Abstract

We report an end-to-end image compression framework for retina optical coherence tomography (OCT) images based on convolutional neural networks (CNNs), which achieved an image size compression ratio as high as 80. Our compression scheme consists of three parts: data preprocessing, compression CNNs, and reconstruction CNNs. The preprocessing module was designed to reduce OCT speckle noise and segment out the region of interest. Skip connections with quantization were developed and added between the compression CNNs and the reconstruction CNNs to reserve the fine-structure information. Two networks were trained together by taking the semantic segmented images from the preprocessing module as input. To train the two networks sensitive to both low and high frequency information, we leveraged an objective function with two components: an adversarial discriminator to judge the high frequency information and a differentiable multi-scale structural similarity (MS-SSIM) penalty to evaluate the low frequency information. The proposed framework was trained and evaluated on ophthalmic OCT images with pathological information. The evaluation showed reconstructed images can still achieve above 99% similarity in terms of MS-SSIM when the compression ratio reached 40. Furthermore, the reconstructed images after 80-fold compression with the proposed framework even presented comparable quality with those of a compression ratio 20 from state-of-the-art methods. The test results showed that the proposed framework outperformed other methods in terms of both MS-SSIM and visualization, which was more obvious at higher compression ratios. Compression and reconstruction were fast and took only about 0.015 seconds per image. The results suggested a promising potential of deep neural networks on customized medical image compression, particularly valuable for effective image storage and tele-transfer.

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

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2019 (1)

2018 (2)

R. Rasti, H. Rabbani, A. Mehridehnavi, and F. Hajizadeh, “Macular OCT classification using a multi-scale convolutional neural network ensemble,” IEEE Trans. Med. Imaging 37(4), 1024–1034 (2018).
[Crossref]

Y. Han and J. C. Ye, “Framing U-Net via deep convolutional framelets: Application to sparse-view CT,” IEEE Trans. Med. Imaging 37(6), 1418–1429 (2018).
[Crossref]

2017 (5)

2015 (2)

L. Fang, S. Li, X. Kang, J. A. Izatt, and S. Farsiu, “3-D adaptive sparsity based image compression with applications to optical coherence tomography,” IEEE Trans. Med. Imaging 34(6), 1306–1320 (2015).
[Crossref]

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]

2014 (1)

2013 (1)

M. Maggioni, V. Katkovnik, K. Egiazarian, and A. J. I. T. O. I. P. Foi, “Nonlocal transform-domain filter for volumetric data denoising and reconstruction,” IEEE Trans. on Image Process. 22(1), 119–133 (2013).
[Crossref]

2012 (1)

S. Lefkimmiatis, A. Bourquard, and M. Unser, “Hessian-based norm regularization for image restoration with biomedical applications,” IEEE Trans. on Image Process. 21(3), 983–995 (2012).
[Crossref]

2007 (1)

K. Dabov, A. Foi, V. Katkovnik, and K. Egiazarian, “Image denoising by sparse 3-D transform-domain collaborative filtering,” IEEE Trans. on Image Process. 16(8), 2080–2095 (2007).
[Crossref]

2006 (1)

D. L. Donoho, “Compressed sensing,” IEEE Trans. Inform. Theory 52(4), 1289–1306 (2006).
[Crossref]

2004 (1)

Z. Wang, A. C. Bovik, H. R. Sheikh, and E. P. Simoncelli, “Image quality assessment: from error visibility to structural similarity,” IEEE Trans. on Image Process. 13(4), 600–612 (2004).
[Crossref]

1992 (1)

G. K. Wallace, “The JPEG still picture compression standard,” IEEE Trans. Consumer Electron. 38(1), xviii–xxxiv (1992).
[Crossref]

1991 (1)

D. Le Gall, “MPEG: A video compression standard for multimedia applications,” Commun. ACM 34(4), 46–58 (1991).
[Crossref]

Abdulghani, A. M.

A. M. Abdulghani and E. Rodriguez-Villegas, “Compressive sensing: From “Compressing while Sampling” to “Compressing and Securing while Sampling,” in 2010 Annual International Conference of the IEEE Engineering in Medicine and Biology, 2010), 1127–1130.

Acosta, A.

C. Ledig, L. Theis, F. Huszár, J. Caballero, A. Cunningham, A. Acosta, A. Aitken, A. Tejani, J. Totz, and Z. Wang, “Photo-realistic single image super-resolution using a generative adversarial network,” in Proceedings of the IEEE conference on computer vision and pattern recognition, 2017), 4681–4690.

Agustsson, E.

E. Agustsson, F. Mentzer, M. Tschannen, L. Cavigelli, R. Timofte, L. Benini, and L. V. Gool, “Soft-to-hard vector quantization for end-to-end learning compressible representations,” in Advances in Neural Information Processing Systems, 2017), 1141–1151.

F. Mentzer, E. Agustsson, M. Tschannen, R. Timofte, and L. Van Gool, “Conditional probability models for deep image compression,” in Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, 2018), 4394–4402.

Ahmadi, S.-A.

F. Milletari, N. Navab, and S.-A. Ahmadi, “V-net: Fully convolutional neural networks for volumetric medical image segmentation,” in 2016 Fourth International Conference on 3D Vision (3DV), (IEEE, 2016), 565–571.

Aitken, A.

C. Ledig, L. Theis, F. Huszár, J. Caballero, A. Cunningham, A. Acosta, A. Aitken, A. Tejani, J. Totz, and Z. Wang, “Photo-realistic single image super-resolution using a generative adversarial network,” in Proceedings of the IEEE conference on computer vision and pattern recognition, 2017), 4681–4690.

Alahi, A.

J. Johnson, A. Alahi, and L. Fei-Fei, “Perceptual losses for real-time style transfer and super-resolution,” in European Conference on computer Vision, (Springer, 2016), 694–711.

Albalawi, U.

U. Albalawi, S. P. Mohanty, and E. Kougianos, “A hardware architecture for better portable graphics (BPG) compression encoder,” in 2015 IEEE International Symposium on Nanoelectronic and Information Systems, (IEEE, 2015), 291–296.

Allingham, M. J.

Ba, J.

D. P. Kingma and J. Ba, “Adam: A method for stochastic optimization,” arXiv preprint arXiv:1412.6980 (2014).

Ballé, J.

J. Ballé, V. Laparra, and E. P. Simoncelli, “End-to-end optimized image compression,” arXiv preprint arXiv:1611.01704 (2016).

Benini, L.

E. Agustsson, F. Mentzer, M. Tschannen, L. Cavigelli, R. Timofte, L. Benini, and L. V. Gool, “Soft-to-hard vector quantization for end-to-end learning compressible representations,” in Advances in Neural Information Processing Systems, 2017), 1141–1151.

Berg, T. L.

Y. Zhou and T. L. Berg, “Learning temporal transformations from time-lapse videos,” in European conference on computer vision, (Springer, 2016), 262–277.

Bilgin, A.

F. Liu, M. Hernandez-Cabronero, V. Sanchez, M. Marcellin, and A. Bilgin, “The current role of image compression standards in medical imaging,” Inform. 8(4), 131 (2017).
[Crossref]

Blau, Y.

Y. Blau and T. Michaeli, “The perception-distortion tradeoff,” in Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, 2018), 6228–6237.

Bottou, L.

L. Bottou, “Stochastic gradient descent tricks,” in Neural networks: Tricks of the trade (Springer, 2012), pp. 421–436.

Bourdev, L.

O. Rippel and L. Bourdev, “Real-time adaptive image compression,” in Proceedings of the 34th International Conference on Machine Learning-Volume 70, (JMLR. org, 2017), 2922–2930.

Bourquard, A.

S. Lefkimmiatis, A. Bourquard, and M. Unser, “Hessian-based norm regularization for image restoration with biomedical applications,” IEEE Trans. on Image Process. 21(3), 983–995 (2012).
[Crossref]

Bovik, A. C.

Z. Wang, A. C. Bovik, H. R. Sheikh, and E. P. Simoncelli, “Image quality assessment: from error visibility to structural similarity,” IEEE Trans. on Image Process. 13(4), 600–612 (2004).
[Crossref]

Z. Wang, E. P. Simoncelli, and A. C. Bovik, “Multiscale structural similarity for image quality assessment,” in The Thrity-Seventh Asilomar Conference on Signals, Systems & Computers, 2003, (IEEE, 2003), 1398–1402.

Brox, T.

O. Ronneberger, P. Fischer, and T. Brox, “U-net: Convolutional networks for biomedical image segmentation,” in International Conference on Medical Image Computing and Computer-assisted Intervention, (Springer, 2015), 234–241.

Caballero, J.

C. Ledig, L. Theis, F. Huszár, J. Caballero, A. Cunningham, A. Acosta, A. Aitken, A. Tejani, J. Totz, and Z. Wang, “Photo-realistic single image super-resolution using a generative adversarial network,” in Proceedings of the IEEE conference on computer vision and pattern recognition, 2017), 4681–4690.

Cavigelli, L.

E. Agustsson, F. Mentzer, M. Tschannen, L. Cavigelli, R. Timofte, L. Benini, and L. V. Gool, “Soft-to-hard vector quantization for end-to-end learning compressible representations,” in Advances in Neural Information Processing Systems, 2017), 1141–1151.

Chen, D.

Chinen, T.

N. Johnston, D. Vincent, D. Minnen, M. Covell, S. Singh, T. Chinen, S. Jin Hwang, J. Shor, and G. Toderici, “Improved lossy image compression with priming and spatially adaptive bit rates for recurrent networks,” in Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, 2018), 4385–4393.

Chiu, S. J.

Comer, G. M.

Conjeti, S.

Cousins, S. W.

Covell, M.

G. Toderici, D. Vincent, N. Johnston, S. Jin Hwang, D. Minnen, J. Shor, and M. Covell, “Full resolution image compression with recurrent neural networks,” in Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, 2017), 5306–5314.

N. Johnston, D. Vincent, D. Minnen, M. Covell, S. Singh, T. Chinen, S. Jin Hwang, J. Shor, and G. Toderici, “Improved lossy image compression with priming and spatially adaptive bit rates for recurrent networks,” in Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, 2018), 4385–4393.

Cunefare, D.

Cunningham, A.

L. Theis, W. Shi, A. Cunningham, and F. Huszár, “Lossy image compression with compressive autoencoders,” arXiv preprint arXiv:1703.00395 (2017).

C. Ledig, L. Theis, F. Huszár, J. Caballero, A. Cunningham, A. Acosta, A. Aitken, A. Tejani, J. Totz, and Z. Wang, “Photo-realistic single image super-resolution using a generative adversarial network,” in Proceedings of the IEEE conference on computer vision and pattern recognition, 2017), 4681–4690.

Dabov, K.

K. Dabov, A. Foi, V. Katkovnik, and K. Egiazarian, “Image denoising by sparse 3-D transform-domain collaborative filtering,” IEEE Trans. on Image Process. 16(8), 2080–2095 (2007).
[Crossref]

Dong, J.

J. Pan, Y. Liu, J. Dong, J. Zhang, J. Ren, J. Tang, Y.-W. Tai, and M.-H. Yang, “Physics-based generative adversarial models for image restoration and beyond,” arXiv preprint arXiv:1808.00605 (2018).

Donoho, D. L.

D. L. Donoho, “Compressed sensing,” IEEE Trans. Inform. Theory 52(4), 1289–1306 (2006).
[Crossref]

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 the IEEE conference on computer vision and pattern recognition, 2017), 1125–1134.

Egiazarian, K.

M. Maggioni, V. Katkovnik, K. Egiazarian, and A. J. I. T. O. I. P. Foi, “Nonlocal transform-domain filter for volumetric data denoising and reconstruction,” IEEE Trans. on Image Process. 22(1), 119–133 (2013).
[Crossref]

K. Dabov, A. Foi, V. Katkovnik, and K. Egiazarian, “Image denoising by sparse 3-D transform-domain collaborative filtering,” IEEE Trans. on Image Process. 16(8), 2080–2095 (2007).
[Crossref]

Eldar, Y. C.

Y. C. Eldar and G. Kutyniok, Compressed Sensing: Theory and Applications (Cambridge University Press, 2012).

Fang, L.

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]

L. Fang, S. Li, X. Kang, J. A. Izatt, and S. Farsiu, “3-D adaptive sparsity based image compression with applications to optical coherence tomography,” IEEE Trans. Med. Imaging 34(6), 1306–1320 (2015).
[Crossref]

Farsiu, S.

Fei-Fei, L.

J. Johnson, A. Alahi, and L. Fei-Fei, “Perceptual losses for real-time style transfer and super-resolution,” in European Conference on computer Vision, (Springer, 2016), 694–711.

Ferreira, M.

Fischer, P.

O. Ronneberger, P. Fischer, and T. Brox, “U-net: Convolutional networks for biomedical image segmentation,” in International Conference on Medical Image Computing and Computer-assisted Intervention, (Springer, 2015), 234–241.

Foi, A.

K. Dabov, A. Foi, V. Katkovnik, and K. Egiazarian, “Image denoising by sparse 3-D transform-domain collaborative filtering,” IEEE Trans. on Image Process. 16(8), 2080–2095 (2007).
[Crossref]

Foi, A. J. I. T. O. I. P.

M. Maggioni, V. Katkovnik, K. Egiazarian, and A. J. I. T. O. I. P. Foi, “Nonlocal transform-domain filter for volumetric data denoising and reconstruction,” IEEE Trans. on Image Process. 22(1), 119–133 (2013).
[Crossref]

Gonçalves, L.

Gool, L. V.

E. Agustsson, F. Mentzer, M. Tschannen, L. Cavigelli, R. Timofte, L. Benini, and L. V. Gool, “Soft-to-hard vector quantization for end-to-end learning compressible representations,” in Advances in Neural Information Processing Systems, 2017), 1141–1151.

Gupta, A.

X. Wang and A. Gupta, “Generative image modeling using style and structure adversarial networks," in European Conference on Computer Vision, (Springer, 2016), 318–335.

Guymer, R. H.

Hacihaliloglu, I.

P. Wang, V. M. Patel, and I. Hacihaliloglu, “Simultaneous segmentation and classification of bone surfaces from ultrasound using a multi-feature guided cnn,” in International Conference on Medical Image Computing and Computer-Assisted Intervention, (Springer, 2018), 134–142.

Hajizadeh, F.

R. Rasti, H. Rabbani, A. Mehridehnavi, and F. Hajizadeh, “Macular OCT classification using a multi-scale convolutional neural network ensemble,” IEEE Trans. Med. Imaging 37(4), 1024–1034 (2018).
[Crossref]

Han, Y.

Y. Han and J. C. Ye, “Framing U-Net via deep convolutional framelets: Application to sparse-view CT,” IEEE Trans. Med. Imaging 37(6), 1418–1429 (2018).
[Crossref]

He, Y.

Hernandez-Cabronero, M.

F. Liu, M. Hernandez-Cabronero, V. Sanchez, M. Marcellin, and A. Bilgin, “The current role of image compression standards in medical imaging,” Inform. 8(4), 131 (2017).
[Crossref]

Hinton, G. E.

A. Krizhevsky, I. Sutskever, and G. E. Hinton, “Imagenet classification with deep convolutional neural networks,” in Advances in neural information processing systems, 2012), 1097–1105.

Huszár, F.

L. Theis, W. Shi, A. Cunningham, and F. Huszár, “Lossy image compression with compressive autoencoders,” arXiv preprint arXiv:1703.00395 (2017).

C. Ledig, L. Theis, F. Huszár, J. Caballero, A. Cunningham, A. Acosta, A. Aitken, A. Tejani, J. Totz, and Z. Wang, “Photo-realistic single image super-resolution using a generative adversarial network,” in Proceedings of the IEEE conference on computer vision and pattern recognition, 2017), 4681–4690.

Iglovikov, V.

V. Iglovikov and A. Shvets, “Ternausnet: U-net with vgg11 encoder pre-trained on imagenet for image segmentation,” arXiv preprint arXiv:1801.05746 (2018).

Ioffe, S.

S. Ioffe and C. Szegedy, “Batch normalization: Accelerating deep network training by reducing internal covariate shift,” arXiv preprint arXiv:1502.03167 (2015).

Isola, P.

P. Isola, J.-Y. Zhu, T. Zhou, and A. A. Efros, “Image-to-image translation with conditional adversarial networks,” in Proceedings of the IEEE conference on computer vision and pattern recognition, 2017), 1125–1134.

Izatt, J. A.

Jin Hwang, S.

G. Toderici, D. Vincent, N. Johnston, S. Jin Hwang, D. Minnen, J. Shor, and M. Covell, “Full resolution image compression with recurrent neural networks,” in Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, 2017), 5306–5314.

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Commun. ACM (1)

D. Le Gall, “MPEG: A video compression standard for multimedia applications,” Commun. ACM 34(4), 46–58 (1991).
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IEEE Signal Process. Lett. (1)

P. Wang, H. Zhang, and V. M. Patel, “SAR image despeckling using a convolutional neural network,” IEEE Signal Process. Lett. 24(12), 1763–1767 (2017).
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IEEE Trans. Med. Imaging (3)

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D. Taubman and M. Marcellin, JPEG2000 Image Compression Fundamentals, Standards and Practice: Image Compression Fundamentals, Standards and Practice (Springer Science & Business Media, 2012), Vol. 642.

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Figures (5)

Fig. 1.
Fig. 1. (a) Schematic of data preprocessing procedure. There were three layers resulting from segmentation: 1. Inner limiting membrane (ILM: green line); 2. Bruch’s membrane (BW: redline); 3. Lower boundary corresponding to BM (LBM: yellow line). (b) Schematic of compression workflow, which is a conditional GANs model that contains a generator (including compression CNNs and reconstruction CNNs), and a discriminator for adversarial training (training phrase only). An additional differentiable MS-SSIM loss module is also deployed (training phrase only). The compressed binary output is quantized feature representations from the compression CNNs and serves as the input for the reconstruction CNNs.
Fig. 2.
Fig. 2. Schematic of proposed generator in a conditional GANs model, which contains compression CNNs (blue blocks on the left) and reconstruction CNNs (green blocks on the right). Leaky Rectified Linear Unit (Leaky ReLu) and Rectified Linear Unit (ReLu) induce nonlinearity for efficient training [7]. Convolution layers with stride 2 reduce feature maps by a factor of 2 along each dimension [13]. Arrows with different colors indicate different operations in the networks: the red arrows indicate quantized skip connections from the compression CNNs to the reconstruction CNNs via a quantizer; the black arrow represents the quantization operation on the most contracted feature map from the last layer of the compression CNNs; arrows with other colors represent the different combinations of convolution layers, normalization layers and activation functions. Please see the Section 2.2.1 for more details.
Fig. 3.
Fig. 3. (a) Visual comparison between the original image and the reconstructed ones with the proposed compression method at a compression ratio (CR) of 10, 20, 40, and 80, respectively. (b) 8x zoomed-in visual comparison of retina images with fine structures for the red square regions in the original and reconstructed images. (c) Visual comparison between the original image and BPG images at a compression ratio of 10, 20, 40 and 80, respectively. The arrows point to the Bruch’s membrane.
Fig. 4.
Fig. 4. Comparison between the images with age-related macular degeneration (AMD) and diabetic macular edema (DME) and the reconstructed ones by the proposed compression method at a compression ratio of 80. The first row shows the original and reconstructed images with AMD. The second and third rows show the original and reconstructed images with DME. The circle indicates drusen, the arrows point to hyperreflective foci, the “*” indicates a giant outer nuclear layer (ONL) cyst, and the bracket indicates inner layer cysts.
Fig. 5.
Fig. 5. (a) Visual comparison of the reconstructed images between four models at a compression ratio of 40. (b) Quantitative comparison by CR curve between the proposed method, the method without data preprocessing (including denoising and segmentation), the method without skip connections, and the method without both.

Tables (2)

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Table 1. Quantitative comparison of different methods in term of the average of MS-SSIM. The corresponding 95% confidence intervals are reported in the brackets. CR denotes the compression ratio. EDT denotes the encoding-decoding time per image in seconds.

Tables Icon

Table 2. Quantitative comparison of different methods in term of the average of MS-SSIM on a second set of ophthalmic OCT images from an independent source [2]. The corresponding 95% confidence intervals are reported in the brackets.

Equations (4)

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C o m p r e s s i o n R a t i o ( C R ) = H i n p u t W i n p u t S i = 0 n H i W i C i log 2 L .
L G = arg m i n G m a x D L c G A N ( G , D ) + λ L M S S S I M ( G ) ,
L c G A N ( G , D ) = E x , y [ log D ( x , y ) ] + E x , z [ log ( 1 D ( x , G ( x , z ) ) ] .
M S S S I M ( x , y ) = l M ( x , y ) j = 1 M c j ( x , y ) s j ( x , y ) ,

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