Abstract

Automatic segmentation is important for esophageal OCT image processing, which is able to provide tissue characteristics such as shape and thickness for disease diagnosis. Existing automatical segmentation methods based on deep convolutional networks may not generate accurate segmentation results due to limited training set and various layer shapes. This study proposed a novel adversarial convolutional network (ACN) to segment esophageal OCT images using a convolutional network trained by adversarial learning. The proposed framework includes a generator and a discriminator, both with U-Net alike fully convolutional architecture. The discriminator is a hybrid network that discriminates whether the generated results are real and implements pixel classification at the same time. Leveraging on the adversarial training, the discriminator becomes more powerful. In addition, the adversarial loss is able to encode high order relationships of pixels, thus eliminating the requirements of post-processing. Experiments on segmenting esophageal OCT images from guinea pigs confirmed that the ACN outperforms several deep learning frameworks in pixel classification accuracy and improves the segmentation result. The potential clinical application of ACN for detecting eosinophilic esophagitis (EoE), an esophageal disease, is also presented in the experiment.

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

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2020 (4)

2019 (9)

J. Wang, Z. Wang, F. Li, G. X. Qu, Y. Qiao, H. R. Lv, and X. L. Zhang, “Joint retina segmentation and classification for early glaucoma diagnosis,” Biomed. Opt. Express 10(5), 2639–2656 (2019).
[Crossref]

P. A. Ganaye, M. Sdika, B. Triggs, and H. Benoit-Cattin, “Removing segmentation inconsistencies with semi-supervised non-adjacency constraint,” Med. Image Anal. 58, 101551 (2019).
[Crossref]

Y. F. He, A. Carass, Y. H. Liu, B. M. Jedynak, S. D. Solomon, S. Saidha, P. A. Calabresi, and J. L. Prince, “Deep learning based topology guaranteed surface and mme segmentation of multiple sclerosis subjects from retinal oct,” Biomed. Opt. Express 10(10), 5042–5058 (2019).
[Crossref]

X. M. Liu, J. Cao, T. Y. Fu, Z. F. Pan, W. Hu, K. Zhang, and J. Liu, “Semi-supervised automatic segmentation of layer and fluid region in retinal optical coherence tomography images using adversarial learning,” IEEE Access 7, 3046–3061 (2019).
[Crossref]

L. Y. Fang, C. Wang, S. T. Li, H. Rabbani, X. D. Chen, and Z. M. Liu, “Attention to lesion: Lesion-aware convolutional neural network for retinal optical coherence tomography image classification,” IEEE Trans. Med. Imaging 38(8), 1959–1970 (2019).
[Crossref]

Y. G. Shi, K. Cheng, and Z. W. Liu, “Hippocampal subfields segmentation in brain mr images using generative adversarial networks,” BioMed. Eng. OnLine 18(1), 5 (2019).
[Crossref]

C. Wang, M. Gan, N. Yang, T. Yang, M. Zhang, S. H. Nao, J. Zhu, H. Y. Ge, and L. R. Wang, “Fast esophageal layer segmentation in oct images of guinea pigs based on sparse bayesian classification and graph search,” Biomed. Opt. Express 10(2), 978–994 (2019).
[Crossref]

D. W. Li, J. M. Wu, Y. F. He, X. W. Yao, W. Yuan, D. F. Chen, H. C. Park, S. Y. Yu, J. L. Prince, and X. D. Li, “Parallel deep neural networks for endoscopic oct image segmentation,” Biomed. Opt. Express 10(3), 1126–1135 (2019).
[Crossref]

L. A. Thiede and U. Parlitz, “Gradient based hyperparameter optimization in echo state networks,” Neural Netw. 115, 23–29 (2019).
[Crossref]

2018 (11)

L. Y. Fang, N. J. He, S. T. Li, P. Ghamisi, and J. A. Benediktsson, “Extinction profiles fusion for hyperspectral images classification,” IEEE Trans. Geosci. Remote Sensing 56(3), 1803–1815 (2018).
[Crossref]

M. Gan, C. Wang, T. Yang, N. Yang, M. Zhang, W. Yuan, X. D. Li, and L. R. 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]

Z. Y. Han, B. Z. Wei, A. Mercado, S. Leung, and S. Li, “Spine-gan: Semantic segmentation of multiple spinal structures,” Med. Image Anal. 50, 23–35 (2018).
[Crossref]

Y. X. Li and L. L. Shen, “cc-gan: A robust transfer-learning framework for hep-2 specimen image segmentation,” IEEE Access 6, 14048–14058 (2018).
[Crossref]

S. K. Devalla, P. K. Renukanand, B. K. Sreedhar, G. Subramanian, L. Zhang, S. Perera, J. M. Mari, K. S. Chin, T. A. Tun, N. G. Strouthidis, T. Aung, A. H. Thiery, and M. J. A. Girard, “Drunet: a dilated-residual u-net deep learning network to segment optic nerve head tissues in optical coherence tomography images,” Biomed. Opt. Express 9(7), 3244–3265 (2018).
[Crossref]

F. G. Venhuizen, B. van Ginneken, B. Liefers, F. van Asten, V. Schreur, S. Fauser, C. Hoyng, T. Theelen, and C. I. Sanchez, “Deep learning approach for the detection and quantification of intraretinal cystoid fluid in multivendor optical coherence tomography,” Biomed. Opt. Express 9(4), 1545–1569 (2018).
[Crossref]

Y. Xue, T. Xu, H. Zhang, L. R. Long, and X. L. Huang, “Segan: Adversarial network with multi-scale l (1) loss for medical image segmentation,” Neuroinform. 16(3-4), 383–392 (2018).
[Crossref]

D. Nie, R. Trullo, J. Lian, L. Wang, C. Petitjean, S. Ruan, Q. Wang, and D. Shen, “Medical image synthesis with deep convolutional adversarial networks,” IEEE Trans. Biomed. Eng. 65(12), 2720–2730 (2018).
[Crossref]

F. Mahmood, R. Chen, and N. J. Durr, “Unsupervised reverse domain adaptation for synthetic medical images via adversarial training,” IEEE Trans. Med. Imaging 37(12), 2572–2581 (2018).
[Crossref]

K. Chen, D. D. Zhu, J. W. Lu, and Y. Luo, “An adversarial and densely dilated network for connectomes segmentation,” Symmetry 10(10), 467 (2018).
[Crossref]

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]

2017 (4)

2016 (2)

2015 (1)

O. Ronneberger, P. Fischer, and T. Brox, “U-net: Convolutional networks for biomedical image segmentation,” Med. Image Comput. Comput. Interv. Pt III 9351, 234–241 (2015).

2014 (3)

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

Z. Y. Liu, J. F. Xi, M. Tse, A. C. Myers, X. D. Li, P. J. Pasricha, and S. Y. Yu, “Allergic inflammation-induced structural and functional changes in esophageal epithelium in a guinea pig model of eosinophilic esophagitis,” Gastroenterology 146(5), S92 (2014).
[Crossref]

M. J. Suter, M. J. Gora, G. Y. Lauwers, T. Arnason, J. Sauk, K. A. Gallagher, L. Kava, K. M. Tan, A. R. Soomro, T. P. Gallagher, J. A. Gardecki, B. E. Bouma, M. Rosenberg, N. S. Nishioka, and G. J. Tearney, “Esophageal-guided biopsy with volumetric laser endomicroscopy and laser cautery marking: a pilot clinical study,” Gastrointest. Endosc. 79(6), 886–896 (2014).
[Crossref]

2010 (1)

2006 (2)

X. Qi, M. V. Sivak, G. Isenberg, J. E. Willis, and A. M. Rollins, “Computer-aided diagnosis of dysplasia in barrett’s esophagus using endoscopic optical coherence tomography,” J. Biomed. Opt. 11(4), 044010 (2006).
[Crossref]

P. A. Yushkevich, J. Piven, H. C. Hazlett, R. G. Smith, S. Ho, J. C. Gee, and G. Gerig, “User-guided 3d active contour segmentation of anatomical structures: Significantly improved efficiency and reliability,” NeuroImage 31(3), 1116–1128 (2006).
[Crossref]

2001 (1)

J. M. Poneros, S. Brand, B. E. Bouma, G. J. Tearney, C. C. Compton, and N. S. Nishioka, “Diagnosis of specialized intestinal metaplasia by optical coherence tomography,” Gastroenterology 120(1), 7–12 (2001).
[Crossref]

1997 (1)

G. J. Tearney, M. E. Brezinski, B. E. Bouma, S. A. Boppart, C. Pitris, J. F. Southern, and J. G. Fujimoto, “In vivo endoscopic optical biopsy with optical coherence tomography,” Science 276(5321), 2037–2039 (1997).
[Crossref]

1991 (1)

D. Huang, E. A. Swanson, C. P. Lin, J. S. Schuman, W. G. Stinson, W. Chang, M. R. Hee, T. Flotte, K. Gregory, C. A. Puliafito, and J. G. Fujimoto, “Optical coherence tomography,” Science 254(5035), 1178–1181 (1991).
[Crossref]

Adam, H.

L.-C. Chen, Y. Zhu, G. Papandreou, F. Schroff, and H. Adam, “Encoder-decoder with atrous separable convolution for semantic image segmentation,” (2018).

Allingham, M. J.

Alonso-Caneiro, D.

Arjovsky, M.

M. Arjovsky, S. Chintala, and L. Bottou, “Wasserstein gan,” http://arxiv.org/abs/1701.07875 (2017).

Arnason, T.

M. J. Suter, M. J. Gora, G. Y. Lauwers, T. Arnason, J. Sauk, K. A. Gallagher, L. Kava, K. M. Tan, A. R. Soomro, T. P. Gallagher, J. A. Gardecki, B. E. Bouma, M. Rosenberg, N. S. Nishioka, and G. J. Tearney, “Esophageal-guided biopsy with volumetric laser endomicroscopy and laser cautery marking: a pilot clinical study,” Gastrointest. Endosc. 79(6), 886–896 (2014).
[Crossref]

Aung, T.

Ba, J.

D. P. Kingma and J. Ba, “Adam: A method for stochastic optimization,” (2014).

Bab-Hadiashar, A.

R. Tennakoon, A. K. Gostar, R. Hoseinnezhad, and A. Bab-Hadiashar, “Retinal fluid segmentation in oct images using adversarial loss based convolutional neural networks,” 2018 Ieee 15th International Symposium on Biomedical Imaging (Isbi 2018) pp. 1436–1440, (2018).

Badrinarayanan, V.

V. Badrinarayanan, A. Kendall, and R. Cipolla, “Segnet: A deep convolutional encoder-decoder architecture for image segmentation,” (2015).

Bailey, S. T.

Benediktsson, J. A.

L. Y. Fang, N. J. He, S. T. Li, P. Ghamisi, and J. A. Benediktsson, “Extinction profiles fusion for hyperspectral images classification,” IEEE Trans. Geosci. Remote Sensing 56(3), 1803–1815 (2018).
[Crossref]

Bengio, Y.

I. J. Goodfellow, J. Pouget-Abadie, M. Mirza, B. Xu, D. Warde-Farley, S. Ozair, A. Courville, and Y. Bengio, “Generative adversarial nets,” in Advances in Neural Information Processing Systems 27 (Nips 2014), vol. 27 (2014).

Benoit-Cattin, H.

P. A. Ganaye, M. Sdika, B. Triggs, and H. Benoit-Cattin, “Removing segmentation inconsistencies with semi-supervised non-adjacency constraint,” Med. Image Anal. 58, 101551 (2019).
[Crossref]

Bogunovic, H.

Boppart, S. A.

G. J. Tearney, M. E. Brezinski, B. E. Bouma, S. A. Boppart, C. Pitris, J. F. Southern, and J. G. Fujimoto, “In vivo endoscopic optical biopsy with optical coherence tomography,” Science 276(5321), 2037–2039 (1997).
[Crossref]

Bottou, L.

M. Arjovsky, S. Chintala, and L. Bottou, “Wasserstein gan,” http://arxiv.org/abs/1701.07875 (2017).

Bouma, B. E.

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Figures (7)

Fig. 1.
Fig. 1. Demonstration of (a) a typical esophageal OCT image for the guinea pig and (b) the corresponding manual segmentation result.
Fig. 2.
Fig. 2. The ACN framework.
Fig. 3.
Fig. 3. Architecture of the generator.
Fig. 4.
Fig. 4. Demonstration of (a) a normal OCT B-scan sample from guinea pig esophagus; (b) manual segmentation ; (c) result of segnet; (d) result of U-Net; (e) result of Pix2Pix and (f) result of the proposed ACN.
Fig. 5.
Fig. 5. Demonstration of (a) an EoE OCT B-scan sample from guinea pig esophagus; (b) manual segmentation ; (c) result of FCN ; (d) result of U-Net; (e) result of Pix2Pix and (f) result of the proposed ACN.
Fig. 6.
Fig. 6. Bland-Altman plot of the proposed ACN framework compared to the segmentation result from Grader #1 for testing images from (a) the normal case and (b) the EoE case.
Fig. 7.
Fig. 7. Statistical results of layer thicknesses for guinea pigs of different health conditions, where Auto indicates the result of ACN and Manu represents the thickness achieved from the human grading.

Tables (3)

Tables Icon

Table 1. Information of the dataset used in this study.

Tables Icon

Table 2. Metrics of different segmentation methods on esophageal layer segmentation using the annotation from Grader #1 as ground truth.

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Table 3. DSCs of different segmentation methods for five tissue layers.

Equations (10)

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$$\begin{aligned} L_{\textrm{ACN}} = L_{\textrm{cGAN}}(G, D_1) + \lambda_1 L_{l_1}(G) + \lambda_2 L_{\textrm{class}}(G, D_2) + \lambda_3 L_{\textrm{dice}} (G, D_2) \end{aligned}$$
$$\begin{aligned} L_{\textrm{cGAN}}(G, D_1) = E_{x,y \sim p_{\textrm{data}} (x, y)} [\log D_1(x, y)] + E_{x \sim p_{\textrm{data}}(x)} [\log (1 - D_1(x, G(x)))] \end{aligned}$$
$$L_{l_1}(G) = E_{x, y, z} [\|y - G(x, z)\|_1]$$
$$\begin{aligned} L_{\textrm{class}} = E_{x, y \sim p_{\textrm{data}}(x, y)} [\log P(C = c \mid x, y)] + E_{x \sim p_{\textrm{data}(x)}} [\log P(C = c \mid x, G(x))] \end{aligned}$$
$$\begin{aligned} L_{\textrm{class}} ={-}\frac{1}{N}\sum_{i=1}^{N} g_l(f_i) \log p_l(f_i \mid x, y) - \frac{1}{N}\sum_{i=1}^{N} g_l(x_i) \log p_l(f_i \mid x, G(x)) \end{aligned}$$
$$\begin{aligned} & p_l(f \mid x, y) = D_2(x, y)\\ & p_l(f \mid x, G(x)) = D_2(x, G(x)) \end{aligned}$$
$$\begin{aligned} L_\textrm{dice} & = \left[ 1 - \frac{2 \sum_{i=1}^{N}p_l(f_i \mid x, y)g_l(f_i)}{\sum_{i=1}^{N} p_l^2(f_i \mid x, y) + \sum_{i=1}^{N} g_l^2(f_i)} \right] \\ & + \left[ 1 - \frac{2 \sum_{i=1}^{N}p_l(f_i \mid x, G(x))g_l(f_i)}{\sum_{i=1}^{N} p_l^2(f_i \mid x, G(x)) + \sum_{i=1}^{N} g_l^2(f_i)} \right] \end{aligned}$$
$$\begin{aligned} G^\star, D^\star & = \arg (\min_G\max_D (L_{cGAN}(G, D_1) + \lambda_1L_{L_1}(G)) \\ & + \min_G\min_D (\lambda_2 L_{\textrm{class}}(G, D_2) + \lambda_3 L_{\textrm{dice}}(G, D_2))) \end{aligned}$$
$$\begin{aligned} & \textrm{PWA}(A, B) = \frac{|A \cap B|}{|A|} \\ & \textrm{DSC}(A, B) = 2 \times \frac{|A \cap B|}{|A| + |B|} \end{aligned}$$
$$\begin{aligned} & \textrm{ASSD}(A, B) = \frac{1}{2} \times \left[\frac{\sum_{a \in A} \min_{b \in B} d(a, b)}{|A|} + \frac{\sum_{b \in B} \min_{a \in A} d(b, a)}{|B|} \right] \\ & \textrm{HD}(A, B) = \max \{\max_{a \in A} \min_{b\in B}d(a, b), \max_{b \in B} \min_{a \in A} d(a, b) \} \end{aligned}$$