Abstract

Cytopathologic testing is one of the most critical steps in the diagnosis of diseases, including cancer. However, the task is laborious and demands skill. Associated high cost and low throughput drew considerable interest in automating the testing process. Several neural network architectures were designed to provide human expertise to machines. In this paper, we explore and propose the feasibility of using deep-learning networks for cytopathologic analysis by performing the classification of three important unlabeled, unstained leukemia cell lines (K562, MOLT, and HL60). The cell images used in the classification are captured using a low-cost, high-throughput cell imaging technique: microfluidics-based imaging flow cytometry. We demonstrate that without any conventional fine segmentation followed by explicit feature extraction, the proposed deep-learning algorithms effectively classify the coarsely localized cell lines. We show that the designed deep belief network as well as the deeply pretrained convolutional neural network outperform the conventionally used decision systems and are important in the medical domain, where the availability of labeled data is limited for training. We hope that our work enables the development of a clinically significant high-throughput microfluidic microscopy-based tool for disease screening/triaging, especially in resource-limited settings.

© 2016 Optical Society of America

Full Article  |  PDF Article
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References

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2016 (6)

V. K. Jagannadh, B. P. Bhat, L. A. Nirupa, and S. S. Gorthi, “High-throughput miniaturized microfluidic microscopy with radially parallelized channel geometry,” Anal. Bioanal. Chem. 408, 1909–1916 (2016).
[Crossref]

V. K. Jagannadh, R. S. Murthy, R. Srinivasan, and S. S. Gorthi, “Automated quantitative cytological analysis using portable microfluidic microscopy,” J. Biophoton. 9, 586–595 (2016).
[Crossref]

A. Pouliakis, E. Karakitsou, N. Margari, P. Bountris, M. Haritou, J. Panayiotides, D. Koutsouris, and P. Karakitsos, “Artificial neural networks as decision support tools in cytopathology: past, present, and future,” Biomed. Eng. Comput. Biol. 7, 1–18 (2016).
[Crossref]

G. Gopakumar, V. K. Jagannadh, S. S. Gorthi, and G. R. K. S. Subrahmanyam, “Framework for morphometric classification of cells in imaging flow cytometry,” J. Microsc. 261, 307–319 (2016).
[Crossref]

H. Greenspan, B. van Ginneken, and R. M. Summers, “Guest editorial: deep learning in medical imaging: overview and future promise of an exciting new technique,” IEEE Trans. Med. Imaging 35, 1153–1159 (2016).
[Crossref]

E. Kim, M. Corte-Real, and Z. Baloch, “A deep semantic mobile application for thyroid cytopathology,” Proc. SPIE 9789, 97890A (2016).

2015 (2)

T. Zeng, R. Li, R. Mukkamala, J. Ye, and S. Ji, “Deep convolutional neural networks for annotating gene expression patterns in the mouse brain,” BMC Bioinf. 16, 1–10 (2015).
[Crossref]

L. W. Yaniv Bar, I. Diamant, and H. Greenspan, “Deep learning with non-medical training used for chest pathology identification,” Proc. SPIE 9414, 94140V (2015).

2014 (3)

L. M. Niswander, K. E. McGrath, J. C. Kennedy, and J. Palis, “Improved quantitative analysis of primary bone marrow megakaryocytes utilising imaging flow cytometry,” Cytometry Part A 85, 302–312 (2014).
[Crossref]

E. J. Beers, L. Samsel, L. Mendelsohn, R. Saiyed, K. Y. Fertrin, C. A. Brantner, M. P. Daniels, J. Nichols, J. P. McCoy, and G. J. Kato, “Imaging flow cytometry for automated detection of hypoxia-induced erythrocyte shape change in sickle cell disease,” Am. J. Hematol. 89, 598–603 (2014).
[Crossref]

H. Irshad, A. Veillard, L. Roux, and D. Racoceanu, “Methods for nuclei detection, segmentation, and classification in digital histopathology: a review–2014; current status and future potential,” IEEE Rev. Biomed. Eng. 7, 97–114 (2014).
[Crossref]

2012 (3)

E. Schonbrun, S. S. Gorthi, and D. Schaak, “Microfabricated multiple field of view imaging flow cytometry,” Lab. Chip 12, 268–273 (2012).
[Crossref]

H. Larochelle, M. Mandel, R. Pascanu, and Y. Bengio, “Learning algorithms for the classification restricted Boltzmann machine,” J. Mach. Learn. Res. 13, 643–669 (2012).

N. Barteneva, E. Fasler-Kan, and I. Vorobjev, “Imaging flow cytometry: coping with heterogeneity in biological systems,” J. Histochem. Cytochem. 60, 723–733 (2012).
[Crossref]

2011 (1)

Z. Shi and L. He, “Current status and future potential of neural networks used for medical image processing,” J. Multimedia 6, 244–251 (2011).

2009 (1)

Y. Bengio, “Learning deep architectures for AI,” Found. Trends Mach. Learn. 2, 1–127 (2009).
[Crossref]

2008 (1)

G. Deco, V. K. Jirsa, P. A. Robinson, M. Breakspear, and K. J. Friston, “The dynamic brain: from spiking neurons to neural masses and cortical fields,” PLoS Comput. Biol. 4, e1000092 (2008).

2007 (1)

D. A. Basiji, W. E. Ortyn, L. Liang, V. Venkatachalam, and P. Morrissey, “Cellular image analysis and imaging by flow cytometry,” Clin. Lab. Med. 27, 653–670 (2007).
[Crossref]

2006 (1)

G. E. Hinton and R. R. Salakhutdinov, “Reducing the dimensionality of data with neural networks,” Science 313, 504–507 (2006).
[Crossref]

2002 (1)

G. E. Hinton, “Training products of experts by minimizing contrastive divergence,” Neural Comput. 14, 1771–1800 (2002).
[Crossref]

1996 (2)

B. Sahiner, H.-P. Chan, N. Petrick, D. Wei, M. A. Helvie, D. D. Adler, and M. M. Goodsitt, “Classification of mass and normal breast tissue: a convolution neural network classifier with spatial domain and texture images,” IEEE Trans. Med. Imag. 15, 598–610 (1996).
[Crossref]

J.-P. Thiran and B. Macq, “Morphological feature extraction for the classification of digital images of cancerous tissues,” IEEE Trans. Biomed. Eng. 43, 1011–1020 (1996).
[Crossref]

1995 (1)

S.-C. B. Lo, H.-P. Chan, J.-S. Lin, H. Li, M. T. Freedman, and S. K. Mun, “Artificial convolution neural network for medical image pattern recognition,” Neural Netw. 8, 1201–1214 (1995).
[Crossref]

1984 (1)

S. Geman and D. Geman, “Stochastic relaxation, Gibbs distributions, and the Bayesian restoration of images,” IEEE Trans. Pattern Anal. Mach. Intell. PAMI-6, 721–741 (1984).
[Crossref]

Aalto, M.

M. Aalto, “Classification of Medical Data Using Restricted Boltzmann Machines,” Master’s thesis (University of Tampere, 2014).

Acquaviva, A.

S. Di Cataldo, E. Ficarra, A. Acquaviva, and E. Macii, “Segmentation of nuclei in cancer tissue images: contrasting active contours with morphology-based approach,” in 8th IEEE International Conference on BioInformatics and BioEngineering (BIBE) (2008), pp. 1–6.

Adler, D. D.

B. Sahiner, H.-P. Chan, N. Petrick, D. Wei, M. A. Helvie, D. D. Adler, and M. M. Goodsitt, “Classification of mass and normal breast tissue: a convolution neural network classifier with spatial domain and texture images,” IEEE Trans. Med. Imag. 15, 598–610 (1996).
[Crossref]

Baloch, Z.

E. Kim, M. Corte-Real, and Z. Baloch, “A deep semantic mobile application for thyroid cytopathology,” Proc. SPIE 9789, 97890A (2016).

Barteneva, N.

N. Barteneva, E. Fasler-Kan, and I. Vorobjev, “Imaging flow cytometry: coping with heterogeneity in biological systems,” J. Histochem. Cytochem. 60, 723–733 (2012).
[Crossref]

Basiji, D. A.

D. A. Basiji, W. E. Ortyn, L. Liang, V. Venkatachalam, and P. Morrissey, “Cellular image analysis and imaging by flow cytometry,” Clin. Lab. Med. 27, 653–670 (2007).
[Crossref]

Beers, E. J.

E. J. Beers, L. Samsel, L. Mendelsohn, R. Saiyed, K. Y. Fertrin, C. A. Brantner, M. P. Daniels, J. Nichols, J. P. McCoy, and G. J. Kato, “Imaging flow cytometry for automated detection of hypoxia-induced erythrocyte shape change in sickle cell disease,” Am. J. Hematol. 89, 598–603 (2014).
[Crossref]

Bengio, Y.

H. Larochelle, M. Mandel, R. Pascanu, and Y. Bengio, “Learning algorithms for the classification restricted Boltzmann machine,” J. Mach. Learn. Res. 13, 643–669 (2012).

Y. Bengio, “Learning deep architectures for AI,” Found. Trends Mach. Learn. 2, 1–127 (2009).
[Crossref]

Y. LeCun and Y. Bengio, “Convolutional networks for images, speech, and time series,” in The Handbook of Brain Theory and Neural Networks (MIT Press, 1998), pp. 255–258.

Bhat, B. P.

V. K. Jagannadh, B. P. Bhat, L. A. Nirupa, and S. S. Gorthi, “High-throughput miniaturized microfluidic microscopy with radially parallelized channel geometry,” Anal. Bioanal. Chem. 408, 1909–1916 (2016).
[Crossref]

Borowsky, A.

N. Nayak, H. Chang, A. Borowsky, P. Spellman, and B. Parvin, “Classification of tumor histopathology via sparse feature learning,” in IEEE 10th International Symposium on Biomedical Imaging (ISBI) (2013), pp. 1348–1351.

Bountris, P.

A. Pouliakis, E. Karakitsou, N. Margari, P. Bountris, M. Haritou, J. Panayiotides, D. Koutsouris, and P. Karakitsos, “Artificial neural networks as decision support tools in cytopathology: past, present, and future,” Biomed. Eng. Comput. Biol. 7, 1–18 (2016).
[Crossref]

Brantner, C. A.

E. J. Beers, L. Samsel, L. Mendelsohn, R. Saiyed, K. Y. Fertrin, C. A. Brantner, M. P. Daniels, J. Nichols, J. P. McCoy, and G. J. Kato, “Imaging flow cytometry for automated detection of hypoxia-induced erythrocyte shape change in sickle cell disease,” Am. J. Hematol. 89, 598–603 (2014).
[Crossref]

Breakspear, M.

G. Deco, V. K. Jirsa, P. A. Robinson, M. Breakspear, and K. J. Friston, “The dynamic brain: from spiking neurons to neural masses and cortical fields,” PLoS Comput. Biol. 4, e1000092 (2008).

Brosch, T.

T. Brosch and R. Tam, “Manifold learning of brain MRIs by deep learning,” in Medical Image Computing and Computer-Assisted Intervention (MICCAI), Vol. 8150 of Lecture Notes in Computer Science (Springer, 2013), pp. 633–640.

Buyssens, P.

P. Buyssens, A. Elmoataz, and O. Lézoray, Multiscale Convolutional Neural Networks for Vision-Based Classification of Cells (Springer, 2013), pp. 342–352.

Cai, W.

Q. Li, W. Cai, X. Wang, Y. Zhou, D. Feng, and M. Chen, “Medical image classification with convolutional neural network,” in 13th International Conference on Control Automation Robotics Vision (ICARCV) (2014), pp. 844–848.

Chan, H.-P.

B. Sahiner, H.-P. Chan, N. Petrick, D. Wei, M. A. Helvie, D. D. Adler, and M. M. Goodsitt, “Classification of mass and normal breast tissue: a convolution neural network classifier with spatial domain and texture images,” IEEE Trans. Med. Imag. 15, 598–610 (1996).
[Crossref]

S.-C. B. Lo, H.-P. Chan, J.-S. Lin, H. Li, M. T. Freedman, and S. K. Mun, “Artificial convolution neural network for medical image pattern recognition,” Neural Netw. 8, 1201–1214 (1995).
[Crossref]

Chang, E. I. C.

Y. Xu, T. Mo, Q. Feng, P. Zhong, M. Lai, and E. I. C. Chang, “Deep learning of feature representation with multiple instance learning for medical image analysis,” in IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP) (2014), pp. 1626–1630.

Chang, H.

N. Nayak, H. Chang, A. Borowsky, P. Spellman, and B. Parvin, “Classification of tumor histopathology via sparse feature learning,” in IEEE 10th International Symposium on Biomedical Imaging (ISBI) (2013), pp. 1348–1351.

Chatfield, K.

K. Chatfield, K. Simonyan, A. Vedaldi, and A. Zisserman, “Return of the devil in the details: delving deep into convolutional nets,” in British Machine Vision Conference (2014).

Chen, M.

Q. Li, W. Cai, X. Wang, Y. Zhou, D. Feng, and M. Chen, “Medical image classification with convolutional neural network,” in 13th International Conference on Control Automation Robotics Vision (ICARCV) (2014), pp. 844–848.

Ciresan, D. C.

D. C. Cireşan, A. Giusti, L. M. Gambardella, and J. Schmidhuber, Mitosis Detection in Breast Cancer Histology Images with Deep Neural Networks (Springer, 2013), pp. 411–418.

Corte-Real, M.

E. Kim, M. Corte-Real, and Z. Baloch, “A deep semantic mobile application for thyroid cytopathology,” Proc. SPIE 9789, 97890A (2016).

Daniels, M. P.

E. J. Beers, L. Samsel, L. Mendelsohn, R. Saiyed, K. Y. Fertrin, C. A. Brantner, M. P. Daniels, J. Nichols, J. P. McCoy, and G. J. Kato, “Imaging flow cytometry for automated detection of hypoxia-induced erythrocyte shape change in sickle cell disease,” Am. J. Hematol. 89, 598–603 (2014).
[Crossref]

Deco, G.

G. Deco, V. K. Jirsa, P. A. Robinson, M. Breakspear, and K. J. Friston, “The dynamic brain: from spiking neurons to neural masses and cortical fields,” PLoS Comput. Biol. 4, e1000092 (2008).

Deng, J.

J. Deng, W. Dong, R. Socher, L.-J. Li, K. Li, and L. Fei-Fei, “ImageNet: a large-scale hierarchical image database,” in IEEE Conference on Computer Vision and Pattern Recognition (CVPR) (2009).

Di Cataldo, S.

S. Di Cataldo, E. Ficarra, A. Acquaviva, and E. Macii, “Segmentation of nuclei in cancer tissue images: contrasting active contours with morphology-based approach,” in 8th IEEE International Conference on BioInformatics and BioEngineering (BIBE) (2008), pp. 1–6.

Diamant, I.

L. W. Yaniv Bar, I. Diamant, and H. Greenspan, “Deep learning with non-medical training used for chest pathology identification,” Proc. SPIE 9414, 94140V (2015).

Dong, W.

J. Deng, W. Dong, R. Socher, L.-J. Li, K. Li, and L. Fei-Fei, “ImageNet: a large-scale hierarchical image database,” in IEEE Conference on Computer Vision and Pattern Recognition (CVPR) (2009).

Elmoataz, A.

P. Buyssens, A. Elmoataz, and O. Lézoray, Multiscale Convolutional Neural Networks for Vision-Based Classification of Cells (Springer, 2013), pp. 342–352.

Fasler-Kan, E.

N. Barteneva, E. Fasler-Kan, and I. Vorobjev, “Imaging flow cytometry: coping with heterogeneity in biological systems,” J. Histochem. Cytochem. 60, 723–733 (2012).
[Crossref]

Fei-Fei, L.

J. Deng, W. Dong, R. Socher, L.-J. Li, K. Li, and L. Fei-Fei, “ImageNet: a large-scale hierarchical image database,” in IEEE Conference on Computer Vision and Pattern Recognition (CVPR) (2009).

Feng, D.

Q. Li, W. Cai, X. Wang, Y. Zhou, D. Feng, and M. Chen, “Medical image classification with convolutional neural network,” in 13th International Conference on Control Automation Robotics Vision (ICARCV) (2014), pp. 844–848.

Feng, Q.

Y. Xu, T. Mo, Q. Feng, P. Zhong, M. Lai, and E. I. C. Chang, “Deep learning of feature representation with multiple instance learning for medical image analysis,” in IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP) (2014), pp. 1626–1630.

Fertrin, K. Y.

E. J. Beers, L. Samsel, L. Mendelsohn, R. Saiyed, K. Y. Fertrin, C. A. Brantner, M. P. Daniels, J. Nichols, J. P. McCoy, and G. J. Kato, “Imaging flow cytometry for automated detection of hypoxia-induced erythrocyte shape change in sickle cell disease,” Am. J. Hematol. 89, 598–603 (2014).
[Crossref]

Ficarra, E.

S. Di Cataldo, E. Ficarra, A. Acquaviva, and E. Macii, “Segmentation of nuclei in cancer tissue images: contrasting active contours with morphology-based approach,” in 8th IEEE International Conference on BioInformatics and BioEngineering (BIBE) (2008), pp. 1–6.

Freedman, M. T.

S.-C. B. Lo, H.-P. Chan, J.-S. Lin, H. Li, M. T. Freedman, and S. K. Mun, “Artificial convolution neural network for medical image pattern recognition,” Neural Netw. 8, 1201–1214 (1995).
[Crossref]

Friston, K. J.

G. Deco, V. K. Jirsa, P. A. Robinson, M. Breakspear, and K. J. Friston, “The dynamic brain: from spiking neurons to neural masses and cortical fields,” PLoS Comput. Biol. 4, e1000092 (2008).

Gambardella, L. M.

D. C. Cireşan, A. Giusti, L. M. Gambardella, and J. Schmidhuber, Mitosis Detection in Breast Cancer Histology Images with Deep Neural Networks (Springer, 2013), pp. 411–418.

Geman, D.

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Figures (10)

Fig. 1.
Fig. 1. Block diagram showing the overview of the system.
Fig. 2.
Fig. 2. (a) Frames containing K562 cells (first row), MOLT cells (second row) and HL60 cells (third row) and (b) the corresponding background-subtracted enhanced frames.
Fig. 3.
Fig. 3. (a) Roughly localized cells from K562 frame in Fig. 2 and (b) bounding box containing leftmost object from 3(a), and leftmost cells from background subtracted frames in Fig. 2.
Fig. 4.
Fig. 4. Architecture of RBM.
Fig. 5.
Fig. 5. CD-1 depiction.
Fig. 6.
Fig. 6. DBN for classification: depicting the back propagation-based fine-tuning across the layers.
Fig. 7.
Fig. 7. CNN architecture pretrained ImageNet model used to extract the features for leukemia cell-line classification.
Fig. 8.
Fig. 8. Effectiveness of RBM classification on raw cell images.
Fig. 9.
Fig. 9. Training error of the RBM model during discriminative fine-tuning.
Fig. 10.
Fig. 10. Classification accuracy of RBM and SVM on features generated by CNN-ImageNet as well as on the morphometric features [12].

Tables (8)

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Table 1. Cross-Validation Accuracy—FFN, SVM, and RBM on Cell Images

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Table 2. Classification Accuracy—Learn Structure from Data

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Table 3. Cross-Validation Accuracy on CNN Features

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Table 4. Cross-Validation Accuracy on Features in [12]

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Table 5. Run-Time Analysis

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Table 6. Classification Accuracy: Training with Less Than 50% Samples

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Table 7. Comparison of Class-Specific Accuracy

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Table 8. Comparison of Precision and Recall (One Against All)

Equations (18)

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

P(v,h)=expE(v,h)Σv,hexpE(v,h),
E(v,h)=[hTWv+bTv+cTh];
W=[W11..W1m........Wn1..Wnm];
b=[b1..bm];c=[c1..cn];v=[v1..vm];h=[h1..hn].
P(v)=ΣhexpE(v,h)Σv,hexpE(v,h).
logP(v)θ=Σhp(h|v)E(v,h)θ+Σv,hp(v,h)E(v,h)θ,
logP(v)θ=Eh|v[E(v,h)θ]+E(v,h)[E(v,h)θ],
E(v,h)Wij=hivj;E(v,h)bj=vj;E(v,h)ci=hi,
p(hi|v)=exp(hiWiv+cihi)1+exp(Wiv+ci),
p(hi=1|v)=exp(ci+Wiv)1+exp(ci+Wiv)=11+exp(ci+Wiv).
p(vj=1|h)=11+exp(bj+hTWj),
ΔWij=ϵ(E(vjhi)0E(vjhi)1),
Wijt+1=Wijt+ηΔWijt+1bjt+1=bjt+ηΔbjt+1cit+1=cit+ηΔcit+1
ΔWijt+1=μΔWijt+α(E(vjhi)0E(vjhi)1)=μΔWijt+α[vjP(hi|v)vjP(hi|v)]Δbjt+1=μΔbjt+β(ΣivjΣivj)Δcit+1=μΔcit+γ(ΣjP(hi|v)ΣjP(hi|v)).
XRH×W×D,fRH×W×D×K,YRH×W×K
yijk=bk+i=1Hj=1Wd=1Dfijdk×xi+i1,j+j1,d,
yijd1=max(0,yijd).
yijd2=max(yi+i1,j+j1,d1)1iH,1jW.

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