, “Myopic stretch lines: linear lesions in fundus of eyes with pathologic myopia that differ from lacquer cracks,” Retina 34(3), 461–469 (2014).
[Crossref] [PubMed]

Soga, K.

Y. Ikuno, K. Sayanagi, K. Soga, M. Sawa, F. Gomi, M. Tsujikawa, and Y. Tano, “Lacquer crack formation and choroidal neovascularization in pathologic myopia,” Retina 28(8), 1124–1131 (2008).
[Crossref] [PubMed]

Soubrane, G.

M. Quaranta, J. Arnold, G. Coscas, C. Français, G. Quentel, D. Kuhn, and G. Soubrane, “Indocyanine green angiographic features of pathologic myopia,” Am. J. Ophthalmol. 122(5), 663–671 (1996).
[Crossref] [PubMed]

Suga, M.

M. Suga, K. Shinohara, and K. Ohno-Matsui, “Lacquer cracks observed in peripheral fundus of eyes with high myopia,” Int. Med. Case Rep. J. 10, 127–130 (2017).
[Crossref] [PubMed]

Sun, J.

K. He, X. Zhang, S. Ren, and J. Sun, “Deep residual learning for image recognition,” In Proceedings of IEEE Conference on Computer Vision and Pattern Recognition (2016), pp. 770–778.

Tanaka, Y.

K. Shinohara, M. Moriyama, N. Shimada, Y. Tanaka, and K. Ohno-Matsui, “Myopic stretch lines: linear lesions in fundus of eyes with pathologic myopia that differ from lacquer cracks,” Retina 34(3), 461–469 (2014).
[Crossref] [PubMed]

Tano, Y.

Y. Ikuno, K. Sayanagi, K. Soga, M. Sawa, F. Gomi, M. Tsujikawa, and Y. Tano, “Lacquer crack formation and choroidal neovascularization in pathologic myopia,” Retina 28(8), 1124–1131 (2008).
[Crossref] [PubMed]

Tian, H.

D. Xiang, H. Tian, X. Yang, F. Shi, W. Zhu, H. Chen, and X. Chen, “Automatic segmentation of retinal layer in OCT images with choroidal neovascularization,” IEEE Trans. Image Process. 27(12), 5880–5891 (2018).
[Crossref] [PubMed]

Tokoro, T.

K. Ohno-Matsui, T. Yoshida, S. Futagami, K. Yasuzumi, N. Shimada, A. Kojima, T. Tokoro, and M. Mochizuki, “Patchy atrophy and lacquer cracks predispose to the development of choroidal neovascularisation in pathological myopia,” Br. J. Ophthalmol. 87(5), 570–573 (2003).
[Crossref] [PubMed]

K. Ohno-Matsui, N. Morishima, M. Ito, and T. Tokoro, “Indocyanine green angiographic findings of lacquer cracks in pathologic myopia,” Jpn. J. Ophthalmol. 42(4), 293–299 (1998).
[Crossref] [PubMed]

K. Ohno-Matsui and T. Tokoro, “The progression of lacquer cracks in pathologic myopia,” Retina 16(1), 29–37 (1996).
[Crossref] [PubMed]

Tsang, S. H.

N. K. Wang, C. C. Lai, C. L. Chou, Y. P. Chen, L. H. Chuang, A. N. Chao, H. J. Tseng, C. J. Chang, W. C. Wu, K. J. Chen, and S. H. Tsang, “Choroidal thickness and biometric markers for the screening of lacquer cracks in patients with high myopia,” PLoS One 8(1), e53660 (2013).
[Crossref] [PubMed]

Tseng, H. J.

N. K. Wang, C. C. Lai, C. L. Chou, Y. P. Chen, L. H. Chuang, A. N. Chao, H. J. Tseng, C. J. Chang, W. C. Wu, K. J. Chen, and S. H. Tsang, “Choroidal thickness and biometric markers for the screening of lacquer cracks in patients with high myopia,” PLoS One 8(1), e53660 (2013).
[Crossref] [PubMed]

Tsujikawa, M.

Y. Ikuno, K. Sayanagi, K. Soga, M. Sawa, F. Gomi, M. Tsujikawa, and Y. Tano, “Lacquer crack formation and choroidal neovascularization in pathologic myopia,” Retina 28(8), 1124–1131 (2008).
[Crossref] [PubMed]

Valentino, D. J.

H. H. Chang, A. H. Zhuang, D. J. Valentino, and W. C. Chu, “Performance measure characterization for evaluating neuroimage segmentation algorithms,” Neuroimage 47(1), 122–135 (2009).
[Crossref] [PubMed]

van der Maaten, L.

G. Huang, S. Liu, L. van der Maaten, and K. Q. Weinberger, “CondenseNet: An efficient densenet using learned group convolutions,” In Proceedings of IEEE Conference on Computer Vision and Pattern Recognition (2018), pp. 2752–2761.
[Crossref]

G. Huang, Z. Liu, L. van der Maaten, and K. Q. Weinberger, “Densely connected convolutional networks,” In Proceedings of IEEE Conference on Computer Vision and Pattern Recognition (2017), pp. 2261–2269.

Wand, M.

C. Li and M. Wand, “Precomputed real-time texture synthesis with markovian generative adversarial networks,” In Proceedings of European Conference on Computer Vision (2016), pp. 702–716.
[Crossref]

Wang, L.

W. Ju, D. Xiang, B. Zhang, L. Wang, I. Kopriva, and X. Chen, “Random walk and graph cut for co-segmentation of lung tumor on PET-CT images,” IEEE Trans. Image Process. 24(12), 5854–5867 (2015).
[Crossref] [PubMed]

Wang, N. K.

N. K. Wang, C. C. Lai, C. L. Chou, Y. P. Chen, L. H. Chuang, A. N. Chao, H. J. Tseng, C. J. Chang, W. C. Wu, K. J. Chen, and S. H. Tsang, “Choroidal thickness and biometric markers for the screening of lacquer cracks in patients with high myopia,” PLoS One 8(1), e53660 (2013).
[Crossref] [PubMed]

Wang, S. W.

K. C. Hung, M. S. Chen, C. M. Yang, S. W. Wang, and T. C. Ho, “Multimodal imaging of linear lesions in the fundus of pathologic myopic eyes with macular lesions,” Graefes Arch. Clin. Exp. Ophthalmol. 256(1), 71–81 (2018).
[Crossref] [PubMed]

Wang, X.

X. Wang and A. Gupta, “Generative image modeling using style and structure adversarial networks,” In Proceedings of European Conference on Computer Vision (2016), pp. 318–335.
[Crossref]

H. Zhao, J. Shi, X. Qi, X. Wang, and J. Jia, “Pyramid scene parsing network,” In Proceedings of IEEE Conference on Computer Vision and Pattern Recognition (2017), pp. 2881–2890.

Weifang Zhu,

Jingyun Guo, Weifang Zhu, Fei Shi, Dehui Xiang, Haoyu Chen, and Xinjian Chen, “A framework for classification and segmentation of branch retinal artery occlusion in SD-OCT,” IEEE Trans. Image Process. 26(7), 3518–3527 (2017).
[PubMed]

Weinberger, K. Q.

G. Huang, Z. Liu, L. van der Maaten, and K. Q. Weinberger, “Densely connected convolutional networks,” In Proceedings of IEEE Conference on Computer Vision and Pattern Recognition (2017), pp. 2261–2269.

G. Huang, S. Liu, L. van der Maaten, and K. Q. Weinberger, “CondenseNet: An efficient densenet using learned group convolutions,” In Proceedings of IEEE Conference on Computer Vision and Pattern Recognition (2018), pp. 2752–2761.
[Crossref]

Wong, D. W. K.

H. Fu, Y. Xu, D. W. K. Wong, and J. Liu, “Retinal vessel segmentation via deep learning network and fully-connected conditional random fields,” In Proceedings of International Symposium on Biomedical Imaging (2016), pp. 698–701.
[Crossref]

Wu, A.

A. Wu, Z. Xu, M. Gao, M. Buty, and D. J. Mollura, “Deep vessel tracking: a generalized probabilistic approach via deep learning,” In Proceedings of International Symposium on Biomedical Imaging (2016), pp. 1363–1367.
[Crossref]

Wu, W. C.

N. K. Wang, C. C. Lai, C. L. Chou, Y. P. Chen, L. H. Chuang, A. N. Chao, H. J. Tseng, C. J. Chang, W. C. Wu, K. J. Chen, and S. H. Tsang, “Choroidal thickness and biometric markers for the screening of lacquer cracks in patients with high myopia,” PLoS One 8(1), e53660 (2013).
[Crossref] [PubMed]

Xiang, D.

Y. Rong, D. Xiang, W. Zhu, K. Yu, F. Shi, Z. Fan, and X. Chen, “Surrogate-assisted retinal OCT image classification based on convolutional neural networks,” IEEE J. Biomed. Health Inform. 23(1), 253–263 (2019).
[Crossref] [PubMed]

D. Xiang, H. Tian, X. Yang, F. Shi, W. Zhu, H. Chen, and X. Chen, “Automatic segmentation of retinal layer in OCT images with choroidal neovascularization,” IEEE Trans. Image Process. 27(12), 5880–5891 (2018).
[Crossref] [PubMed]

Y. Ma, X. Chen, W. Zhu, X. Cheng, D. Xiang, and F. Shi, “Speckle noise reduction in optical coherence tomography images based on edge-sensitive cGAN,” Biomed. Opt. Express 9(11), 5129–5146 (2018).
[Crossref] [PubMed]

W. Ju, D. Xiang, B. Zhang, L. Wang, I. Kopriva, and X. Chen, “Random walk and graph cut for co-segmentation of lung tumor on PET-CT images,” IEEE Trans. Image Process. 24(12), 5854–5867 (2015).
[Crossref] [PubMed]

Xinjian Chen,

Jingyun Guo, Weifang Zhu, Fei Shi, Dehui Xiang, Haoyu Chen, and Xinjian Chen, “A framework for classification and segmentation of branch retinal artery occlusion in SD-OCT,” IEEE Trans. Image Process. 26(7), 3518–3527 (2017).
[PubMed]

Xu, Y.

H. Fu, Y. Xu, D. W. K. Wong, and J. Liu, “Retinal vessel segmentation via deep learning network and fully-connected conditional random fields,” In Proceedings of International Symposium on Biomedical Imaging (2016), pp. 698–701.
[Crossref]

Xu, Z.

A. Wu, Z. Xu, M. Gao, M. Buty, and D. J. Mollura, “Deep vessel tracking: a generalized probabilistic approach via deep learning,” In Proceedings of International Symposium on Biomedical Imaging (2016), pp. 1363–1367.
[Crossref]

Yang, C. M.

K. C. Hung, M. S. Chen, C. M. Yang, S. W. Wang, and T. C. Ho, “Multimodal imaging of linear lesions in the fundus of pathologic myopic eyes with macular lesions,” Graefes Arch. Clin. Exp. Ophthalmol. 256(1), 71–81 (2018).
[Crossref] [PubMed]

Yang, X.

D. Xiang, H. Tian, X. Yang, F. Shi, W. Zhu, H. Chen, and X. Chen, “Automatic segmentation of retinal layer in OCT images with choroidal neovascularization,” IEEE Trans. Image Process. 27(12), 5880–5891 (2018).
[Crossref] [PubMed]

Yasuzumi, K.

K. Ohno-Matsui, T. Yoshida, S. Futagami, K. Yasuzumi, N. Shimada, A. Kojima, T. Tokoro, and M. Mochizuki, “Patchy atrophy and lacquer cracks predispose to the development of choroidal neovascularisation in pathological myopia,” Br. J. Ophthalmol. 87(5), 570–573 (2003).
[Crossref] [PubMed]

Yoshida, T.

K. Ohno-Matsui, T. Yoshida, S. Futagami, K. Yasuzumi, N. Shimada, A. Kojima, T. Tokoro, and M. Mochizuki, “Patchy atrophy and lacquer cracks predispose to the development of choroidal neovascularisation in pathological myopia,” Br. J. Ophthalmol. 87(5), 570–573 (2003).
[Crossref] [PubMed]

Yu, K.

Y. Rong, D. Xiang, W. Zhu, K. Yu, F. Shi, Z. Fan, and X. Chen, “Surrogate-assisted retinal OCT image classification based on convolutional neural networks,” IEEE J. Biomed. Health Inform. 23(1), 253–263 (2019).
[Crossref] [PubMed]

Zhang, B.

W. Ju, D. Xiang, B. Zhang, L. Wang, I. Kopriva, and X. Chen, “Random walk and graph cut for co-segmentation of lung tumor on PET-CT images,” IEEE Trans. Image Process. 24(12), 5854–5867 (2015).
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Zhang, X.

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Figures (8)

Fig. 1
Fig. 1 Linear lesions in ICGA.
Fig. 2
Fig. 2 Flowchart of the proposed method.
Fig. 3
Fig. 3 Example of the proposed partial dense connections.
Fig. 4
Fig. 4 Architecture of generator.
Fig. 5
Fig. 5 Architecture of discriminator.
Fig. 6
Fig. 6 Example of ICGA image data set. (a) An original ICGA image. (b) The annotation of the ICGA image (a). Red regions present the background. Green regions indicate linear lesions and blue regions indicate retinal vessels.
Fig. 7
Fig. 7 Segmentation results by model variations. Green regions present the segmentation results and blue regions present the ground truth. Red regions indicate the intersection between the ground truth and segmentation results. (a) Results of cGAN. (b) Results of cGAN + Dice. (c) Results of cGAN + wBCE. (d) Results of cGAN + Dice + wBCE. (e) Results of cGAN + partial dense connections. (f) Results of the proposed networks. (Dice: Dice loss; wBCE: weighted binary cross-entropy loss)
Fig. 8
Fig. 8 Segmentation results on other deep networks. Green regions present the segmentation results and blue regions present the ground truth. Red regions indicate the intersection between the ground truth and segmentation results. (a) Results of U-Net. (b) Results of PSPNet. (c) Results of TiramisuNet. (d) Results of U-Net + partial dense connections. (e) Results of the proposed method.

Tables (3)

Tables Icon

Table 1 Evaluation metrics adopted in the experiments

Tables Icon

Table 2 Segmentation results of comparison experiments on model variations, measured with mean and standard deviation.

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Table 3 Segmentation results of comparison experiments on other deep networks, measured with mean and standard deviation.

Equations (7)

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L c G A N ( G , D ) = E x , y p d a t a ( x , y ) [ log D ( x , y ) ] + E x p d a t a ( x ) , z p z ( z ) [ log ( 1 D ( x , G ( x , z ) ) ) ]
G = arg min G max D L c G A N ( G , D )
x i = H i ( [ x i 1 , x i 2 ] )
L L 1 ( G ) = E x , y p d a t a ( x , y ) , z p z ( z ) [ y G ( x , z ) 1 ]
L D i c e ( G ) = i = 0 2 w i · E x , y p d a t a ( x , y ) , z p z ( z ) [ 1 2 y i G ( x , z ) i y i 2 + G ( x , z ) i 2 ]
L w B C E ( G ) = i = 0 2 E x , y p d a t a ( x , y ) , z p z ( z ) [ w i + ( y i log G ( x , z ) i ) w i ( y i log ( 1 G ( x , z ) i ) ) ]
L o s s = arg min G max D L c G A N ( G , D ) + λ 1 L L 1 ( G ) + λ 2 L D i c e ( G ) + λ 3 L w B C E ( G )

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