Abstract

Since its invention, the microscope has been optimized for interpretation by a human observer. With the recent development of deep learning algorithms for automated image analysis, there is now a clear need to re-design the microscope’s hardware for specific interpretation tasks. To increase the speed and accuracy of automated image classification, this work presents a method to co-optimize how a sample is illuminated in a microscope, along with a pipeline to automatically classify the resulting image, using a deep neural network. By adding a “physical layer” to a deep classification network, we are able to jointly optimize for specific illumination patterns that highlight the most important sample features for the particular learning task at hand, which may not be obvious under standard illumination. We demonstrate how our learned sensing approach for illumination design can automatically identify malaria-infected cells with up to 5-10% greater accuracy than standard and alternative microscope lighting designs. We show that this joint hardware-software design procedure generalizes to offer accurate diagnoses for two different blood smear types, and experimentally show how our new procedure can translate across different experimental setups while maintaining high accuracy.

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

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References

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2019 (8)

M. D. Zarella, D. Bowman, F. Aeffner, N. Farahani, A. Xthona, S. F. Absar, A. Parwani, M. Bui, and D. J. Hartman, “A practical guide to whole slide imaging: A white paper from the digital pathology association,” Arch. Pathol. Lab. Med. 143(2), 222–234 (2019). PMID: 30307746.
[Crossref]

H. Li, H. Soto-Montoya, M. Voisin, L. F. Valenzuela, and M. Prakash, “Octopi: Open configurable high-throughput imaging platform for infectious disease diagnosis in the field,” bioRxiv 25, 6 (2019).
[Crossref]

Y. Rivenson, H. Wang, Z. Wei, K. de Haan, Y. Zhang, Y. Wu, H. Günaydin, J. E. Zuckerman, T. Chong, A. E. Sisk, L. M. Westbrook, W. D. Wallace, and A. Ozcan, “Virtual histological staining of unlabelled tissue-autofluorescence images via deep learning,” Nat. Biomed. Eng. 3(6), 466–477 (2019).
[Crossref]

T. Aidukas, R. Eckert, A. R. Harvey, L. Waller, and P. C. Konda, “Low-cost, sub-micron resolution, wide-field computational microscopy using opensource hardware,” Sci. Rep. 9(1), 7457 (2019).
[Crossref]

M. Kellman, E. Bostan, N. Repina, and L. Waller, “Physics-based Learned Design: Optimized Coded-Illumination for Quantitative Phase Imaging,” IEEE Trans. Comput. Imaging 5(3), 344–353 (2019).
[Crossref]

Y. F. Cheng, M. Strachan, Z. Weiss, M. Deb, D. Carone, and V. Ganapati, “Illumination pattern design with deep learning for single-shot Fourier ptychographic microscopy,” Opt. Express 27(2), 644 (2019).
[Crossref]

E. Hershko, L. E. Weiss, T. Michaeli, and Y. Shechtman, “Multicolor localization microscopy and point-spread-function engineering by deep learning,” Opt. Express 27(5), 6158 (2019).
[Crossref]

Y. Xue, S. Cheng, Y. Li, and L. Tian, “Reliable deep-learning-based phase imaging with uncertainty quantification,” Optica 6, 618 (2019).
[Crossref]

2018 (9)

S. Jiang, K. Guo, J. Liao, and G. Zheng, “Solving Fourier ptychographic imaging problems via neural network modeling and TensorFlow,” Biomed. Opt. Express 9(7), 3306 (2018).
[Crossref]

R. Eckert, Z. F. Phillips, and L. Waller, “Efficient illumination angle self-calibration in Fourier ptychography,” Appl. Opt. 57(19), 5434 (2018).
[Crossref]

T. Nguyen, Y. Xue, Y. Li, L. Tian, and G. Nehmetallah, “Deep learning approach for Fourier ptychography microscopy,” Opt. Express 26(20), 26470 (2018).
[Crossref]

M. Chen, Z. F. Phillips, and L. Waller, “Quantitative differential phase contrast (DPC) microscopy with computational aberration correction,” Opt. Express 26(25), 32888 (2018).
[Crossref]

V. Sitzmann, S. Diamond, Y. Peng, X. Dun, S. Boyd, W. Heidrich, F. Heide, and G. Wetzstein, “End-to-end optimization of optics and image processing for achromatic extended depth of field and super-resolution imaging,” ACM Trans. Graph. 37(4), 1–13 (2018).
[Crossref]

J. Chang, V. Sitzmann, X. Dun, W. Heidrich, and G. Wetzstein, “Hybrid optical-electronic convolutional neural networks with optimized diffractive optics for image classification,” Sci. Rep. 8(1), 12324 (2018).
[Crossref]

B. Diederich, R. Wartmann, H. Schadwinkel, and R. Heintzmann, “Using machine-learning to optimize phase contrast in a low-cost cellphone microscope,” PLoS One 13(3), e0192937 (2018).
[Crossref]

E. M. Christiansen, S. J. Yang, D. M. Ando, A. Javaherian, G. Skibinski, S. Lipnick, E. Mount, A. O’Neil, K. Shah, A. K. Lee, P. Goyal, W. Fedus, R. Poplin, A. Esteva, M. Berndl, L. L. Rubin, P. Nelson, and S. Finkbeiner, “In Silico Labeling: Predicting Fluorescent Labels in Unlabeled Images,” Cell 173(3), 792–803.e19 (2018).
[Crossref]

M. Poostchi, K. Silamut, R. J. Maude, S. Jaeger, and G. Thoma, “Image analysis and machine learning for detecting malaria,” Transl. Res. 194, 36–55 (2018).
[Crossref]

2017 (4)

F. Buggenthin, F. Buettner, P. S. Hoppe, M. Endele, M. Kroiss, M. Strasser, M. Schwarzfischer, D. Loeffler, K. D. Kokkaliaris, O. Hilsenbeck, T. Schroeder, F. J. Theis, and C. Marr, “Prospective identification of hematopoietic lineage choice by deep learning,” Nat. Methods 14(4), 403–406 (2017).
[Crossref]

P. Eulenberg, N. Köhler, T. Blasi, A. Filby, A. E. Carpenter, P. Rees, F. J. Theis, and F. A. Wolf, “Reconstructing cell cycle and disease progression using deep learning,” Nat. Commun. 8(1), 463 (2017).
[Crossref]

A. Esteva, B. Kuprel, R. A. Novoa, J. Ko, S. M. Swetter, H. M. Blau, and S. Thrun, “Dermatologist-level classification of skin cancer with deep neural networks,” Nature 542(7639), 115–118 (2017).
[Crossref]

Z. F. Phillips, M. Chen, and L. Waller, “Single-shot quantitative phase microscopy with color-multiplexed differential phase contrast (cDPC),” PLoS One 12(2), e0171228 (2017).
[Crossref]

2016 (4)

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]

A. Madabhushi and G. Lee, “Image analysis and machine learning in digital pathology: Challenges and opportunities,” Med. Image Anal. 33, 170–175 (2016).
[Crossref]

H. S. Park, M. T. Rinehart, K. A. Walzer, J. T. Ashley Chi, and A. Wax, “Automated Detection of P. falciparum using machine learning algorithms with quantitative phase images of unstained cells,” PLoS One 11(9), e0163045 (2016).
[Crossref]

M. Chen, L. Tian, and L. Waller, “3D differential phase contrast microscopy,” Biomed. Opt. Express 7(10), 3940 (2016).
[Crossref]

2015 (5)

2014 (5)

L. Tian, X. Li, K. Ramchandran, and L. Waller, “Multiplexed coded illumination for Fourier Ptychography with an LED array microscope,” Biomed. Opt. Express 5(7), 2376 (2014).
[Crossref]

L. Bian, J. Suo, G. Situ, G. Zheng, F. Chen, and Q. Dai, “Content adaptive illumination for Fourier ptychography,” Opt. Lett. 39(23), 6648 (2014).
[Crossref]

G. Zheng, X. Ou, R. Horstmeyer, J. Chung, and C. Yang, “Fourier Ptychographic Microscopy: A Gigapixel Superscope for Biomedicine,” Opt. Photonics News 25(4), 26 (2014).
[Crossref]

W. Roobsoong, S. P. Maher, N. Rachaphaew, S. J. Barnes, K. C. Williamson, J. Sattabongkot, and J. H. Adams, “A rapid sensitive, flow cytometry-based method for the detection of Plasmodium vivax-infected blood cells,” Malar. J. 13(1), 55 (2014).
[Crossref]

G. Lu and B. Fei, “Medical hyperspectral imaging: a review,” J. Biomed. Opt. 19(1), 010901 (2014).
[Crossref]

2013 (2)

T. Zhang, S. Osborn, C. Brandow, D. Dwyre, R. Green, S. Lane, and S. Wachsmann-Hogiu, “Structured illumination-based super-resolution optical microscopy for hemato- and cyto-pathology applications,” Anal. Cell. Pathol. 36(1-2), 27–35 (2013).
[Crossref]

G. Zheng, R. Horstmeyer, and C. Yang, “Wide-field, high-resolution Fourier ptychographic microscopy,” Nat. Photonics 7(9), 739–745 (2013).
[Crossref]

2011 (1)

W. Bishara, U. Sikora, O. Mudanyali, T. W. Su, O. Yaglidere, S. Luckhart, and A. Ozcan, “Holographic pixel super-resolution in portable lensless on-chip microscopy using a fiber-optic array,” Lab Chip 11(7), 1276 (2011).
[Crossref]

2009 (1)

Absar, S. F.

M. D. Zarella, D. Bowman, F. Aeffner, N. Farahani, A. Xthona, S. F. Absar, A. Parwani, M. Bui, and D. J. Hartman, “A practical guide to whole slide imaging: A white paper from the digital pathology association,” Arch. Pathol. Lab. Med. 143(2), 222–234 (2019). PMID: 30307746.
[Crossref]

Adams, J. H.

W. Roobsoong, S. P. Maher, N. Rachaphaew, S. J. Barnes, K. C. Williamson, J. Sattabongkot, and J. H. Adams, “A rapid sensitive, flow cytometry-based method for the detection of Plasmodium vivax-infected blood cells,” Malar. J. 13(1), 55 (2014).
[Crossref]

Aeffner, F.

M. D. Zarella, D. Bowman, F. Aeffner, N. Farahani, A. Xthona, S. F. Absar, A. Parwani, M. Bui, and D. J. Hartman, “A practical guide to whole slide imaging: A white paper from the digital pathology association,” Arch. Pathol. Lab. Med. 143(2), 222–234 (2019). PMID: 30307746.
[Crossref]

Aguilar, R.

N. Apthorpe, A. Riordan, R. Aguilar, J. Homann, Y. Gu, D. Tank, and H. S. Seung, “Automatic neuron detection in calcium imaging data using convolutional networks,” in Advances in Neural Information Processing Systems 29, D. D. Lee, M. Sugiyama, U. V. Luxburg, I. Guyon, and R. Garnett, eds. (Curran Associates, Inc., 2016), pp. 3270–3278.

Aidukas, T.

T. Aidukas, R. Eckert, A. R. Harvey, L. Waller, and P. C. Konda, “Low-cost, sub-micron resolution, wide-field computational microscopy using opensource hardware,” Sci. Rep. 9(1), 7457 (2019).
[Crossref]

Airola, K.

N. Wu, J. Phang, J. Park, Y. Shen, Z. Huang, M. Zorin, S. Jastrzębski, T. Févry, J. Katsnelson, E. Kim, S. Wolfson, U. Parikh, S. Gaddam, L. L. Y. Lin, K. Ho, J. D. Weinstein, B. Reig, Y. Gao, H. Toth, K. Pysarenko, A. Lewin, J. Lee, K. Airola, E. Mema, S. Chung, E. Hwang, N. Samreen, S. G. Kim, L. Heacock, L. Moy, K. Cho, and K. J. Geras, “Deep Neural Networks Improve Radiologists’ Performance in Breast Cancer Screening,” arXiv:1903.08297 (2019).

Akinwande, A. I.

A. Goy, G. Rughoobur, S. Li, K. Arthur, A. I. Akinwande, and G. Barbastathis, “High-Resolution Limited-Angle Phase Tomography of Dense Layered Objects Using Deep Neural Networks,” arXiv:1812.07380 (2018).

Andama, A.

J. A. Quinn, R. Nakasi, P. K. B. Mugagga, P. Byanyima, W. Lubega, and A. Andama, “Deep convolutional neural networks for microscopy-based point of care diagnostics,” in Proceedings of the 1st Machine Learning for Healthcare Conference, vol. 56 of Proceedings of Machine Learning ResearchF. Doshi-Velez, J. Fackler, D. Kale, B. Wallace, and J. Wiens, eds. (PMLR, Children’s Hospital LA, Los Angeles, CA, USA, 2016), pp. 271–281.

Ando, D. M.

E. M. Christiansen, S. J. Yang, D. M. Ando, A. Javaherian, G. Skibinski, S. Lipnick, E. Mount, A. O’Neil, K. Shah, A. K. Lee, P. Goyal, W. Fedus, R. Poplin, A. Esteva, M. Berndl, L. L. Rubin, P. Nelson, and S. Finkbeiner, “In Silico Labeling: Predicting Fluorescent Labels in Unlabeled Images,” Cell 173(3), 792–803.e19 (2018).
[Crossref]

Apthorpe, N.

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Arthur, K.

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Figures (8)

Fig. 1.
Fig. 1. We present a learned sensing network (LSN), which optimizes a microscope’s illumination to improve the accuracy of automated image classification. (a) Standard optical microscope outfitted with an array of individually controllable LEDs for illumination. (b) Network training is accomplished with a large number of training image stacks, each containing $N$ uniquely illuminated images. The proposed network’s physical layer combines images within a stack via a weighted sum before classifying the result, where each weight corresponds to the relative brightness of each LED in the array. (c) After training, the physical layer returns an optimized LED illumination pattern that is displayed on the LED array to improve classification accuracies in subsequent experiments.
Fig. 2.
Fig. 2. Optimal single-color illumination patterns determined by our network for thin-smear malaria classification (average over 15 trials). (a) Optimized LED arrangement determined using only the red spectral channel exhibits negative (Pattern 1) and positive (Pattern 2) weights that lead to two LED patterns to display on the LED array, recording two images which are then subtracted. Variance over 15 independent runs of the network’s algorithm shows limited fluctuation in the optimized LED pattern. (b-c) Same as (a), but using only the green and blue spectral channels, respectively, for the classification optimization. Dashed line denotes bright-field/dark-field cutoff.
Fig. 3.
Fig. 3. Optimal multispectral illumination patterns determined by our network for thin-smear malaria classification (average over 15 trials). Learned sensing optimization is jointly performed here over 3 LED colors and 28 LED locations simultaneously, yielding 2 unique spatio-spectral patterns that produce 2 optimally illuminated images for subtraction.
Fig. 4.
Fig. 4. Example images of individual thin-smear blood cells under different forms of illumination. Top two rows are negative examples of cells that do not include a malaria parasite, bottom two rows are positive examples that contain the parasite. Example illuminations include from (a) just the center LED, (b) uniform light from all LEDs, (c) all LEDs with uniformly random brightnesses, (d) a phase contrast-type (PC) arrangement, (e) an off-axis LED, (f) a phase contrast (PC) ring, (g) optimized pattern with red illumination, (h) optimized multispectral pattern, and (i) the same as in (h) but showing response to each individual LED color in pseudo-color, to highlight color illumination’s effect at different locations across the sample.
Fig. 5.
Fig. 5. Optimal multispectral illumination patterns determined by the learned sensing network for thick-smear malaria classification. Network optimization is jointly performed over 3 LED colors and 40 locations simultaneously. Dashed line denotes bright-field/dark-field cutoff. Optimized pattern uses a similar phase contrast mechanism as with the thin smear, but converges towards a distinctly unique spatial pattern and multispectral profile.
Fig. 6.
Fig. 6. Example images of thick-smear locations under different forms of illumination. Top two rows are negative examples of areas that do not include a malaria parasite, bottom two rows are positive examples that contain the parasite. Example illuminations for (a)–(f) mirror those from Fig. 4, while the optimized patterns used for (h)–(i) are shown in Fig. 5.
Fig. 7.
Fig. 7. Full-slide classification performance using a pre-trained learned sensing network with a new experimental setup. (a) Zoom-in of a thick smear image captured in Durham, NC with sliding window classification process depicted on the top. (b) Classification map (in yellow) overlayed on top of the experimental image of the thick smear. Here the classification map for the entire thick smear image was generated by the sliding-window technique, using the learned-sensing CNN trained on the data captured in Erlangen, Germany. (c) Human annotation map of same thick smear. (d) Confusion matrix of classification performance.
Fig. 8.
Fig. 8. Optimal single-color illumination patterns determined by our network for thick-smear malaria classification (average over 15 trials). (a) Optimized LED arrangement determined using only the red spectral channel has (Pattern 1) negative and (Pattern 2) positive weights that lead to two LED patterns to display while capturing and subtracting two images. Variance over 15 independent runs. (b-c) Same as (a), but using only the green and blue spectral channels, respectively, for optimization.

Tables (2)

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Table 1. Learned sensing classification of P. falciparum infection, thin smear.

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Table 2. Learned sensing classification of P. falciparum infection, thick smear

Equations (1)

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I = n = 1 N w n I n .