Abstract

In this paper, we present two models for automatically extracting red blood cells (RBCs) from RBCs holographic images based on a deep learning fully convolutional neural network (FCN) algorithm. The first model, called FCN-1, only uses the FCN algorithm to carry out RBCs prediction, whereas the second model, called FCN-2, combines the FCN approach with the marker-controlled watershed transform segmentation scheme to achieve RBCs extraction. Both models achieve good segmentation accuracy. In addition, the second model has much better performance in terms of cell separation than traditional segmentation methods. In the proposed methods, the RBCs phase images are first numerically reconstructed from RBCs holograms recorded with off-axis digital holographic microscopy. Then, some RBCs phase images are manually segmented and used as training data to fine-tune the FCN. Finally, each pixel in new input RBCs phase images is predicted into either foreground or background using the trained FCN models. The RBCs prediction result from the first model is the final segmentation result, whereas the result from the second model is used as the internal markers of the marker-controlled transform algorithm for further segmentation. Experimental results show that the given schemes can automatically extract RBCs from RBCs phase images and much better RBCs separation results are obtained when the FCN technique is combined with the marker-controlled watershed segmentation algorithm.

© 2017 Optical Society of America

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2017 (2)

Y. Song, E. L. Tan, X. Jiang, J. Z. Cheng, D. Ni, S. Chen, B. Lei, and T. Wang, “Accurate Cervical Cell Segmentation from Overlapping Clumps in Pap Smear Images,” IEEE Trans. Med. Imaging 36(1), 288–300 (2017).
[Crossref] [PubMed]

A. Anand, I. Moon, and B. Javidi, “Automated Disease Identification With 3-D Optical Imaging: A Medical Diagnostic Tool,” Proc. IEEE 105(5), 924–946 (2017).
[Crossref]

2016 (6)

B. Gu, X. Sun, and V. Sheng, “Structural minimax probability machine,” IEEE Trans. Neural Netw. Learn. Syst. 99, 1–11 (2016).
[PubMed]

A. Takeki, T. Trinh, R. Yoshihashi, R. Kawakami, M. Iida, and T. Naemura, “Combining deep features for object detection at various scales: finding small birds in landscape images,” IPSJ Transactions on Computer Vision and Applications 8(1), 5 (2016).
[Crossref]

H. Su, Z. Yin, S. Huh, T. Kanade, and J. Zhu, “Interactive cell segmentation based on active and semi-supervised learning,” IEEE Trans. Med. Imaging 35(3), 762–777 (2016).
[Crossref] [PubMed]

F. Merola, P. Memmolo, L. Miccio, R. Savoia, M. Mugnano, A. Fontana, G. D’Ippolito, A. Sardo, A. Iolascon, A. Gambale, and P. Ferraro, “Tomographic flow cytometry by digital holography,” Light Sci. Appl. 6(4), e16241 (2016).
[Crossref]

P. Y. Liu, L. K. Chin, W. Ser, H. F. Chen, C. M. Hsieh, C. H. Lee, K. B. Sung, T. C. Ayi, P. H. Yap, B. Liedberg, K. Wang, T. Bourouina, and Y. Leprince-Wang, “Cell refractive index for cell biology and disease diagnosis: past, present and future,” Lab Chip 16(4), 634–644 (2016).
[Crossref] [PubMed]

X. He, C. V. Nguyen, M. Pratap, Y. Zheng, Y. Wang, D. R. Nisbet, R. J. Williams, M. Rug, A. G. Maier, and W. M. Lee, “Automated Fourier space region-recognition filtering for off-axis digital holographic microscopy,” Biomed. Opt. Express 7(8), 3111–3123 (2016).
[Crossref] [PubMed]

2015 (3)

B. Rappaz, I. Moon, F. Yi, B. Javidi, P. Marquet, and G. Turcatti, “Automated multi-parameter measurement of cardiomyocytes dynamics with digital holographic microscopy,” Opt. Express 23(10), 13333–13347 (2015).
[Crossref] [PubMed]

F. Yi, I. Moon, and Y. H. Lee, “Three-dimensional counting of morphologically normal human red blood cells via digital holographic microscopy,” J. Biomed. Opt. 20(1), 016005 (2015).
[Crossref] [PubMed]

J. Schmidhuber, “Deep learning in neural networks: An overview,” Neural Netw. 61, 85–117 (2015).
[Crossref] [PubMed]

2014 (5)

X. Yu, J. Hong, C. Liu, M. Cross, D. T. Haynie, and M. K. Kim, “Four-dimensional motility tracking of biological cells by digital holographic microscopy,” J. Biomed. Opt. 19(4), 045001 (2014).
[Crossref] [PubMed]

C. Lu and M. Mandal, “Toward automatic mitotic cell detection and segmentation in multispectral histopathological images,” IEEE J. Biomed. Health Inform. 18(2), 594–605 (2014).
[Crossref] [PubMed]

X. Yu, J. Hong, C. Liu, and M. Kim, “Review of digital holographic microscopy for three-dimensional profiling and tracking,” Opt. Eng. 53(11), 112306 (2014).
[Crossref]

P. Memmolo, L. Miccio, F. Merola, O. Gennari, P. A. Netti, and P. Ferraro, “3D morphometry of red blood cells by digital holography,” Cytometry A 85(12), 1030–1036 (2014).
[Crossref] [PubMed]

P. Roma, L. Siman, F. Amaral, U. Agero, and O. Mesquita, “Total three-dimensional imaging of phase objects using defocusing microscopy: Application to red blood cells,” Appl. Phys. Lett. 104(25), 251107 (2014).
[Crossref]

2013 (4)

F. Yi, I. Moon, and Y. H. Lee, “Extraction of target specimens from bioholographic images using interactive graph cuts,” J. Biomed. Opt. 18(12), 126015 (2013).
[Crossref] [PubMed]

F. Yi, I. Moon, B. Javidi, D. Boss, and P. Marquet, “Automated segmentation of multiple red blood cells with digital holographic microscopy,” J. Biomed. Opt. 18(2), 026006 (2013).
[Crossref] [PubMed]

L. B. Dorini, R. Minetto, and N. J. Leite, “Semiautomatic white blood cell segmentation based on multiscale analysis,” IEEE J. Biomed. Health Inform. 17(1), 250–256 (2013).
[Crossref] [PubMed]

I. Moon, A. Anand, M. Cruz, and B. Javidi, “Identification of malaria-infected red blood cells via digital shearing interferometry and statistical inference,” IEEE Photonics J. 5(5), 6900207 (2013).
[Crossref]

2012 (4)

G. Nehmetallah and P. Banerjee, “Applications of digital and analog holography in three-dimensional imaging,” Adv. Opt. Photonics 4(4), 472–553 (2012).
[Crossref]

A. Anand, V. Chhaniwal, N. Patel, and B. Javidi, “Automatic identification of malaria infected RBC with digital holographic microscopy using correlation algorithms,” IEEE Photonics J. 4(5), 1456–1464 (2012).
[Crossref]

F. Merola, L. Miccio, P. Memmolo, M. Paturzo, S. Grilli, and P. Ferraro, “Simultaneous optical manipulation, 3-D tracking, and imaging of micro-objects by digital holography in microfluidics,” IEEE Photonics J. 4(2), 451–454 (2012).
[Crossref]

I. Moon, B. Javidi, F. Yi, D. Boss, and P. Marquet, “Automated statistical quantification of three-dimensional morphology and mean corpuscular hemoglobin of multiple red blood cells,” Opt. Express 20(9), 10295–10309 (2012).
[Crossref] [PubMed]

2010 (2)

N. T. Shaked, L. L. Satterwhite, N. Bursac, and A. Wax, “Whole-cell-analysis of live cardiomyocytes using wide-field interferometric phase microscopy,” Biomed. Opt. Express 1(2), 706–719 (2010).
[Crossref] [PubMed]

B. Kemper, A. Bauwens, A. Vollmer, S. Ketelhut, P. Langehanenberg, J. Müthing, H. Karch, and G. von Bally, “Label-free quantitative cell division monitoring of endothelial cells by digital holographic microscopy,” J. Biomed. Opt. 15(3), 036009 (2010).
[Crossref] [PubMed]

2009 (2)

I. Moon, M. Daneshpanah, B. Javidi, and A. Stern, “Automated three-dimensional identification and tracking of micro/nanobiological organisms by computational holographic microscopy,” Proc. IEEE 97(6), 990–1010 (2009).
[Crossref]

Y. S. Choi and S. J. Lee, “Three-dimensional volumetric measurement of red blood cell motion using digital holographic microscopy,” Appl. Opt. 48(16), 2983–2990 (2009).
[Crossref] [PubMed]

2008 (1)

I. Moon and B. Javidi, “3-D visualization and identification of biological microorganisms using partially temporal incoherent light in-line computational holographic imaging,” IEEE Trans. Med. Imaging 27(12), 1782–1790 (2008).
[Crossref] [PubMed]

2007 (2)

I. Moon and B. Javidi, “3D identification of stem cells by computational holographic imaging,” J. R. Soc. Interface 4(13), 305–313 (2007).
[Crossref] [PubMed]

W. Choi, C. Fang-Yen, K. Badizadegan, S. Oh, N. Lue, R. R. Dasari, and M. S. Feld, “Tomographic phase microscopy,” Nat. Methods 4(9), 717–719 (2007).
[Crossref] [PubMed]

2006 (4)

X. Yang, H. Li, and X. Zhou, “Nuclei segmentation using marker-controlled watershed, tracking using mean-shift, and Kalman filter in time-lapse microscopy,” IEEE Trans. Circ. Syst. 53(11), 2405–2414 (2006).
[Crossref]

T. Colomb, E. Cuche, F. Charrière, J. Kühn, N. Aspert, F. Montfort, P. Marquet, and C. Depeursinge, “Automatic procedure for aberration compensation in digital holographic microscopy and applications to specimen shape compensation,” Appl. Opt. 45(5), 851–863 (2006).
[Crossref] [PubMed]

I. Moon and B. Javidi, “Volumetric three-dimensional recognition of biological microorganisms using multivariate statistical method and digital holography,” J. Biomed. Opt. 11(6), 064004 (2006).
[Crossref] [PubMed]

F. Dubois, C. Yourassowsky, O. Monnom, J. C. Legros, O. Debeir, P. Van Ham, R. Kiss, and C. Decaestecker, “Digital holographic microscopy for the three-dimensional dynamic analysis of in vitro cancer cell migration,” J. Biomed. Opt. 11(5), 054032 (2006).
[Crossref] [PubMed]

2005 (2)

2002 (2)

N. Meuleau and M. Dorigo, “Ant colony optimization and stochastic gradient descent,” Artif. Life 8(2), 103–121 (2002).
[Crossref] [PubMed]

C. Ruberto, A. Dempster, S. Khan, and B. Jarra, “Analysis of infected blood cell images using morphological operators,” Image Vis. Comput. 20(2), 133–146 (2002).
[Crossref]

2001 (1)

G. Ongun, U. Halici, K. Leblebicioğlu, V. Atalay, S. Beksaç, and M. Beksaç, “Automated contour detection in blood cell images by an efficient snake algorithm,” Nonlinear Anal Theory Methods Appl. 47(9), 5839–5847 (2001).
[Crossref]

1999 (1)

1992 (1)

S. S. Poon, R. K. Ward, and B. Palcic, “Automated image detection and segmentation in blood smears,” Cytometry 13(7), 766–774 (1992).
[Crossref] [PubMed]

Agero, U.

P. Roma, L. Siman, F. Amaral, U. Agero, and O. Mesquita, “Total three-dimensional imaging of phase objects using defocusing microscopy: Application to red blood cells,” Appl. Phys. Lett. 104(25), 251107 (2014).
[Crossref]

Ahmadi, S.

P. Christ, M. Elshaer, F. Ettlinger, S. Tatavarty, M. Bickel, P. Bilic, M. Rempfler, M. Armbruster, F. Hofmann, M. D’Anastasi, W. Sommer, S. Ahmadi, and B. Menze, “Automatic Liver and Lesion Segmentation in CT Using Cascaded Fully Convolutional Neural Networks and 3D Conditional Random Fields,” In Proceedings of MICCAI, (2016), pp. 415–423.
[Crossref]

Alliez, P.

E. Maggiori, Y. Tarabalka, G. Charpiat, and P. Alliez, “Fully convolutional neural networks for remote sensing image classification,” In Proceedings of IGARSS, (2016), pp. 5071–5074.
[Crossref]

Amaral, F.

P. Roma, L. Siman, F. Amaral, U. Agero, and O. Mesquita, “Total three-dimensional imaging of phase objects using defocusing microscopy: Application to red blood cells,” Appl. Phys. Lett. 104(25), 251107 (2014).
[Crossref]

Anand, A.

A. Anand, I. Moon, and B. Javidi, “Automated Disease Identification With 3-D Optical Imaging: A Medical Diagnostic Tool,” Proc. IEEE 105(5), 924–946 (2017).
[Crossref]

I. Moon, A. Anand, M. Cruz, and B. Javidi, “Identification of malaria-infected red blood cells via digital shearing interferometry and statistical inference,” IEEE Photonics J. 5(5), 6900207 (2013).
[Crossref]

A. Anand, V. Chhaniwal, N. Patel, and B. Javidi, “Automatic identification of malaria infected RBC with digital holographic microscopy using correlation algorithms,” IEEE Photonics J. 4(5), 1456–1464 (2012).
[Crossref]

Armbruster, M.

P. Christ, M. Elshaer, F. Ettlinger, S. Tatavarty, M. Bickel, P. Bilic, M. Rempfler, M. Armbruster, F. Hofmann, M. D’Anastasi, W. Sommer, S. Ahmadi, and B. Menze, “Automatic Liver and Lesion Segmentation in CT Using Cascaded Fully Convolutional Neural Networks and 3D Conditional Random Fields,” In Proceedings of MICCAI, (2016), pp. 415–423.
[Crossref]

Aspert, N.

Atalay, V.

G. Ongun, U. Halici, K. Leblebicioğlu, V. Atalay, S. Beksaç, and M. Beksaç, “Automated contour detection in blood cell images by an efficient snake algorithm,” Nonlinear Anal Theory Methods Appl. 47(9), 5839–5847 (2001).
[Crossref]

Ayi, T. C.

P. Y. Liu, L. K. Chin, W. Ser, H. F. Chen, C. M. Hsieh, C. H. Lee, K. B. Sung, T. C. Ayi, P. H. Yap, B. Liedberg, K. Wang, T. Bourouina, and Y. Leprince-Wang, “Cell refractive index for cell biology and disease diagnosis: past, present and future,” Lab Chip 16(4), 634–644 (2016).
[Crossref] [PubMed]

Badizadegan, K.

W. Choi, C. Fang-Yen, K. Badizadegan, S. Oh, N. Lue, R. R. Dasari, and M. S. Feld, “Tomographic phase microscopy,” Nat. Methods 4(9), 717–719 (2007).
[Crossref] [PubMed]

Banerjee, P.

G. Nehmetallah and P. Banerjee, “Applications of digital and analog holography in three-dimensional imaging,” Adv. Opt. Photonics 4(4), 472–553 (2012).
[Crossref]

Bauwens, A.

B. Kemper, A. Bauwens, A. Vollmer, S. Ketelhut, P. Langehanenberg, J. Müthing, H. Karch, and G. von Bally, “Label-free quantitative cell division monitoring of endothelial cells by digital holographic microscopy,” J. Biomed. Opt. 15(3), 036009 (2010).
[Crossref] [PubMed]

Beksaç, M.

G. Ongun, U. Halici, K. Leblebicioğlu, V. Atalay, S. Beksaç, and M. Beksaç, “Automated contour detection in blood cell images by an efficient snake algorithm,” Nonlinear Anal Theory Methods Appl. 47(9), 5839–5847 (2001).
[Crossref]

Beksaç, S.

G. Ongun, U. Halici, K. Leblebicioğlu, V. Atalay, S. Beksaç, and M. Beksaç, “Automated contour detection in blood cell images by an efficient snake algorithm,” Nonlinear Anal Theory Methods Appl. 47(9), 5839–5847 (2001).
[Crossref]

Bickel, M.

P. Christ, M. Elshaer, F. Ettlinger, S. Tatavarty, M. Bickel, P. Bilic, M. Rempfler, M. Armbruster, F. Hofmann, M. D’Anastasi, W. Sommer, S. Ahmadi, and B. Menze, “Automatic Liver and Lesion Segmentation in CT Using Cascaded Fully Convolutional Neural Networks and 3D Conditional Random Fields,” In Proceedings of MICCAI, (2016), pp. 415–423.
[Crossref]

Bilic, P.

P. Christ, M. Elshaer, F. Ettlinger, S. Tatavarty, M. Bickel, P. Bilic, M. Rempfler, M. Armbruster, F. Hofmann, M. D’Anastasi, W. Sommer, S. Ahmadi, and B. Menze, “Automatic Liver and Lesion Segmentation in CT Using Cascaded Fully Convolutional Neural Networks and 3D Conditional Random Fields,” In Proceedings of MICCAI, (2016), pp. 415–423.
[Crossref]

Boss, D.

F. Yi, I. Moon, B. Javidi, D. Boss, and P. Marquet, “Automated segmentation of multiple red blood cells with digital holographic microscopy,” J. Biomed. Opt. 18(2), 026006 (2013).
[Crossref] [PubMed]

I. Moon, B. Javidi, F. Yi, D. Boss, and P. Marquet, “Automated statistical quantification of three-dimensional morphology and mean corpuscular hemoglobin of multiple red blood cells,” Opt. Express 20(9), 10295–10309 (2012).
[Crossref] [PubMed]

Bourouina, T.

P. Y. Liu, L. K. Chin, W. Ser, H. F. Chen, C. M. Hsieh, C. H. Lee, K. B. Sung, T. C. Ayi, P. H. Yap, B. Liedberg, K. Wang, T. Bourouina, and Y. Leprince-Wang, “Cell refractive index for cell biology and disease diagnosis: past, present and future,” Lab Chip 16(4), 634–644 (2016).
[Crossref] [PubMed]

Brox, T.

O. Ronneberger, P. Fischer, and T. Brox, “U-net: Convolutional networks for biomedical image segmentation,” In Proceedings of International Conference on Medical Image Computing and Computer-Assisted Intervention, (Springer International Publishing, 2015), pp. 234–241.
[Crossref]

Bursac, N.

Carapezza, E.

Charpiat, G.

E. Maggiori, Y. Tarabalka, G. Charpiat, and P. Alliez, “Fully convolutional neural networks for remote sensing image classification,” In Proceedings of IGARSS, (2016), pp. 5071–5074.
[Crossref]

Charrière, F.

Chen, H. F.

P. Y. Liu, L. K. Chin, W. Ser, H. F. Chen, C. M. Hsieh, C. H. Lee, K. B. Sung, T. C. Ayi, P. H. Yap, B. Liedberg, K. Wang, T. Bourouina, and Y. Leprince-Wang, “Cell refractive index for cell biology and disease diagnosis: past, present and future,” Lab Chip 16(4), 634–644 (2016).
[Crossref] [PubMed]

Chen, S.

Y. Song, E. L. Tan, X. Jiang, J. Z. Cheng, D. Ni, S. Chen, B. Lei, and T. Wang, “Accurate Cervical Cell Segmentation from Overlapping Clumps in Pap Smear Images,” IEEE Trans. Med. Imaging 36(1), 288–300 (2017).
[Crossref] [PubMed]

Cheng, J. Z.

Y. Song, E. L. Tan, X. Jiang, J. Z. Cheng, D. Ni, S. Chen, B. Lei, and T. Wang, “Accurate Cervical Cell Segmentation from Overlapping Clumps in Pap Smear Images,” IEEE Trans. Med. Imaging 36(1), 288–300 (2017).
[Crossref] [PubMed]

Chhaniwal, V.

A. Anand, V. Chhaniwal, N. Patel, and B. Javidi, “Automatic identification of malaria infected RBC with digital holographic microscopy using correlation algorithms,” IEEE Photonics J. 4(5), 1456–1464 (2012).
[Crossref]

Chin, L. K.

P. Y. Liu, L. K. Chin, W. Ser, H. F. Chen, C. M. Hsieh, C. H. Lee, K. B. Sung, T. C. Ayi, P. H. Yap, B. Liedberg, K. Wang, T. Bourouina, and Y. Leprince-Wang, “Cell refractive index for cell biology and disease diagnosis: past, present and future,” Lab Chip 16(4), 634–644 (2016).
[Crossref] [PubMed]

Choi, W.

W. Choi, C. Fang-Yen, K. Badizadegan, S. Oh, N. Lue, R. R. Dasari, and M. S. Feld, “Tomographic phase microscopy,” Nat. Methods 4(9), 717–719 (2007).
[Crossref] [PubMed]

Choi, Y. S.

Christ, P.

P. Christ, M. Elshaer, F. Ettlinger, S. Tatavarty, M. Bickel, P. Bilic, M. Rempfler, M. Armbruster, F. Hofmann, M. D’Anastasi, W. Sommer, S. Ahmadi, and B. Menze, “Automatic Liver and Lesion Segmentation in CT Using Cascaded Fully Convolutional Neural Networks and 3D Conditional Random Fields,” In Proceedings of MICCAI, (2016), pp. 415–423.
[Crossref]

Colomb, T.

Cross, M.

X. Yu, J. Hong, C. Liu, M. Cross, D. T. Haynie, and M. K. Kim, “Four-dimensional motility tracking of biological cells by digital holographic microscopy,” J. Biomed. Opt. 19(4), 045001 (2014).
[Crossref] [PubMed]

Cruz, M.

I. Moon, A. Anand, M. Cruz, and B. Javidi, “Identification of malaria-infected red blood cells via digital shearing interferometry and statistical inference,” IEEE Photonics J. 5(5), 6900207 (2013).
[Crossref]

Cuche, E.

D’Anastasi, M.

P. Christ, M. Elshaer, F. Ettlinger, S. Tatavarty, M. Bickel, P. Bilic, M. Rempfler, M. Armbruster, F. Hofmann, M. D’Anastasi, W. Sommer, S. Ahmadi, and B. Menze, “Automatic Liver and Lesion Segmentation in CT Using Cascaded Fully Convolutional Neural Networks and 3D Conditional Random Fields,” In Proceedings of MICCAI, (2016), pp. 415–423.
[Crossref]

D’Ippolito, G.

F. Merola, P. Memmolo, L. Miccio, R. Savoia, M. Mugnano, A. Fontana, G. D’Ippolito, A. Sardo, A. Iolascon, A. Gambale, and P. Ferraro, “Tomographic flow cytometry by digital holography,” Light Sci. Appl. 6(4), e16241 (2016).
[Crossref]

Daneshpanah, M.

I. Moon, M. Daneshpanah, B. Javidi, and A. Stern, “Automated three-dimensional identification and tracking of micro/nanobiological organisms by computational holographic microscopy,” Proc. IEEE 97(6), 990–1010 (2009).
[Crossref]

Darrell, T.

J. Long, E. Shelhamer, and T. Darrell, “Fully convolutional networks for semantic segmentation,” in Proceedings of IEEE CVPR, (IEEE, 2015), pp. 3431–3440.

Y. Jia, E. Shelhamer, J. Donahue, S. Karayev, J. Long, R. Girshick, S. Guadarrama, and T. Darrell, “Caffe: Convolutional architecture for fast feature embedding,” In Proceedings of ACM Multimedia, (2014), pp. 675–678.
[Crossref]

Dasari, R. R.

W. Choi, C. Fang-Yen, K. Badizadegan, S. Oh, N. Lue, R. R. Dasari, and M. S. Feld, “Tomographic phase microscopy,” Nat. Methods 4(9), 717–719 (2007).
[Crossref] [PubMed]

Debeir, O.

F. Dubois, C. Yourassowsky, O. Monnom, J. C. Legros, O. Debeir, P. Van Ham, R. Kiss, and C. Decaestecker, “Digital holographic microscopy for the three-dimensional dynamic analysis of in vitro cancer cell migration,” J. Biomed. Opt. 11(5), 054032 (2006).
[Crossref] [PubMed]

Decaestecker, C.

F. Dubois, C. Yourassowsky, O. Monnom, J. C. Legros, O. Debeir, P. Van Ham, R. Kiss, and C. Decaestecker, “Digital holographic microscopy for the three-dimensional dynamic analysis of in vitro cancer cell migration,” J. Biomed. Opt. 11(5), 054032 (2006).
[Crossref] [PubMed]

Dempster, A.

C. Ruberto, A. Dempster, S. Khan, and B. Jarra, “Analysis of infected blood cell images using morphological operators,” Image Vis. Comput. 20(2), 133–146 (2002).
[Crossref]

Depeursinge, C.

Donahue, J.

Y. Jia, E. Shelhamer, J. Donahue, S. Karayev, J. Long, R. Girshick, S. Guadarrama, and T. Darrell, “Caffe: Convolutional architecture for fast feature embedding,” In Proceedings of ACM Multimedia, (2014), pp. 675–678.
[Crossref]

Dorigo, M.

N. Meuleau and M. Dorigo, “Ant colony optimization and stochastic gradient descent,” Artif. Life 8(2), 103–121 (2002).
[Crossref] [PubMed]

Dorini, L. B.

L. B. Dorini, R. Minetto, and N. J. Leite, “Semiautomatic white blood cell segmentation based on multiscale analysis,” IEEE J. Biomed. Health Inform. 17(1), 250–256 (2013).
[Crossref] [PubMed]

Dubois, F.

F. Dubois, C. Yourassowsky, O. Monnom, J. C. Legros, O. Debeir, P. Van Ham, R. Kiss, and C. Decaestecker, “Digital holographic microscopy for the three-dimensional dynamic analysis of in vitro cancer cell migration,” J. Biomed. Opt. 11(5), 054032 (2006).
[Crossref] [PubMed]

Elshaer, M.

P. Christ, M. Elshaer, F. Ettlinger, S. Tatavarty, M. Bickel, P. Bilic, M. Rempfler, M. Armbruster, F. Hofmann, M. D’Anastasi, W. Sommer, S. Ahmadi, and B. Menze, “Automatic Liver and Lesion Segmentation in CT Using Cascaded Fully Convolutional Neural Networks and 3D Conditional Random Fields,” In Proceedings of MICCAI, (2016), pp. 415–423.
[Crossref]

Emery, Y.

Ettlinger, F.

P. Christ, M. Elshaer, F. Ettlinger, S. Tatavarty, M. Bickel, P. Bilic, M. Rempfler, M. Armbruster, F. Hofmann, M. D’Anastasi, W. Sommer, S. Ahmadi, and B. Menze, “Automatic Liver and Lesion Segmentation in CT Using Cascaded Fully Convolutional Neural Networks and 3D Conditional Random Fields,” In Proceedings of MICCAI, (2016), pp. 415–423.
[Crossref]

Fang-Yen, C.

W. Choi, C. Fang-Yen, K. Badizadegan, S. Oh, N. Lue, R. R. Dasari, and M. S. Feld, “Tomographic phase microscopy,” Nat. Methods 4(9), 717–719 (2007).
[Crossref] [PubMed]

Feld, M. S.

W. Choi, C. Fang-Yen, K. Badizadegan, S. Oh, N. Lue, R. R. Dasari, and M. S. Feld, “Tomographic phase microscopy,” Nat. Methods 4(9), 717–719 (2007).
[Crossref] [PubMed]

Ferraro, P.

F. Merola, P. Memmolo, L. Miccio, R. Savoia, M. Mugnano, A. Fontana, G. D’Ippolito, A. Sardo, A. Iolascon, A. Gambale, and P. Ferraro, “Tomographic flow cytometry by digital holography,” Light Sci. Appl. 6(4), e16241 (2016).
[Crossref]

P. Memmolo, L. Miccio, F. Merola, O. Gennari, P. A. Netti, and P. Ferraro, “3D morphometry of red blood cells by digital holography,” Cytometry A 85(12), 1030–1036 (2014).
[Crossref] [PubMed]

F. Merola, L. Miccio, P. Memmolo, M. Paturzo, S. Grilli, and P. Ferraro, “Simultaneous optical manipulation, 3-D tracking, and imaging of micro-objects by digital holography in microfluidics,” IEEE Photonics J. 4(2), 451–454 (2012).
[Crossref]

Fischer, P.

O. Ronneberger, P. Fischer, and T. Brox, “U-net: Convolutional networks for biomedical image segmentation,” In Proceedings of International Conference on Medical Image Computing and Computer-Assisted Intervention, (Springer International Publishing, 2015), pp. 234–241.
[Crossref]

Fontana, A.

F. Merola, P. Memmolo, L. Miccio, R. Savoia, M. Mugnano, A. Fontana, G. D’Ippolito, A. Sardo, A. Iolascon, A. Gambale, and P. Ferraro, “Tomographic flow cytometry by digital holography,” Light Sci. Appl. 6(4), e16241 (2016).
[Crossref]

Gambale, A.

F. Merola, P. Memmolo, L. Miccio, R. Savoia, M. Mugnano, A. Fontana, G. D’Ippolito, A. Sardo, A. Iolascon, A. Gambale, and P. Ferraro, “Tomographic flow cytometry by digital holography,” Light Sci. Appl. 6(4), e16241 (2016).
[Crossref]

Gennari, O.

P. Memmolo, L. Miccio, F. Merola, O. Gennari, P. A. Netti, and P. Ferraro, “3D morphometry of red blood cells by digital holography,” Cytometry A 85(12), 1030–1036 (2014).
[Crossref] [PubMed]

Girshick, R.

Y. Jia, E. Shelhamer, J. Donahue, S. Karayev, J. Long, R. Girshick, S. Guadarrama, and T. Darrell, “Caffe: Convolutional architecture for fast feature embedding,” In Proceedings of ACM Multimedia, (2014), pp. 675–678.
[Crossref]

Grilli, S.

F. Merola, L. Miccio, P. Memmolo, M. Paturzo, S. Grilli, and P. Ferraro, “Simultaneous optical manipulation, 3-D tracking, and imaging of micro-objects by digital holography in microfluidics,” IEEE Photonics J. 4(2), 451–454 (2012).
[Crossref]

Gu, B.

B. Gu, X. Sun, and V. Sheng, “Structural minimax probability machine,” IEEE Trans. Neural Netw. Learn. Syst. 99, 1–11 (2016).
[PubMed]

Guadarrama, S.

Y. Jia, E. Shelhamer, J. Donahue, S. Karayev, J. Long, R. Girshick, S. Guadarrama, and T. Darrell, “Caffe: Convolutional architecture for fast feature embedding,” In Proceedings of ACM Multimedia, (2014), pp. 675–678.
[Crossref]

Halici, U.

G. Ongun, U. Halici, K. Leblebicioğlu, V. Atalay, S. Beksaç, and M. Beksaç, “Automated contour detection in blood cell images by an efficient snake algorithm,” Nonlinear Anal Theory Methods Appl. 47(9), 5839–5847 (2001).
[Crossref]

Haynie, D. T.

X. Yu, J. Hong, C. Liu, M. Cross, D. T. Haynie, and M. K. Kim, “Four-dimensional motility tracking of biological cells by digital holographic microscopy,” J. Biomed. Opt. 19(4), 045001 (2014).
[Crossref] [PubMed]

He, X.

Hofmann, F.

P. Christ, M. Elshaer, F. Ettlinger, S. Tatavarty, M. Bickel, P. Bilic, M. Rempfler, M. Armbruster, F. Hofmann, M. D’Anastasi, W. Sommer, S. Ahmadi, and B. Menze, “Automatic Liver and Lesion Segmentation in CT Using Cascaded Fully Convolutional Neural Networks and 3D Conditional Random Fields,” In Proceedings of MICCAI, (2016), pp. 415–423.
[Crossref]

Hong, J.

X. Yu, J. Hong, C. Liu, M. Cross, D. T. Haynie, and M. K. Kim, “Four-dimensional motility tracking of biological cells by digital holographic microscopy,” J. Biomed. Opt. 19(4), 045001 (2014).
[Crossref] [PubMed]

X. Yu, J. Hong, C. Liu, and M. Kim, “Review of digital holographic microscopy for three-dimensional profiling and tracking,” Opt. Eng. 53(11), 112306 (2014).
[Crossref]

Hsieh, C. M.

P. Y. Liu, L. K. Chin, W. Ser, H. F. Chen, C. M. Hsieh, C. H. Lee, K. B. Sung, T. C. Ayi, P. H. Yap, B. Liedberg, K. Wang, T. Bourouina, and Y. Leprince-Wang, “Cell refractive index for cell biology and disease diagnosis: past, present and future,” Lab Chip 16(4), 634–644 (2016).
[Crossref] [PubMed]

Huh, S.

H. Su, Z. Yin, S. Huh, T. Kanade, and J. Zhu, “Interactive cell segmentation based on active and semi-supervised learning,” IEEE Trans. Med. Imaging 35(3), 762–777 (2016).
[Crossref] [PubMed]

Iida, M.

A. Takeki, T. Trinh, R. Yoshihashi, R. Kawakami, M. Iida, and T. Naemura, “Combining deep features for object detection at various scales: finding small birds in landscape images,” IPSJ Transactions on Computer Vision and Applications 8(1), 5 (2016).
[Crossref]

Iolascon, A.

F. Merola, P. Memmolo, L. Miccio, R. Savoia, M. Mugnano, A. Fontana, G. D’Ippolito, A. Sardo, A. Iolascon, A. Gambale, and P. Ferraro, “Tomographic flow cytometry by digital holography,” Light Sci. Appl. 6(4), e16241 (2016).
[Crossref]

Jarra, B.

C. Ruberto, A. Dempster, S. Khan, and B. Jarra, “Analysis of infected blood cell images using morphological operators,” Image Vis. Comput. 20(2), 133–146 (2002).
[Crossref]

Javidi, B.

A. Anand, I. Moon, and B. Javidi, “Automated Disease Identification With 3-D Optical Imaging: A Medical Diagnostic Tool,” Proc. IEEE 105(5), 924–946 (2017).
[Crossref]

B. Rappaz, I. Moon, F. Yi, B. Javidi, P. Marquet, and G. Turcatti, “Automated multi-parameter measurement of cardiomyocytes dynamics with digital holographic microscopy,” Opt. Express 23(10), 13333–13347 (2015).
[Crossref] [PubMed]

F. Yi, I. Moon, B. Javidi, D. Boss, and P. Marquet, “Automated segmentation of multiple red blood cells with digital holographic microscopy,” J. Biomed. Opt. 18(2), 026006 (2013).
[Crossref] [PubMed]

I. Moon, A. Anand, M. Cruz, and B. Javidi, “Identification of malaria-infected red blood cells via digital shearing interferometry and statistical inference,” IEEE Photonics J. 5(5), 6900207 (2013).
[Crossref]

I. Moon, B. Javidi, F. Yi, D. Boss, and P. Marquet, “Automated statistical quantification of three-dimensional morphology and mean corpuscular hemoglobin of multiple red blood cells,” Opt. Express 20(9), 10295–10309 (2012).
[Crossref] [PubMed]

A. Anand, V. Chhaniwal, N. Patel, and B. Javidi, “Automatic identification of malaria infected RBC with digital holographic microscopy using correlation algorithms,” IEEE Photonics J. 4(5), 1456–1464 (2012).
[Crossref]

I. Moon, M. Daneshpanah, B. Javidi, and A. Stern, “Automated three-dimensional identification and tracking of micro/nanobiological organisms by computational holographic microscopy,” Proc. IEEE 97(6), 990–1010 (2009).
[Crossref]

I. Moon and B. Javidi, “3-D visualization and identification of biological microorganisms using partially temporal incoherent light in-line computational holographic imaging,” IEEE Trans. Med. Imaging 27(12), 1782–1790 (2008).
[Crossref] [PubMed]

I. Moon and B. Javidi, “3D identification of stem cells by computational holographic imaging,” J. R. Soc. Interface 4(13), 305–313 (2007).
[Crossref] [PubMed]

I. Moon and B. Javidi, “Volumetric three-dimensional recognition of biological microorganisms using multivariate statistical method and digital holography,” J. Biomed. Opt. 11(6), 064004 (2006).
[Crossref] [PubMed]

B. Javidi, I. Moon, S. Yeom, and E. Carapezza, “Three-dimensional imaging and recognition of microorganism using single-exposure on-line (SEOL) digital holography,” Opt. Express 13(12), 4492–4506 (2005).
[Crossref] [PubMed]

Jia, Y.

Y. Jia, E. Shelhamer, J. Donahue, S. Karayev, J. Long, R. Girshick, S. Guadarrama, and T. Darrell, “Caffe: Convolutional architecture for fast feature embedding,” In Proceedings of ACM Multimedia, (2014), pp. 675–678.
[Crossref]

Jiang, X.

Y. Song, E. L. Tan, X. Jiang, J. Z. Cheng, D. Ni, S. Chen, B. Lei, and T. Wang, “Accurate Cervical Cell Segmentation from Overlapping Clumps in Pap Smear Images,” IEEE Trans. Med. Imaging 36(1), 288–300 (2017).
[Crossref] [PubMed]

Kanade, T.

H. Su, Z. Yin, S. Huh, T. Kanade, and J. Zhu, “Interactive cell segmentation based on active and semi-supervised learning,” IEEE Trans. Med. Imaging 35(3), 762–777 (2016).
[Crossref] [PubMed]

Karayev, S.

Y. Jia, E. Shelhamer, J. Donahue, S. Karayev, J. Long, R. Girshick, S. Guadarrama, and T. Darrell, “Caffe: Convolutional architecture for fast feature embedding,” In Proceedings of ACM Multimedia, (2014), pp. 675–678.
[Crossref]

Karch, H.

B. Kemper, A. Bauwens, A. Vollmer, S. Ketelhut, P. Langehanenberg, J. Müthing, H. Karch, and G. von Bally, “Label-free quantitative cell division monitoring of endothelial cells by digital holographic microscopy,” J. Biomed. Opt. 15(3), 036009 (2010).
[Crossref] [PubMed]

Kawakami, R.

A. Takeki, T. Trinh, R. Yoshihashi, R. Kawakami, M. Iida, and T. Naemura, “Combining deep features for object detection at various scales: finding small birds in landscape images,” IPSJ Transactions on Computer Vision and Applications 8(1), 5 (2016).
[Crossref]

Kemper, B.

B. Kemper, A. Bauwens, A. Vollmer, S. Ketelhut, P. Langehanenberg, J. Müthing, H. Karch, and G. von Bally, “Label-free quantitative cell division monitoring of endothelial cells by digital holographic microscopy,” J. Biomed. Opt. 15(3), 036009 (2010).
[Crossref] [PubMed]

Ketelhut, S.

B. Kemper, A. Bauwens, A. Vollmer, S. Ketelhut, P. Langehanenberg, J. Müthing, H. Karch, and G. von Bally, “Label-free quantitative cell division monitoring of endothelial cells by digital holographic microscopy,” J. Biomed. Opt. 15(3), 036009 (2010).
[Crossref] [PubMed]

Khan, S.

C. Ruberto, A. Dempster, S. Khan, and B. Jarra, “Analysis of infected blood cell images using morphological operators,” Image Vis. Comput. 20(2), 133–146 (2002).
[Crossref]

Kim, M.

X. Yu, J. Hong, C. Liu, and M. Kim, “Review of digital holographic microscopy for three-dimensional profiling and tracking,” Opt. Eng. 53(11), 112306 (2014).
[Crossref]

Kim, M. K.

X. Yu, J. Hong, C. Liu, M. Cross, D. T. Haynie, and M. K. Kim, “Four-dimensional motility tracking of biological cells by digital holographic microscopy,” J. Biomed. Opt. 19(4), 045001 (2014).
[Crossref] [PubMed]

Kiss, R.

F. Dubois, C. Yourassowsky, O. Monnom, J. C. Legros, O. Debeir, P. Van Ham, R. Kiss, and C. Decaestecker, “Digital holographic microscopy for the three-dimensional dynamic analysis of in vitro cancer cell migration,” J. Biomed. Opt. 11(5), 054032 (2006).
[Crossref] [PubMed]

Kühn, J.

Langehanenberg, P.

B. Kemper, A. Bauwens, A. Vollmer, S. Ketelhut, P. Langehanenberg, J. Müthing, H. Karch, and G. von Bally, “Label-free quantitative cell division monitoring of endothelial cells by digital holographic microscopy,” J. Biomed. Opt. 15(3), 036009 (2010).
[Crossref] [PubMed]

Leblebicioglu, K.

G. Ongun, U. Halici, K. Leblebicioğlu, V. Atalay, S. Beksaç, and M. Beksaç, “Automated contour detection in blood cell images by an efficient snake algorithm,” Nonlinear Anal Theory Methods Appl. 47(9), 5839–5847 (2001).
[Crossref]

Lee, C. H.

P. Y. Liu, L. K. Chin, W. Ser, H. F. Chen, C. M. Hsieh, C. H. Lee, K. B. Sung, T. C. Ayi, P. H. Yap, B. Liedberg, K. Wang, T. Bourouina, and Y. Leprince-Wang, “Cell refractive index for cell biology and disease diagnosis: past, present and future,” Lab Chip 16(4), 634–644 (2016).
[Crossref] [PubMed]

Lee, S. J.

Lee, W. M.

Lee, Y. H.

F. Yi, I. Moon, and Y. H. Lee, “Three-dimensional counting of morphologically normal human red blood cells via digital holographic microscopy,” J. Biomed. Opt. 20(1), 016005 (2015).
[Crossref] [PubMed]

F. Yi, I. Moon, and Y. H. Lee, “Extraction of target specimens from bioholographic images using interactive graph cuts,” J. Biomed. Opt. 18(12), 126015 (2013).
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Legros, J. C.

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F. Merola, L. Miccio, P. Memmolo, M. Paturzo, S. Grilli, and P. Ferraro, “Simultaneous optical manipulation, 3-D tracking, and imaging of micro-objects by digital holography in microfluidics,” IEEE Photonics J. 4(2), 451–454 (2012).
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L. B. Dorini, R. Minetto, and N. J. Leite, “Semiautomatic white blood cell segmentation based on multiscale analysis,” IEEE J. Biomed. Health Inform. 17(1), 250–256 (2013).
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B. Rappaz, I. Moon, F. Yi, B. Javidi, P. Marquet, and G. Turcatti, “Automated multi-parameter measurement of cardiomyocytes dynamics with digital holographic microscopy,” Opt. Express 23(10), 13333–13347 (2015).
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F. Yi, I. Moon, and Y. H. Lee, “Three-dimensional counting of morphologically normal human red blood cells via digital holographic microscopy,” J. Biomed. Opt. 20(1), 016005 (2015).
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F. Yi, I. Moon, and Y. H. Lee, “Extraction of target specimens from bioholographic images using interactive graph cuts,” J. Biomed. Opt. 18(12), 126015 (2013).
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F. Yi, I. Moon, B. Javidi, D. Boss, and P. Marquet, “Automated segmentation of multiple red blood cells with digital holographic microscopy,” J. Biomed. Opt. 18(2), 026006 (2013).
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I. Moon, A. Anand, M. Cruz, and B. Javidi, “Identification of malaria-infected red blood cells via digital shearing interferometry and statistical inference,” IEEE Photonics J. 5(5), 6900207 (2013).
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I. Moon, B. Javidi, F. Yi, D. Boss, and P. Marquet, “Automated statistical quantification of three-dimensional morphology and mean corpuscular hemoglobin of multiple red blood cells,” Opt. Express 20(9), 10295–10309 (2012).
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I. Moon, M. Daneshpanah, B. Javidi, and A. Stern, “Automated three-dimensional identification and tracking of micro/nanobiological organisms by computational holographic microscopy,” Proc. IEEE 97(6), 990–1010 (2009).
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I. Moon and B. Javidi, “3-D visualization and identification of biological microorganisms using partially temporal incoherent light in-line computational holographic imaging,” IEEE Trans. Med. Imaging 27(12), 1782–1790 (2008).
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I. Moon and B. Javidi, “3D identification of stem cells by computational holographic imaging,” J. R. Soc. Interface 4(13), 305–313 (2007).
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I. Moon and B. Javidi, “Volumetric three-dimensional recognition of biological microorganisms using multivariate statistical method and digital holography,” J. Biomed. Opt. 11(6), 064004 (2006).
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B. Kemper, A. Bauwens, A. Vollmer, S. Ketelhut, P. Langehanenberg, J. Müthing, H. Karch, and G. von Bally, “Label-free quantitative cell division monitoring of endothelial cells by digital holographic microscopy,” J. Biomed. Opt. 15(3), 036009 (2010).
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Ni, D.

Y. Song, E. L. Tan, X. Jiang, J. Z. Cheng, D. Ni, S. Chen, B. Lei, and T. Wang, “Accurate Cervical Cell Segmentation from Overlapping Clumps in Pap Smear Images,” IEEE Trans. Med. Imaging 36(1), 288–300 (2017).
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I. Moon, M. Daneshpanah, B. Javidi, and A. Stern, “Automated three-dimensional identification and tracking of micro/nanobiological organisms by computational holographic microscopy,” Proc. IEEE 97(6), 990–1010 (2009).
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Y. Song, E. L. Tan, X. Jiang, J. Z. Cheng, D. Ni, S. Chen, B. Lei, and T. Wang, “Accurate Cervical Cell Segmentation from Overlapping Clumps in Pap Smear Images,” IEEE Trans. Med. Imaging 36(1), 288–300 (2017).
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S. Liu, N. Yang, M. Li, and M. Zhou, “A Recursive Recurrent Neural Network for Statistical Machine Translation,” in Proceedings of ACL, (2014), pp. 1491–1500.
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Figures (8)

Fig. 1
Fig. 1

Schematic of an off-axis digital holographic microscopy (DHM).

Fig. 2
Fig. 2

Fully convolutional neural networks [38]. Row A: Single-stream net, upsamples stride 32 predictions back to pixels in a single step (FCN-32s); Row B: Fusing predictions from both the final convolutional layer and the pool4 layer for additional prediction (FCN-16s); Row C: Fusing predictions from the final convolutional layer, pool4, and pool3 for additional prediction (FCN-8s).

Fig. 3
Fig. 3

RBC’s phase images and ground truth label images. (a) RBC’s 3D profile obtained by off-axis DHM, (b) A reconstructed RBCs phase image, (c) A ground truth label image for the FCN-1 model, (d) A ground truth label image for the FCN-2 model. Bar is 10μm.

Fig. 4
Fig. 4

Flowchart for (a) FCN-1 model, (b) FCN-2 model.

Fig. 5
Fig. 5

FCN structure used for RBCs phase image segmentation.

Fig. 6
Fig. 6

Segmentation results for the four segmentation algorithms. (a) Original RBC phase images, (b) Segmentation results using FCN-1, (c) Segmentation results using FCN-2, (d) Segmentation results using Yi et al.’s method [26], (e) Segmentation results using Yang et al.’s method [55]).

Fig. 7
Fig. 7

RBCs separation. (a) Connected RBCs region in original RBCs phase images, (b) RBCs separation results using FCN-1, (c) RBCs separation results using FCN-2, (d) RBCs separation results using Yi et al.’s method [26], (e) RBCs separation results using Yang et al.’s method [55].

Fig. 8
Fig. 8

RBCs separation evaluation results.

Tables (2)

Tables Icon

Table 1 Segmentation Accuracy on RBCs Phase Image

Tables Icon

Table 2 RBCs Separation Evaluation

Equations (3)

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y ij = f ks ( { x si+δi,sj+δj }0δi,δjk )
f ks g k s = ( fg ) k +( k1 ) s ,s s
SA( S seg , S gt )=2 | S seg S gt | | S seg |+| S gt | ,

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