Abstract

We present a computational approach for improving the quality of the resolution of images acquired from commonly available low magnification commercial slide scanners. Images from such scanners can be acquired cheaply and are efficient in terms of storage and data transfer. However, they are generally of poorer quality than images from high-resolution scanners and microscopes and do not have the necessary resolution needed in diagnostic or clinical environments, and hence are not used in such settings. The driving question of this presented research is whether the resolution of these images could be enhanced such that it would serve the same diagnostic purpose as high-resolution images from expensive scanners or microscopes. This need is generally known as the image super-resolution (SR) problem in image processing, and it has been studied extensively. Even so, none of the existing methods directly work for the slide scanner images, due to the unique challenges posed by this modality. Here, we propose a convolutional neural network (CNN) based approach, which is specifically trained to take low-resolution slide scanner images of cancer data and convert it into a high-resolution image. We validate these resolution improvements with computational analysis to show the enhanced images offer the same quantitative results. In summary, our extensive experiments demonstrate that this method indeed produces images that are similar to images from high-resolution scanners, both in quality and quantitative measures. This approach opens up new application possibilities for using low-resolution scanners, not only in terms of cost but also in access and speed of scanning for both research and possible clinical use.

© 2018 Optical Society of America under the terms of the OSA Open Access Publishing Agreement

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2017 (1)

Y. Chen and T. Pock, “Trainable nonlinear reaction diffusion: A flexible framework for fast and effective image restoration,” IEEE Transactions on Pattern Analysis Mach. Intell. 39, 1256–1272 (2017).
[Crossref]

2016 (3)

D. Liu, Z. Wang, B. Wen, J. Yang, W. Han, and T. S. Huang, “Robust single image super-resolution via deep networks with sparse prior,” IEEE Transactions on Image Process. 25, 3194–3207 (2016).
[Crossref]

C. Dong, C. C. Loy, K. He, and X. Tang, “Image super-resolution using deep convolutional networks,” IEEE Transactions on Pattern Analysis Mach. intelligence 38, 295–307 (2016).
[Crossref]

X. Zhang, J. Zou, K. He, and J. Sun, “Accelerating very deep convolutional networks for classification and detection,” IEEE Transactions on Pattern Analysis Mach. Intell. 38, 1943–1955 (2016).
[Crossref]

2012 (1)

J. Yang, Z. Wang, Z. Lin, S. Cohen, and T. Huang, “Coupled dictionary training for image super-resolution,” IEEE Transactions on Image Process. 21, 3467–3478 (2012).
[Crossref]

2011 (5)

W. Dong, L. Zhang, G. Shi, and X. Wu, “Image deblurring and super-resolution by adaptive sparse domain selection and adaptive regularization,” IEEE Transactions on Image Process. 20, 1838–1857 (2011).
[Crossref]

M. W. Conklin, J. C. Eickhoff, K. M. Riching, C. A. Pehlke, K. W. Eliceiri, P. P. Provenzano, A. Friedl, and P. J. Keely, “Aligned collagen is a prognostic signature for survival in human breast carcinoma,” Am. J. Pathol. 178, 1221–1232 (2011).
[Crossref] [PubMed]

D. C. Wilbur, “Digital cytology: current state of the art and prospects for the future,” Acta Cytol. 55, 227–238 (2011).
[Crossref] [PubMed]

M. Zheng, J. Bu, C. Chen, C. Wang, L. Zhang, G. Qiu, and D. Cai, “Graph regularized sparse coding for image representation,” IEEE TIP 20, 1327–1336 (2011).

L. Pantanowitz, P. N. Valenstein, A. J. Evans, K. J. Kaplan, J. D. Pfeifer, D. C. Wilbur, L. C. Collins, and T. J. Colgan, “Review of the current state of whole slide imaging in pathology,” J. Pathol. Informatics 2, 36 (2011).
[Crossref]

2010 (2)

K. I. Kim and Y. Kwon, “Single-image super-resolution using sparse regression and natural image prior,” IEEE PAMI 32, 1127–1133 (2010).
[Crossref]

J. Yang, J. Wright, T. S. Huang, and Y. Ma, “Image super-resolution via sparse representation,” IEEE TIP 19, 2861–2873 (2010).

2009 (2)

D. C. Wilbur, K. Madi, R. B. Colvin, L. M. Duncan, W. C. Faquin, J. A. Ferry, M. P. Frosch, S. L. Houser, R. L. Kradin, Gregory Y. Lauwers, D. Louis, E. Mark, M. Mino-Kenudson, J. Misdraji, G. Nielsen, M. Pitman, A. Rosenberg, R. Smith, A. Sohani, J. Stone, R. Tambouret, C. Wu, R. H. Young, A. Zembowicz, and W. Klietmann, “Whole-slide imaging digital pathology as a platform for teleconsultation: a pilot study using paired subspecialist correlations,” Arch. Pathology & Laboratory Medicine 133, 1949–1953 (2009).

L. Pantanowitz, M. Hornish, and R. A. Goulart, “The impact of digital imaging in the field of cytopathology,” Cytojournal 6, 6 (2009).
[Crossref] [PubMed]

2006 (1)

J. R. Gilbertson, J. Ho, L. Anthony, D. M. Jukic, Y. Yagi, and A. V. Parwani, “Primary histologic diagnosis using automated whole slide imaging: a validation study,” BMC Clin. Pathol. 6, 4 (2006).
[Crossref] [PubMed]

2005 (1)

H. R. Sheikh, A. C. Bovik, and G. de Veciana, “An information fidelity criterion for image quality assessment using natural scene statistics,” IEEE Transactions on Image Process. 14, 2117–2128 (2005).
[Crossref]

2004 (1)

R. S. Weinstein, M. R. Descour, C. Liang, G. Barker, K. M. Scott, L. Richter, E. A. Krupinski, A. K. Bhattacharyya, J. R. Davis, A. Graham, M. Rennels, W. Russum, J. Goodall, P. Zhou, A. Olszak, B. Williams, J. Wyant, and P. Bartels, “An array microscope for ultrarapid virtual slide processing and telepathology. design, fabrication, and validation study,” Hum. Pathol. 35, 1303–1314 (2004).
[Crossref]

2001 (1)

X. Li and M. T. Orchard, “New edge-directed interpolation,” IEEE TIP 10, 1521–1527 (2001).

2000 (1)

N. Damera-Venkata, T. D. Kite, W. S. Geisler, B. L. Evans, and A. C. Bovik, “Image quality assessment based on a degradation model,” IEEE Transactions on Image Process. 9, 636–650 (2000).
[Crossref]

1979 (1)

C. E. Duchon, “Lanczos filtering in one and two dimensions,” J. Appl. Meteorol. 18, 1016–1022 (1979).
[Crossref]

Abel, E.

S. Best, Y. Liu, A. Keikhosravi, C. Drifka, K. Woo, G. Mehta, M. Altwegg, T. Thimm, M. Houlihan, J. Bredfeldt, E. Abel, W. Huang, and K. Eliceiri, “Collagen organization of renal cell carcinoma differs between low and high grade tumors,” Urol. (submitted) (2018).

Acosta, A.

C. Ledig, L. Theis, F. Huszár, J. Caballero, A. Cunningham, A. Acosta, A. P. Aitken, A. Tejani, J. Totz, Z. Wang, and W. Shi, “Photo-realistic single image super-resolution using a generative adversarial network,” in “CVPR,” (2017), vol. 2, p. 4.

Ahuja, N.

W.-S. Lai, J.-B. Huang, N. Ahuja, and M.-H. Yang, “Fast and accurate image super-resolution with deep laplacian pyramid networks,” arXiv preprint arXiv:1710.01992 (2017).

J.-B. Huang, A. Singh, and N. Ahuja, “Single image super-resolution from transformed self-exemplars,” in “Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition,” (2015), pp. 5197–5206.

Aitken, A.

W. Shi, J. Caballero, L. Theis, F. Huszar, A. Aitken, C. Ledig, and Z. Wang, “Is the deconvolution layer the same as a convolutional layer?” arXiv preprint arXiv:1609.07009 (2016).

Aitken, A. P.

W. Shi, J. Caballero, F. Huszár, J. Totz, A. P. Aitken, R. Bishop, D. Rueckert, and Z. Wang, “Real-time single image and video super-resolution using an efficient sub-pixel convolutional neural network,” in “Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition,” (2016), pp. 1874–1883.

C. Ledig, L. Theis, F. Huszar, J. Caballero, A. P. Aitken, A. Tejani, J. Totz, Z. Wang, and W. Shi, “Photo-realistic single image super-resolution using a generative adversarial network,” CoRR (2016).

C. Ledig, L. Theis, F. Huszár, J. Caballero, A. Cunningham, A. Acosta, A. P. Aitken, A. Tejani, J. Totz, Z. Wang, and W. Shi, “Photo-realistic single image super-resolution using a generative adversarial network,” in “CVPR,” (2017), vol. 2, p. 4.

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), pp. 694–711.

Allebach, J.

J. Allebach and P. W. Wong, “Edge-directed interpolation,” in “ICIP,”, vol. 3 (IEEE, 1996), vol. 3, pp. 707–710.

Altwegg, M.

S. Best, Y. Liu, A. Keikhosravi, C. Drifka, K. Woo, G. Mehta, M. Altwegg, T. Thimm, M. Houlihan, J. Bredfeldt, E. Abel, W. Huang, and K. Eliceiri, “Collagen organization of renal cell carcinoma differs between low and high grade tumors,” Urol. (submitted) (2018).

Angles, T.

T. Angles and S. Mallat, “Generative networks as inverse problems with scattering transforms,” (2018).

Anthony, L.

J. R. Gilbertson, J. Ho, L. Anthony, D. M. Jukic, Y. Yagi, and A. V. Parwani, “Primary histologic diagnosis using automated whole slide imaging: a validation study,” BMC Clin. Pathol. 6, 4 (2006).
[Crossref] [PubMed]

Bagon, S.

D. Glasner, S. Bagon, and M. Irani, “Super-resolution from a single image,” in “IEEE ICCV,” (IEEE, 2009), pp. 349–356.

Bansal, A.

A. Bansal, Y. Sheikh, and D. Ramanan, “Pixelnn: Example-based image synthesis,” arXiv preprint arXiv:1708.05349 (2017).

Barker, G.

R. S. Weinstein, M. R. Descour, C. Liang, G. Barker, K. M. Scott, L. Richter, E. A. Krupinski, A. K. Bhattacharyya, J. R. Davis, A. Graham, M. Rennels, W. Russum, J. Goodall, P. Zhou, A. Olszak, B. Williams, J. Wyant, and P. Bartels, “An array microscope for ultrarapid virtual slide processing and telepathology. design, fabrication, and validation study,” Hum. Pathol. 35, 1303–1314 (2004).
[Crossref]

Bartels, P.

R. S. Weinstein, M. R. Descour, C. Liang, G. Barker, K. M. Scott, L. Richter, E. A. Krupinski, A. K. Bhattacharyya, J. R. Davis, A. Graham, M. Rennels, W. Russum, J. Goodall, P. Zhou, A. Olszak, B. Williams, J. Wyant, and P. Bartels, “An array microscope for ultrarapid virtual slide processing and telepathology. design, fabrication, and validation study,” Hum. Pathol. 35, 1303–1314 (2004).
[Crossref]

Best, S.

S. Best, Y. Liu, A. Keikhosravi, C. Drifka, K. Woo, G. Mehta, M. Altwegg, T. Thimm, M. Houlihan, J. Bredfeldt, E. Abel, W. Huang, and K. Eliceiri, “Collagen organization of renal cell carcinoma differs between low and high grade tumors,” Urol. (submitted) (2018).

Bhattacharyya, A. K.

R. S. Weinstein, M. R. Descour, C. Liang, G. Barker, K. M. Scott, L. Richter, E. A. Krupinski, A. K. Bhattacharyya, J. R. Davis, A. Graham, M. Rennels, W. Russum, J. Goodall, P. Zhou, A. Olszak, B. Williams, J. Wyant, and P. Bartels, “An array microscope for ultrarapid virtual slide processing and telepathology. design, fabrication, and validation study,” Hum. Pathol. 35, 1303–1314 (2004).
[Crossref]

Bischof, H.

S. Schulter, C. Leistner, and H. Bischof, “Fast and accurate image upscaling with super-resolution forests,” in “Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition,” (2015), pp. 3791–3799.

Bishop, R.

W. Shi, J. Caballero, F. Huszár, J. Totz, A. P. Aitken, R. Bishop, D. Rueckert, and Z. Wang, “Real-time single image and video super-resolution using an efficient sub-pixel convolutional neural network,” in “Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition,” (2016), pp. 1874–1883.

Bovik, A. C.

H. R. Sheikh, A. C. Bovik, and G. de Veciana, “An information fidelity criterion for image quality assessment using natural scene statistics,” IEEE Transactions on Image Process. 14, 2117–2128 (2005).
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Figures (11)

Fig. 1
Fig. 1 Architecture of our proposed convolutional neural network for image superresolution.
Fig. 2
Fig. 2 Results of effect of KNN applied to the CNN outputs. Row 1 shows the original images, Row 2 shows a small region of the corresponding images in Row 1, zoomed in.
Fig. 3
Fig. 3 Results of reconstruction: Columns 1 and 3 show high and low resolution images and Column 2 shows the reconstructed image. Row 3 shows a small ROI of the breast images from Row 2 at full view.
Fig. 4
Fig. 4 Results of reconstruction: Columns 1 and 3 show high and low resolution images and Column 2 shows the reconstructed image. Row 3 shows a small ROI of the kidney images from Row 2 at full view.
Fig. 5
Fig. 5 Results of reconstruction of a breast image (row 1 and row 2) and a kidney image (row 3 and row 4) from other methods: Row 1 and 3 shows ScR, CSCN and FSRCNN, Row 2 and 4 shows ESCNN, SCDL and SRGAN, our results on this image in shown in Figure 3 and Figure 4.
Fig. 6
Fig. 6 Reconstruction error as a function of frequency.
Fig. 7
Fig. 7 Results of segmentation: Cols 1 & 3 show the segmentation mask on the high resolution image for a breast and kidney image respectively, Cols 2 & 4 shows the segmentation masks for corresponding reconstructions.
Fig. 8
Fig. 8 Reconstruction as a function of filter size.
Fig. 9
Fig. 9 Reconstruction as a function of filter size.
Fig. 10
Fig. 10 Reconstruction as a function of number of layers.
Fig. 11
Fig. 11 Runtime as a function of resolution.

Tables (4)

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Table 1 Quantitative results from reconstructed Breast images.

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Table 2 Quantitative results from reconstructed Kidney images.

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Table 3 Misclassification error from segmentation.

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Table 4 Reconstruction error (measured as psnr) as a function of resolution variation.

Equations (6)

Equations on this page are rendered with MathJax. Learn more.

Y 1 = σ ( θ 1 × L + b 1 )
Y i = σ ( θ i × Y i 1 + b i ) i 3 , 4 , 5
Y 6 = γ ( θ 6 × Y 5 + b 6 )
SIM ( x , y ) = I ( x , y ) α C ( x , y ) β S ( x . y ) γ
MSSIM ( x , y ) = I P ( x , y ) α p = 1 P C p ( x , y ) β p S p ( x . y ) γ p
L ( H , R ) = ρ MSE ( H , R ) + ( ρ 1 ) MSSIM ( H , R )

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