Abstract

Human-induced pluripotent stem cell-derived cardiomyocytes (hiPS-CMs) beating can be efficiently characterized by time-lapse quantitative phase imaging (QPIs) obtained by digital holographic microscopy. Particularly, the CM’s nucleus section can precisely reflect the associated rhythmic beating pattern of the CM suitable for subsequent beating pattern characterization. In this paper, we describe an automated method to characterize single CMs by nucleus extraction from QPIs and subsequent beating pattern reconstruction and quantification. However, accurate CM’s nucleus extraction from the QPIs is a challenging task due to the variations in shape, size, orientation, and lack of special geometry. To this end, we propose a novel fully convolutional neural network (FCN)-based network architecture for accurate CM’s nucleus extraction using pixel classification technique and subsequent beating pattern characterization. Our experimental results show that the beating profile of multiple extracted single CMs is less noisy and more informative compared to the whole image slide. Applying this method allows CM characterization at the single-cell level. Consequently, several single CMs are extracted from the whole slide QPIs and multiple parameters regarding their beating profile of each isolated CM are efficiently measured.

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

Full Article  |  PDF Article
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References

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2020 (2)

M. Mugnano, G. Lama, R. Castaldo, V. Marchesano, F. Merola, D. Giudice, A. Calabuig, G. Gentile, V. Ambrogi, P. Cerruti, P. Memmolo, V. Pagliarulo, P. Ferraro, and S. Grilli, “Cellular Uptake of Mildly Oxidized Nanographene for Drug-Delivery Applications,” ACS Appl. Nano Mater. 3(1), 428–439 (2020).
[Crossref]

K. Jaferzadeh, B. Rappaz, F. Kuttler, B. K. Kim, I. Moon, P. Marquet, and G. Turcatti, “Marker-Free Automatic Quantification of Drug-Treated Cardiomyocytes with Digital Holographic Imaging,” ACS Photonics 7(1), 105–113 (2020).
[Crossref]

2019 (3)

K. Jaferzadeh, S. Hwang, I. Moon, and B. Javidi, “No-search focus prediction at the single cell level in digital holographic imaging with deep convolutional neural network,” Biomed. Opt. Express 10(8), 4276–4289 (2019).
[Crossref]

Y. Wu, A. Ray, Q. Wei, A. Feizi, X. Tong, E. Chen, Y. Luo, and A. Ozcan, “Deep Learning Enables High-Throughput Analysis of Particle-Aggregation-Based Biosensors Imaged Using Holography,” ACS Photonics 6(2), 294–301 (2019).
[Crossref]

M. Khened, V. Kollerathu, and G. Krishnamurthi, “Fully convolutional multi-scale residual DenseNets for cardiac segmentation and automated cardiac diagnosis using ensemble of classifiers,” Med. Image Anal. 51, 21–45 (2019).
[Crossref]

2018 (5)

S. Jastrzębski, D. Arpit, N. Ballas, V. Verma, T. Che, and Y. Bengio, “Residual Connections Encourage Iterative Inference,” ICLR 2018, 1–14 (2018).

L. Chen, P. Bentley, K. Mori, K. Misawa, M. Fujiwara, and D. Rueckert, “DRINet for Medical Image Segmentation,” IEEE Trans. Med. Imaging 37(11), 2453–2462 (2018).
[Crossref]

A. Garcia, S. Escolano, S. Oprea, V. Martinez, P. Gonzalez, and J. Rodriguez, “A survey on deep learning techniques for image and video semantic segmentation,” Appl. Soft Comput. J. 70, 41–65 (2018).
[Crossref]

M. Mugnano, P. Memmolo, L. Miccio, S. Grilli, F. Merola, A. Calabuig, A. Bramanti, E. Mazzon, and P. Ferraro, “In vitro cytotoxicity evaluation of cadmium by label-free holographic microscopy,” J. Biophotonics 11(12), e201800099 (2018).
[Crossref]

K. Jaferzadeh, I. Moon, M. Bardyn, M. Prudent, J. Tissot, B. Rappaz, B. Javidi, G. Turcatti, and P. Marquet, “Quantification of stored red blood cell fluctuations by time-lapse holographic cell imaging,” Biomed. Opt. Express 9(10), 4714–4729 (2018).
[Crossref]

2017 (3)

E. Ahmadzadeh, K. Jaferzadeh, J. Lee, and I. Moon, “Automated three-dimensional morphology-based clustering of human erythrocytes with regular shapes: stomatocytes, discocytes, and echinocytes,” J. Biomed. Opt. 22(7), 076015 (2017).
[Crossref]

A. Brüggemann, C. Haarmann, M. Rapedius, T. Goetze, I. Rinke, M. George, and N. Fertig, “Characterization of iPS Derived Cardiomyocytes in Voltage Clamp and Current Clamp by Automated Patch Clamp,” Biophys. J. 112(3), 236a (2017).
[Crossref]

G. Litjens, T. Kooi, B. Bejnordi, A. Setio, F. Ciompi, M. Ghafoorian, J. Laak, B. Ginneken, and C. Sánchez, “A survey on deep learning in medical image analysis,” Med. Image Anal. 42, 60–88 (2017).
[Crossref]

2016 (3)

I. Nitsan, S. Drori, Y. Lewis, S. Cohen, and S. Tzlil, “Mechanical communication in cardiac cell synchronized beating,” Nat. Phys. 12(5), 472–477 (2016).
[Crossref]

E. Grespan, S. Martewicz, E. Serena, V. Houerou, J. Rühe, and N. Elvassore, “Analysis of calcium transients and uniaxial contraction force in single human embryonic stem cell-derived cardiomyocytes on microstructured elastic substrate with spatially controlled surface chemistries,” Langmuir 32(46), 12190–12201 (2016).
[Crossref]

A. Totaro, F. Urciuolo, G. Imparato, and P. Netti, “Engineered cardiac micromodules for the in vitro fabrication of 3D endogenous macro-tissues,” Biofabrication 8(2), 025014 (2016).
[Crossref]

2015 (4)

N. Huebsch, P. Loskill, M. Mandegar, N. Marks, A. Sheehan, Z. Ma, A. Mathur, T. Nguyen, J. Yoo, L. Judge, C. Spencer, A. Chukka, C. Russell, P. So, B. Conklin, and K. Healy, “Automated video-based analysis of contractility and calcium flux in human-induced pluripotent stem cell-derived cardiomyocytes cultured over different spatial scales,” Tissue Eng., Part C 21(5), 467–479 (2015).
[Crossref]

D. Ossola, M. Amarouch, P. Behr, J. Vörös, H. Abriel, and T. Zambelli, “Force-controlled patch clamp of beating cardiac cells,” Nano Lett. 15(3), 1743–1750 (2015).
[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]

O. Ronneberger, P. Fischer, and T. Brox, “U-net: Convolutional networks for biomedical image segmentation,” Lect. Notes Comput. Sci. 9351, 234–241 (2015).
[Crossref]

2014 (1)

A. Ahola, A. L. Kiviaho, K. Larsson, M. Honkanen, K. Aalto-Setälä, and J. Hyttinen, “Video image-based analysis of single human induced pluripotent stem cell derived cardiomyocyte beating dynamics using digital image correlation,” Biomed. Eng. 13(1), 1–18 (2014).
[Crossref]

2012 (1)

Y. Abassi, B. Xi, N. Li, W. Ouyang, A. Seiler, M. Watzele, R. Kettenhofen, H. Bohlen, A. Ehlich, E. Kolossov, X. Wang, and X. Xu, “Dynamic monitoring of beating periodicity of stem cell-derived cardiomyocytes as a predictive tool for preclinical safety assessment,” Br. J. Pharmacol. 165(5), 1424–1441 (2012).
[Crossref]

2011 (1)

C. Bazan, D. Barba, P. Blomgren, and P. Paolini, “Image processing techniques for assessing contractility in isolated neonatal cardiac myocytes,” Int. J. Biomed. Imaging 2011, 729732 (2011).
[Crossref]

2010 (1)

2009 (1)

B. Rappaz, E. Cano, T. Colomb, J. Kühn, C. Depeursinge, V. Simanis, P. J. Magistretti, and P. Marquet, “Noninvasive characterization of the fission yeast cell cycle by monitoring dry mass with digital holographic microscopy,” J. Biomed. Opt. 14(3), 034049 (2009).
[Crossref]

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]

2005 (4)

2004 (1)

2002 (2)

1999 (1)

Aalto-Setälä, K.

A. Ahola, A. L. Kiviaho, K. Larsson, M. Honkanen, K. Aalto-Setälä, and J. Hyttinen, “Video image-based analysis of single human induced pluripotent stem cell derived cardiomyocyte beating dynamics using digital image correlation,” Biomed. Eng. 13(1), 1–18 (2014).
[Crossref]

Abassi, Y.

Y. Abassi, B. Xi, N. Li, W. Ouyang, A. Seiler, M. Watzele, R. Kettenhofen, H. Bohlen, A. Ehlich, E. Kolossov, X. Wang, and X. Xu, “Dynamic monitoring of beating periodicity of stem cell-derived cardiomyocytes as a predictive tool for preclinical safety assessment,” Br. J. Pharmacol. 165(5), 1424–1441 (2012).
[Crossref]

Abriel, H.

D. Ossola, M. Amarouch, P. Behr, J. Vörös, H. Abriel, and T. Zambelli, “Force-controlled patch clamp of beating cardiac cells,” Nano Lett. 15(3), 1743–1750 (2015).
[Crossref]

Ahmadzadeh, E.

E. Ahmadzadeh, K. Jaferzadeh, J. Lee, and I. Moon, “Automated three-dimensional morphology-based clustering of human erythrocytes with regular shapes: stomatocytes, discocytes, and echinocytes,” J. Biomed. Opt. 22(7), 076015 (2017).
[Crossref]

Ahola, A.

A. Ahola, A. L. Kiviaho, K. Larsson, M. Honkanen, K. Aalto-Setälä, and J. Hyttinen, “Video image-based analysis of single human induced pluripotent stem cell derived cardiomyocyte beating dynamics using digital image correlation,” Biomed. Eng. 13(1), 1–18 (2014).
[Crossref]

Amarouch, M.

D. Ossola, M. Amarouch, P. Behr, J. Vörös, H. Abriel, and T. Zambelli, “Force-controlled patch clamp of beating cardiac cells,” Nano Lett. 15(3), 1743–1750 (2015).
[Crossref]

Ambrogi, V.

M. Mugnano, G. Lama, R. Castaldo, V. Marchesano, F. Merola, D. Giudice, A. Calabuig, G. Gentile, V. Ambrogi, P. Cerruti, P. Memmolo, V. Pagliarulo, P. Ferraro, and S. Grilli, “Cellular Uptake of Mildly Oxidized Nanographene for Drug-Delivery Applications,” ACS Appl. Nano Mater. 3(1), 428–439 (2020).
[Crossref]

Arpit, D.

S. Jastrzębski, D. Arpit, N. Ballas, V. Verma, T. Che, and Y. Bengio, “Residual Connections Encourage Iterative Inference,” ICLR 2018, 1–14 (2018).

Ballas, N.

S. Jastrzębski, D. Arpit, N. Ballas, V. Verma, T. Che, and Y. Bengio, “Residual Connections Encourage Iterative Inference,” ICLR 2018, 1–14 (2018).

Bally, G.

Barba, D.

C. Bazan, D. Barba, P. Blomgren, and P. Paolini, “Image processing techniques for assessing contractility in isolated neonatal cardiac myocytes,” Int. J. Biomed. Imaging 2011, 729732 (2011).
[Crossref]

Bardyn, M.

Bazan, C.

C. Bazan, D. Barba, P. Blomgren, and P. Paolini, “Image processing techniques for assessing contractility in isolated neonatal cardiac myocytes,” Int. J. Biomed. Imaging 2011, 729732 (2011).
[Crossref]

Behr, P.

D. Ossola, M. Amarouch, P. Behr, J. Vörös, H. Abriel, and T. Zambelli, “Force-controlled patch clamp of beating cardiac cells,” Nano Lett. 15(3), 1743–1750 (2015).
[Crossref]

Bejnordi, B.

G. Litjens, T. Kooi, B. Bejnordi, A. Setio, F. Ciompi, M. Ghafoorian, J. Laak, B. Ginneken, and C. Sánchez, “A survey on deep learning in medical image analysis,” Med. Image Anal. 42, 60–88 (2017).
[Crossref]

Bengio, Y.

S. Jastrzębski, D. Arpit, N. Ballas, V. Verma, T. Che, and Y. Bengio, “Residual Connections Encourage Iterative Inference,” ICLR 2018, 1–14 (2018).

Bentley, P.

L. Chen, P. Bentley, K. Mori, K. Misawa, M. Fujiwara, and D. Rueckert, “DRINet for Medical Image Segmentation,” IEEE Trans. Med. Imaging 37(11), 2453–2462 (2018).
[Crossref]

Blomgren, P.

C. Bazan, D. Barba, P. Blomgren, and P. Paolini, “Image processing techniques for assessing contractility in isolated neonatal cardiac myocytes,” Int. J. Biomed. Imaging 2011, 729732 (2011).
[Crossref]

Bohlen, H.

Y. Abassi, B. Xi, N. Li, W. Ouyang, A. Seiler, M. Watzele, R. Kettenhofen, H. Bohlen, A. Ehlich, E. Kolossov, X. Wang, and X. Xu, “Dynamic monitoring of beating periodicity of stem cell-derived cardiomyocytes as a predictive tool for preclinical safety assessment,” Br. J. Pharmacol. 165(5), 1424–1441 (2012).
[Crossref]

Bramanti, A.

M. Mugnano, P. Memmolo, L. Miccio, S. Grilli, F. Merola, A. Calabuig, A. Bramanti, E. Mazzon, and P. Ferraro, “In vitro cytotoxicity evaluation of cadmium by label-free holographic microscopy,” J. Biophotonics 11(12), e201800099 (2018).
[Crossref]

Brox, T.

O. Ronneberger, P. Fischer, and T. Brox, “U-net: Convolutional networks for biomedical image segmentation,” Lect. Notes Comput. Sci. 9351, 234–241 (2015).
[Crossref]

Brüggemann, A.

A. Brüggemann, C. Haarmann, M. Rapedius, T. Goetze, I. Rinke, M. George, and N. Fertig, “Characterization of iPS Derived Cardiomyocytes in Voltage Clamp and Current Clamp by Automated Patch Clamp,” Biophys. J. 112(3), 236a (2017).
[Crossref]

Bursac, N.

Burton, D.

Calabuig, A.

M. Mugnano, G. Lama, R. Castaldo, V. Marchesano, F. Merola, D. Giudice, A. Calabuig, G. Gentile, V. Ambrogi, P. Cerruti, P. Memmolo, V. Pagliarulo, P. Ferraro, and S. Grilli, “Cellular Uptake of Mildly Oxidized Nanographene for Drug-Delivery Applications,” ACS Appl. Nano Mater. 3(1), 428–439 (2020).
[Crossref]

M. Mugnano, P. Memmolo, L. Miccio, S. Grilli, F. Merola, A. Calabuig, A. Bramanti, E. Mazzon, and P. Ferraro, “In vitro cytotoxicity evaluation of cadmium by label-free holographic microscopy,” J. Biophotonics 11(12), e201800099 (2018).
[Crossref]

Cano, E.

B. Rappaz, E. Cano, T. Colomb, J. Kühn, C. Depeursinge, V. Simanis, P. J. Magistretti, and P. Marquet, “Noninvasive characterization of the fission yeast cell cycle by monitoring dry mass with digital holographic microscopy,” J. Biomed. Opt. 14(3), 034049 (2009).
[Crossref]

Carapezza, E.

Carl, D.

Castaldo, R.

M. Mugnano, G. Lama, R. Castaldo, V. Marchesano, F. Merola, D. Giudice, A. Calabuig, G. Gentile, V. Ambrogi, P. Cerruti, P. Memmolo, V. Pagliarulo, P. Ferraro, and S. Grilli, “Cellular Uptake of Mildly Oxidized Nanographene for Drug-Delivery Applications,” ACS Appl. Nano Mater. 3(1), 428–439 (2020).
[Crossref]

Cerruti, P.

M. Mugnano, G. Lama, R. Castaldo, V. Marchesano, F. Merola, D. Giudice, A. Calabuig, G. Gentile, V. Ambrogi, P. Cerruti, P. Memmolo, V. Pagliarulo, P. Ferraro, and S. Grilli, “Cellular Uptake of Mildly Oxidized Nanographene for Drug-Delivery Applications,” ACS Appl. Nano Mater. 3(1), 428–439 (2020).
[Crossref]

Che, T.

S. Jastrzębski, D. Arpit, N. Ballas, V. Verma, T. Che, and Y. Bengio, “Residual Connections Encourage Iterative Inference,” ICLR 2018, 1–14 (2018).

Chen, E.

Y. Wu, A. Ray, Q. Wei, A. Feizi, X. Tong, E. Chen, Y. Luo, and A. Ozcan, “Deep Learning Enables High-Throughput Analysis of Particle-Aggregation-Based Biosensors Imaged Using Holography,” ACS Photonics 6(2), 294–301 (2019).
[Crossref]

Chen, L.

L. Chen, P. Bentley, K. Mori, K. Misawa, M. Fujiwara, and D. Rueckert, “DRINet for Medical Image Segmentation,” IEEE Trans. Med. Imaging 37(11), 2453–2462 (2018).
[Crossref]

Chukka, A.

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G. Litjens, T. Kooi, B. Bejnordi, A. Setio, F. Ciompi, M. Ghafoorian, J. Laak, B. Ginneken, and C. Sánchez, “A survey on deep learning in medical image analysis,” Med. Image Anal. 42, 60–88 (2017).
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I. Nitsan, S. Drori, Y. Lewis, S. Cohen, and S. Tzlil, “Mechanical communication in cardiac cell synchronized beating,” Nat. Phys. 12(5), 472–477 (2016).
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P. Marquet, B. Rappaz, P. J. Magistretti, E. Cuche, Y. Emery, T. Colomb, and C. Depeursinge, “Digital holographic microscopy: a noninvasive contrast imaging technique allowing quantitative visualization of living cells with subwavelength axial accuracy,” Opt. Lett. 30(5), 468–470 (2005).
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N. Huebsch, P. Loskill, M. Mandegar, N. Marks, A. Sheehan, Z. Ma, A. Mathur, T. Nguyen, J. Yoo, L. Judge, C. Spencer, A. Chukka, C. Russell, P. So, B. Conklin, and K. Healy, “Automated video-based analysis of contractility and calcium flux in human-induced pluripotent stem cell-derived cardiomyocytes cultured over different spatial scales,” Tissue Eng., Part C 21(5), 467–479 (2015).
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Depeursinge, C.

Drori, S.

I. Nitsan, S. Drori, Y. Lewis, S. Cohen, and S. Tzlil, “Mechanical communication in cardiac cell synchronized beating,” Nat. Phys. 12(5), 472–477 (2016).
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Y. Abassi, B. Xi, N. Li, W. Ouyang, A. Seiler, M. Watzele, R. Kettenhofen, H. Bohlen, A. Ehlich, E. Kolossov, X. Wang, and X. Xu, “Dynamic monitoring of beating periodicity of stem cell-derived cardiomyocytes as a predictive tool for preclinical safety assessment,” Br. J. Pharmacol. 165(5), 1424–1441 (2012).
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E. Grespan, S. Martewicz, E. Serena, V. Houerou, J. Rühe, and N. Elvassore, “Analysis of calcium transients and uniaxial contraction force in single human embryonic stem cell-derived cardiomyocytes on microstructured elastic substrate with spatially controlled surface chemistries,” Langmuir 32(46), 12190–12201 (2016).
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Escolano, S.

A. Garcia, S. Escolano, S. Oprea, V. Martinez, P. Gonzalez, and J. Rodriguez, “A survey on deep learning techniques for image and video semantic segmentation,” Appl. Soft Comput. J. 70, 41–65 (2018).
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Y. Wu, A. Ray, Q. Wei, A. Feizi, X. Tong, E. Chen, Y. Luo, and A. Ozcan, “Deep Learning Enables High-Throughput Analysis of Particle-Aggregation-Based Biosensors Imaged Using Holography,” ACS Photonics 6(2), 294–301 (2019).
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M. Mugnano, G. Lama, R. Castaldo, V. Marchesano, F. Merola, D. Giudice, A. Calabuig, G. Gentile, V. Ambrogi, P. Cerruti, P. Memmolo, V. Pagliarulo, P. Ferraro, and S. Grilli, “Cellular Uptake of Mildly Oxidized Nanographene for Drug-Delivery Applications,” ACS Appl. Nano Mater. 3(1), 428–439 (2020).
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M. Mugnano, P. Memmolo, L. Miccio, S. Grilli, F. Merola, A. Calabuig, A. Bramanti, E. Mazzon, and P. Ferraro, “In vitro cytotoxicity evaluation of cadmium by label-free holographic microscopy,” J. Biophotonics 11(12), e201800099 (2018).
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A. Brüggemann, C. Haarmann, M. Rapedius, T. Goetze, I. Rinke, M. George, and N. Fertig, “Characterization of iPS Derived Cardiomyocytes in Voltage Clamp and Current Clamp by Automated Patch Clamp,” Biophys. J. 112(3), 236a (2017).
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A. Garcia, S. Escolano, S. Oprea, V. Martinez, P. Gonzalez, and J. Rodriguez, “A survey on deep learning techniques for image and video semantic segmentation,” Appl. Soft Comput. J. 70, 41–65 (2018).
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Gdeisat, M.

Gentile, G.

M. Mugnano, G. Lama, R. Castaldo, V. Marchesano, F. Merola, D. Giudice, A. Calabuig, G. Gentile, V. Ambrogi, P. Cerruti, P. Memmolo, V. Pagliarulo, P. Ferraro, and S. Grilli, “Cellular Uptake of Mildly Oxidized Nanographene for Drug-Delivery Applications,” ACS Appl. Nano Mater. 3(1), 428–439 (2020).
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A. Brüggemann, C. Haarmann, M. Rapedius, T. Goetze, I. Rinke, M. George, and N. Fertig, “Characterization of iPS Derived Cardiomyocytes in Voltage Clamp and Current Clamp by Automated Patch Clamp,” Biophys. J. 112(3), 236a (2017).
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G. Litjens, T. Kooi, B. Bejnordi, A. Setio, F. Ciompi, M. Ghafoorian, J. Laak, B. Ginneken, and C. Sánchez, “A survey on deep learning in medical image analysis,” Med. Image Anal. 42, 60–88 (2017).
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G. Litjens, T. Kooi, B. Bejnordi, A. Setio, F. Ciompi, M. Ghafoorian, J. Laak, B. Ginneken, and C. Sánchez, “A survey on deep learning in medical image analysis,” Med. Image Anal. 42, 60–88 (2017).
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M. Mugnano, G. Lama, R. Castaldo, V. Marchesano, F. Merola, D. Giudice, A. Calabuig, G. Gentile, V. Ambrogi, P. Cerruti, P. Memmolo, V. Pagliarulo, P. Ferraro, and S. Grilli, “Cellular Uptake of Mildly Oxidized Nanographene for Drug-Delivery Applications,” ACS Appl. Nano Mater. 3(1), 428–439 (2020).
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A. Brüggemann, C. Haarmann, M. Rapedius, T. Goetze, I. Rinke, M. George, and N. Fertig, “Characterization of iPS Derived Cardiomyocytes in Voltage Clamp and Current Clamp by Automated Patch Clamp,” Biophys. J. 112(3), 236a (2017).
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A. Garcia, S. Escolano, S. Oprea, V. Martinez, P. Gonzalez, and J. Rodriguez, “A survey on deep learning techniques for image and video semantic segmentation,” Appl. Soft Comput. J. 70, 41–65 (2018).
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E. Grespan, S. Martewicz, E. Serena, V. Houerou, J. Rühe, and N. Elvassore, “Analysis of calcium transients and uniaxial contraction force in single human embryonic stem cell-derived cardiomyocytes on microstructured elastic substrate with spatially controlled surface chemistries,” Langmuir 32(46), 12190–12201 (2016).
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Grilli, S.

M. Mugnano, G. Lama, R. Castaldo, V. Marchesano, F. Merola, D. Giudice, A. Calabuig, G. Gentile, V. Ambrogi, P. Cerruti, P. Memmolo, V. Pagliarulo, P. Ferraro, and S. Grilli, “Cellular Uptake of Mildly Oxidized Nanographene for Drug-Delivery Applications,” ACS Appl. Nano Mater. 3(1), 428–439 (2020).
[Crossref]

M. Mugnano, P. Memmolo, L. Miccio, S. Grilli, F. Merola, A. Calabuig, A. Bramanti, E. Mazzon, and P. Ferraro, “In vitro cytotoxicity evaluation of cadmium by label-free holographic microscopy,” J. Biophotonics 11(12), e201800099 (2018).
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A. Brüggemann, C. Haarmann, M. Rapedius, T. Goetze, I. Rinke, M. George, and N. Fertig, “Characterization of iPS Derived Cardiomyocytes in Voltage Clamp and Current Clamp by Automated Patch Clamp,” Biophys. J. 112(3), 236a (2017).
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N. Huebsch, P. Loskill, M. Mandegar, N. Marks, A. Sheehan, Z. Ma, A. Mathur, T. Nguyen, J. Yoo, L. Judge, C. Spencer, A. Chukka, C. Russell, P. So, B. Conklin, and K. Healy, “Automated video-based analysis of contractility and calcium flux in human-induced pluripotent stem cell-derived cardiomyocytes cultured over different spatial scales,” Tissue Eng., Part C 21(5), 467–479 (2015).
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Honkanen, M.

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E. Grespan, S. Martewicz, E. Serena, V. Houerou, J. Rühe, and N. Elvassore, “Analysis of calcium transients and uniaxial contraction force in single human embryonic stem cell-derived cardiomyocytes on microstructured elastic substrate with spatially controlled surface chemistries,” Langmuir 32(46), 12190–12201 (2016).
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N. Huebsch, P. Loskill, M. Mandegar, N. Marks, A. Sheehan, Z. Ma, A. Mathur, T. Nguyen, J. Yoo, L. Judge, C. Spencer, A. Chukka, C. Russell, P. So, B. Conklin, and K. Healy, “Automated video-based analysis of contractility and calcium flux in human-induced pluripotent stem cell-derived cardiomyocytes cultured over different spatial scales,” Tissue Eng., Part C 21(5), 467–479 (2015).
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Hyttinen, J.

A. Ahola, A. L. Kiviaho, K. Larsson, M. Honkanen, K. Aalto-Setälä, and J. Hyttinen, “Video image-based analysis of single human induced pluripotent stem cell derived cardiomyocyte beating dynamics using digital image correlation,” Biomed. Eng. 13(1), 1–18 (2014).
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K. Jaferzadeh, B. Rappaz, F. Kuttler, B. K. Kim, I. Moon, P. Marquet, and G. Turcatti, “Marker-Free Automatic Quantification of Drug-Treated Cardiomyocytes with Digital Holographic Imaging,” ACS Photonics 7(1), 105–113 (2020).
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K. Jaferzadeh, S. Hwang, I. Moon, and B. Javidi, “No-search focus prediction at the single cell level in digital holographic imaging with deep convolutional neural network,” Biomed. Opt. Express 10(8), 4276–4289 (2019).
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K. Jaferzadeh, I. Moon, M. Bardyn, M. Prudent, J. Tissot, B. Rappaz, B. Javidi, G. Turcatti, and P. Marquet, “Quantification of stored red blood cell fluctuations by time-lapse holographic cell imaging,” Biomed. Opt. Express 9(10), 4714–4729 (2018).
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E. Ahmadzadeh, K. Jaferzadeh, J. Lee, and I. Moon, “Automated three-dimensional morphology-based clustering of human erythrocytes with regular shapes: stomatocytes, discocytes, and echinocytes,” J. Biomed. Opt. 22(7), 076015 (2017).
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Javidi, B.

Judge, L.

N. Huebsch, P. Loskill, M. Mandegar, N. Marks, A. Sheehan, Z. Ma, A. Mathur, T. Nguyen, J. Yoo, L. Judge, C. Spencer, A. Chukka, C. Russell, P. So, B. Conklin, and K. Healy, “Automated video-based analysis of contractility and calcium flux in human-induced pluripotent stem cell-derived cardiomyocytes cultured over different spatial scales,” Tissue Eng., Part C 21(5), 467–479 (2015).
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Kettenhofen, R.

Y. Abassi, B. Xi, N. Li, W. Ouyang, A. Seiler, M. Watzele, R. Kettenhofen, H. Bohlen, A. Ehlich, E. Kolossov, X. Wang, and X. Xu, “Dynamic monitoring of beating periodicity of stem cell-derived cardiomyocytes as a predictive tool for preclinical safety assessment,” Br. J. Pharmacol. 165(5), 1424–1441 (2012).
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M. Khened, V. Kollerathu, and G. Krishnamurthi, “Fully convolutional multi-scale residual DenseNets for cardiac segmentation and automated cardiac diagnosis using ensemble of classifiers,” Med. Image Anal. 51, 21–45 (2019).
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Kim, B. K.

K. Jaferzadeh, B. Rappaz, F. Kuttler, B. K. Kim, I. Moon, P. Marquet, and G. Turcatti, “Marker-Free Automatic Quantification of Drug-Treated Cardiomyocytes with Digital Holographic Imaging,” ACS Photonics 7(1), 105–113 (2020).
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Kiviaho, A. L.

A. Ahola, A. L. Kiviaho, K. Larsson, M. Honkanen, K. Aalto-Setälä, and J. Hyttinen, “Video image-based analysis of single human induced pluripotent stem cell derived cardiomyocyte beating dynamics using digital image correlation,” Biomed. Eng. 13(1), 1–18 (2014).
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Kollerathu, V.

M. Khened, V. Kollerathu, and G. Krishnamurthi, “Fully convolutional multi-scale residual DenseNets for cardiac segmentation and automated cardiac diagnosis using ensemble of classifiers,” Med. Image Anal. 51, 21–45 (2019).
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Kolossov, E.

Y. Abassi, B. Xi, N. Li, W. Ouyang, A. Seiler, M. Watzele, R. Kettenhofen, H. Bohlen, A. Ehlich, E. Kolossov, X. Wang, and X. Xu, “Dynamic monitoring of beating periodicity of stem cell-derived cardiomyocytes as a predictive tool for preclinical safety assessment,” Br. J. Pharmacol. 165(5), 1424–1441 (2012).
[Crossref]

Kooi, T.

G. Litjens, T. Kooi, B. Bejnordi, A. Setio, F. Ciompi, M. Ghafoorian, J. Laak, B. Ginneken, and C. Sánchez, “A survey on deep learning in medical image analysis,” Med. Image Anal. 42, 60–88 (2017).
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M. Khened, V. Kollerathu, and G. Krishnamurthi, “Fully convolutional multi-scale residual DenseNets for cardiac segmentation and automated cardiac diagnosis using ensemble of classifiers,” Med. Image Anal. 51, 21–45 (2019).
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B. Rappaz, E. Cano, T. Colomb, J. Kühn, C. Depeursinge, V. Simanis, P. J. Magistretti, and P. Marquet, “Noninvasive characterization of the fission yeast cell cycle by monitoring dry mass with digital holographic microscopy,” J. Biomed. Opt. 14(3), 034049 (2009).
[Crossref]

Kuttler, F.

K. Jaferzadeh, B. Rappaz, F. Kuttler, B. K. Kim, I. Moon, P. Marquet, and G. Turcatti, “Marker-Free Automatic Quantification of Drug-Treated Cardiomyocytes with Digital Holographic Imaging,” ACS Photonics 7(1), 105–113 (2020).
[Crossref]

Laak, J.

G. Litjens, T. Kooi, B. Bejnordi, A. Setio, F. Ciompi, M. Ghafoorian, J. Laak, B. Ginneken, and C. Sánchez, “A survey on deep learning in medical image analysis,” Med. Image Anal. 42, 60–88 (2017).
[Crossref]

Lalor, M.

Lama, G.

M. Mugnano, G. Lama, R. Castaldo, V. Marchesano, F. Merola, D. Giudice, A. Calabuig, G. Gentile, V. Ambrogi, P. Cerruti, P. Memmolo, V. Pagliarulo, P. Ferraro, and S. Grilli, “Cellular Uptake of Mildly Oxidized Nanographene for Drug-Delivery Applications,” ACS Appl. Nano Mater. 3(1), 428–439 (2020).
[Crossref]

Larsson, K.

A. Ahola, A. L. Kiviaho, K. Larsson, M. Honkanen, K. Aalto-Setälä, and J. Hyttinen, “Video image-based analysis of single human induced pluripotent stem cell derived cardiomyocyte beating dynamics using digital image correlation,” Biomed. Eng. 13(1), 1–18 (2014).
[Crossref]

Lee, J.

E. Ahmadzadeh, K. Jaferzadeh, J. Lee, and I. Moon, “Automated three-dimensional morphology-based clustering of human erythrocytes with regular shapes: stomatocytes, discocytes, and echinocytes,” J. Biomed. Opt. 22(7), 076015 (2017).
[Crossref]

Lewis, Y.

I. Nitsan, S. Drori, Y. Lewis, S. Cohen, and S. Tzlil, “Mechanical communication in cardiac cell synchronized beating,” Nat. Phys. 12(5), 472–477 (2016).
[Crossref]

Li, N.

Y. Abassi, B. Xi, N. Li, W. Ouyang, A. Seiler, M. Watzele, R. Kettenhofen, H. Bohlen, A. Ehlich, E. Kolossov, X. Wang, and X. Xu, “Dynamic monitoring of beating periodicity of stem cell-derived cardiomyocytes as a predictive tool for preclinical safety assessment,” Br. J. Pharmacol. 165(5), 1424–1441 (2012).
[Crossref]

Litjens, G.

G. Litjens, T. Kooi, B. Bejnordi, A. Setio, F. Ciompi, M. Ghafoorian, J. Laak, B. Ginneken, and C. Sánchez, “A survey on deep learning in medical image analysis,” Med. Image Anal. 42, 60–88 (2017).
[Crossref]

Liu, Z.

G. Huang, Z. Liu, L. Maaten, and K. Weinberger, “Densely connected convolutional networks,” IEEE Conf. Comput. Vis. Pattern Recognition, CVPR 2017, 2261–2269 (2017).

Loskill, P.

N. Huebsch, P. Loskill, M. Mandegar, N. Marks, A. Sheehan, Z. Ma, A. Mathur, T. Nguyen, J. Yoo, L. Judge, C. Spencer, A. Chukka, C. Russell, P. So, B. Conklin, and K. Healy, “Automated video-based analysis of contractility and calcium flux in human-induced pluripotent stem cell-derived cardiomyocytes cultured over different spatial scales,” Tissue Eng., Part C 21(5), 467–479 (2015).
[Crossref]

Luo, Y.

Y. Wu, A. Ray, Q. Wei, A. Feizi, X. Tong, E. Chen, Y. Luo, and A. Ozcan, “Deep Learning Enables High-Throughput Analysis of Particle-Aggregation-Based Biosensors Imaged Using Holography,” ACS Photonics 6(2), 294–301 (2019).
[Crossref]

Ma, Z.

N. Huebsch, P. Loskill, M. Mandegar, N. Marks, A. Sheehan, Z. Ma, A. Mathur, T. Nguyen, J. Yoo, L. Judge, C. Spencer, A. Chukka, C. Russell, P. So, B. Conklin, and K. Healy, “Automated video-based analysis of contractility and calcium flux in human-induced pluripotent stem cell-derived cardiomyocytes cultured over different spatial scales,” Tissue Eng., Part C 21(5), 467–479 (2015).
[Crossref]

Maaten, L.

G. Huang, Z. Liu, L. Maaten, and K. Weinberger, “Densely connected convolutional networks,” IEEE Conf. Comput. Vis. Pattern Recognition, CVPR 2017, 2261–2269 (2017).

Magistretti, P.

Magistretti, P. J.

B. Rappaz, E. Cano, T. Colomb, J. Kühn, C. Depeursinge, V. Simanis, P. J. Magistretti, and P. Marquet, “Noninvasive characterization of the fission yeast cell cycle by monitoring dry mass with digital holographic microscopy,” J. Biomed. Opt. 14(3), 034049 (2009).
[Crossref]

P. Marquet, B. Rappaz, P. J. Magistretti, E. Cuche, Y. Emery, T. Colomb, and C. Depeursinge, “Digital holographic microscopy: a noninvasive contrast imaging technique allowing quantitative visualization of living cells with subwavelength axial accuracy,” Opt. Lett. 30(5), 468–470 (2005).
[Crossref]

Mandegar, M.

N. Huebsch, P. Loskill, M. Mandegar, N. Marks, A. Sheehan, Z. Ma, A. Mathur, T. Nguyen, J. Yoo, L. Judge, C. Spencer, A. Chukka, C. Russell, P. So, B. Conklin, and K. Healy, “Automated video-based analysis of contractility and calcium flux in human-induced pluripotent stem cell-derived cardiomyocytes cultured over different spatial scales,” Tissue Eng., Part C 21(5), 467–479 (2015).
[Crossref]

Marchesano, V.

M. Mugnano, G. Lama, R. Castaldo, V. Marchesano, F. Merola, D. Giudice, A. Calabuig, G. Gentile, V. Ambrogi, P. Cerruti, P. Memmolo, V. Pagliarulo, P. Ferraro, and S. Grilli, “Cellular Uptake of Mildly Oxidized Nanographene for Drug-Delivery Applications,” ACS Appl. Nano Mater. 3(1), 428–439 (2020).
[Crossref]

Marks, N.

N. Huebsch, P. Loskill, M. Mandegar, N. Marks, A. Sheehan, Z. Ma, A. Mathur, T. Nguyen, J. Yoo, L. Judge, C. Spencer, A. Chukka, C. Russell, P. So, B. Conklin, and K. Healy, “Automated video-based analysis of contractility and calcium flux in human-induced pluripotent stem cell-derived cardiomyocytes cultured over different spatial scales,” Tissue Eng., Part C 21(5), 467–479 (2015).
[Crossref]

Marquet, P.

K. Jaferzadeh, B. Rappaz, F. Kuttler, B. K. Kim, I. Moon, P. Marquet, and G. Turcatti, “Marker-Free Automatic Quantification of Drug-Treated Cardiomyocytes with Digital Holographic Imaging,” ACS Photonics 7(1), 105–113 (2020).
[Crossref]

K. Jaferzadeh, I. Moon, M. Bardyn, M. Prudent, J. Tissot, B. Rappaz, B. Javidi, G. Turcatti, and P. Marquet, “Quantification of stored red blood cell fluctuations by time-lapse holographic cell imaging,” Biomed. Opt. Express 9(10), 4714–4729 (2018).
[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]

B. Rappaz, E. Cano, T. Colomb, J. Kühn, C. Depeursinge, V. Simanis, P. J. Magistretti, and P. Marquet, “Noninvasive characterization of the fission yeast cell cycle by monitoring dry mass with digital holographic microscopy,” J. Biomed. Opt. 14(3), 034049 (2009).
[Crossref]

P. Marquet, B. Rappaz, P. J. Magistretti, E. Cuche, Y. Emery, T. Colomb, and C. Depeursinge, “Digital holographic microscopy: a noninvasive contrast imaging technique allowing quantitative visualization of living cells with subwavelength axial accuracy,” Opt. Lett. 30(5), 468–470 (2005).
[Crossref]

B. Rappaz, P. Marquet, E. Cuche, Y. Emery, C. Depeursinge, and P. Magistretti, “Measurement of the integral refractive index and dynamic cell morphometry of living cells with digital holographic microscopy,” Opt. Express 13(23), 9361–9373 (2005).
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E. Cuche, P. Marquet, and C. Depeursinge, “Simultaneous amplitude-contrast and quantitative phase-contrast microscopy by numerical reconstruction of Fresnel off-axis holograms,” Appl. Opt. 38(34), 6994–7001 (1999).
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Martewicz, S.

E. Grespan, S. Martewicz, E. Serena, V. Houerou, J. Rühe, and N. Elvassore, “Analysis of calcium transients and uniaxial contraction force in single human embryonic stem cell-derived cardiomyocytes on microstructured elastic substrate with spatially controlled surface chemistries,” Langmuir 32(46), 12190–12201 (2016).
[Crossref]

Martinez, V.

A. Garcia, S. Escolano, S. Oprea, V. Martinez, P. Gonzalez, and J. Rodriguez, “A survey on deep learning techniques for image and video semantic segmentation,” Appl. Soft Comput. J. 70, 41–65 (2018).
[Crossref]

Mathur, A.

N. Huebsch, P. Loskill, M. Mandegar, N. Marks, A. Sheehan, Z. Ma, A. Mathur, T. Nguyen, J. Yoo, L. Judge, C. Spencer, A. Chukka, C. Russell, P. So, B. Conklin, and K. Healy, “Automated video-based analysis of contractility and calcium flux in human-induced pluripotent stem cell-derived cardiomyocytes cultured over different spatial scales,” Tissue Eng., Part C 21(5), 467–479 (2015).
[Crossref]

Mazzon, E.

M. Mugnano, P. Memmolo, L. Miccio, S. Grilli, F. Merola, A. Calabuig, A. Bramanti, E. Mazzon, and P. Ferraro, “In vitro cytotoxicity evaluation of cadmium by label-free holographic microscopy,” J. Biophotonics 11(12), e201800099 (2018).
[Crossref]

Memmolo, P.

M. Mugnano, G. Lama, R. Castaldo, V. Marchesano, F. Merola, D. Giudice, A. Calabuig, G. Gentile, V. Ambrogi, P. Cerruti, P. Memmolo, V. Pagliarulo, P. Ferraro, and S. Grilli, “Cellular Uptake of Mildly Oxidized Nanographene for Drug-Delivery Applications,” ACS Appl. Nano Mater. 3(1), 428–439 (2020).
[Crossref]

M. Mugnano, P. Memmolo, L. Miccio, S. Grilli, F. Merola, A. Calabuig, A. Bramanti, E. Mazzon, and P. Ferraro, “In vitro cytotoxicity evaluation of cadmium by label-free holographic microscopy,” J. Biophotonics 11(12), e201800099 (2018).
[Crossref]

Merola, F.

M. Mugnano, G. Lama, R. Castaldo, V. Marchesano, F. Merola, D. Giudice, A. Calabuig, G. Gentile, V. Ambrogi, P. Cerruti, P. Memmolo, V. Pagliarulo, P. Ferraro, and S. Grilli, “Cellular Uptake of Mildly Oxidized Nanographene for Drug-Delivery Applications,” ACS Appl. Nano Mater. 3(1), 428–439 (2020).
[Crossref]

M. Mugnano, P. Memmolo, L. Miccio, S. Grilli, F. Merola, A. Calabuig, A. Bramanti, E. Mazzon, and P. Ferraro, “In vitro cytotoxicity evaluation of cadmium by label-free holographic microscopy,” J. Biophotonics 11(12), e201800099 (2018).
[Crossref]

Miccio, L.

M. Mugnano, P. Memmolo, L. Miccio, S. Grilli, F. Merola, A. Calabuig, A. Bramanti, E. Mazzon, and P. Ferraro, “In vitro cytotoxicity evaluation of cadmium by label-free holographic microscopy,” J. Biophotonics 11(12), e201800099 (2018).
[Crossref]

Misawa, K.

L. Chen, P. Bentley, K. Mori, K. Misawa, M. Fujiwara, and D. Rueckert, “DRINet for Medical Image Segmentation,” IEEE Trans. Med. Imaging 37(11), 2453–2462 (2018).
[Crossref]

Moon, I.

K. Jaferzadeh, B. Rappaz, F. Kuttler, B. K. Kim, I. Moon, P. Marquet, and G. Turcatti, “Marker-Free Automatic Quantification of Drug-Treated Cardiomyocytes with Digital Holographic Imaging,” ACS Photonics 7(1), 105–113 (2020).
[Crossref]

K. Jaferzadeh, S. Hwang, I. Moon, and B. Javidi, “No-search focus prediction at the single cell level in digital holographic imaging with deep convolutional neural network,” Biomed. Opt. Express 10(8), 4276–4289 (2019).
[Crossref]

K. Jaferzadeh, I. Moon, M. Bardyn, M. Prudent, J. Tissot, B. Rappaz, B. Javidi, G. Turcatti, and P. Marquet, “Quantification of stored red blood cell fluctuations by time-lapse holographic cell imaging,” Biomed. Opt. Express 9(10), 4714–4729 (2018).
[Crossref]

E. Ahmadzadeh, K. Jaferzadeh, J. Lee, and I. Moon, “Automated three-dimensional morphology-based clustering of human erythrocytes with regular shapes: stomatocytes, discocytes, and echinocytes,” J. Biomed. Opt. 22(7), 076015 (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]

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]

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]

Mori, K.

L. Chen, P. Bentley, K. Mori, K. Misawa, M. Fujiwara, and D. Rueckert, “DRINet for Medical Image Segmentation,” IEEE Trans. Med. Imaging 37(11), 2453–2462 (2018).
[Crossref]

Mugnano, M.

M. Mugnano, G. Lama, R. Castaldo, V. Marchesano, F. Merola, D. Giudice, A. Calabuig, G. Gentile, V. Ambrogi, P. Cerruti, P. Memmolo, V. Pagliarulo, P. Ferraro, and S. Grilli, “Cellular Uptake of Mildly Oxidized Nanographene for Drug-Delivery Applications,” ACS Appl. Nano Mater. 3(1), 428–439 (2020).
[Crossref]

M. Mugnano, P. Memmolo, L. Miccio, S. Grilli, F. Merola, A. Calabuig, A. Bramanti, E. Mazzon, and P. Ferraro, “In vitro cytotoxicity evaluation of cadmium by label-free holographic microscopy,” J. Biophotonics 11(12), e201800099 (2018).
[Crossref]

Netti, P.

A. Totaro, F. Urciuolo, G. Imparato, and P. Netti, “Engineered cardiac micromodules for the in vitro fabrication of 3D endogenous macro-tissues,” Biofabrication 8(2), 025014 (2016).
[Crossref]

Nguyen, T.

N. Huebsch, P. Loskill, M. Mandegar, N. Marks, A. Sheehan, Z. Ma, A. Mathur, T. Nguyen, J. Yoo, L. Judge, C. Spencer, A. Chukka, C. Russell, P. So, B. Conklin, and K. Healy, “Automated video-based analysis of contractility and calcium flux in human-induced pluripotent stem cell-derived cardiomyocytes cultured over different spatial scales,” Tissue Eng., Part C 21(5), 467–479 (2015).
[Crossref]

Nitsan, I.

I. Nitsan, S. Drori, Y. Lewis, S. Cohen, and S. Tzlil, “Mechanical communication in cardiac cell synchronized beating,” Nat. Phys. 12(5), 472–477 (2016).
[Crossref]

Oprea, S.

A. Garcia, S. Escolano, S. Oprea, V. Martinez, P. Gonzalez, and J. Rodriguez, “A survey on deep learning techniques for image and video semantic segmentation,” Appl. Soft Comput. J. 70, 41–65 (2018).
[Crossref]

Ossola, D.

D. Ossola, M. Amarouch, P. Behr, J. Vörös, H. Abriel, and T. Zambelli, “Force-controlled patch clamp of beating cardiac cells,” Nano Lett. 15(3), 1743–1750 (2015).
[Crossref]

Ouyang, W.

Y. Abassi, B. Xi, N. Li, W. Ouyang, A. Seiler, M. Watzele, R. Kettenhofen, H. Bohlen, A. Ehlich, E. Kolossov, X. Wang, and X. Xu, “Dynamic monitoring of beating periodicity of stem cell-derived cardiomyocytes as a predictive tool for preclinical safety assessment,” Br. J. Pharmacol. 165(5), 1424–1441 (2012).
[Crossref]

Ozcan, A.

Y. Wu, A. Ray, Q. Wei, A. Feizi, X. Tong, E. Chen, Y. Luo, and A. Ozcan, “Deep Learning Enables High-Throughput Analysis of Particle-Aggregation-Based Biosensors Imaged Using Holography,” ACS Photonics 6(2), 294–301 (2019).
[Crossref]

Pagliarulo, V.

M. Mugnano, G. Lama, R. Castaldo, V. Marchesano, F. Merola, D. Giudice, A. Calabuig, G. Gentile, V. Ambrogi, P. Cerruti, P. Memmolo, V. Pagliarulo, P. Ferraro, and S. Grilli, “Cellular Uptake of Mildly Oxidized Nanographene for Drug-Delivery Applications,” ACS Appl. Nano Mater. 3(1), 428–439 (2020).
[Crossref]

Paolini, P.

C. Bazan, D. Barba, P. Blomgren, and P. Paolini, “Image processing techniques for assessing contractility in isolated neonatal cardiac myocytes,” Int. J. Biomed. Imaging 2011, 729732 (2011).
[Crossref]

Prudent, M.

Rapedius, M.

A. Brüggemann, C. Haarmann, M. Rapedius, T. Goetze, I. Rinke, M. George, and N. Fertig, “Characterization of iPS Derived Cardiomyocytes in Voltage Clamp and Current Clamp by Automated Patch Clamp,” Biophys. J. 112(3), 236a (2017).
[Crossref]

Rappaz, B.

K. Jaferzadeh, B. Rappaz, F. Kuttler, B. K. Kim, I. Moon, P. Marquet, and G. Turcatti, “Marker-Free Automatic Quantification of Drug-Treated Cardiomyocytes with Digital Holographic Imaging,” ACS Photonics 7(1), 105–113 (2020).
[Crossref]

K. Jaferzadeh, I. Moon, M. Bardyn, M. Prudent, J. Tissot, B. Rappaz, B. Javidi, G. Turcatti, and P. Marquet, “Quantification of stored red blood cell fluctuations by time-lapse holographic cell imaging,” Biomed. Opt. Express 9(10), 4714–4729 (2018).
[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]

B. Rappaz, E. Cano, T. Colomb, J. Kühn, C. Depeursinge, V. Simanis, P. J. Magistretti, and P. Marquet, “Noninvasive characterization of the fission yeast cell cycle by monitoring dry mass with digital holographic microscopy,” J. Biomed. Opt. 14(3), 034049 (2009).
[Crossref]

B. Rappaz, P. Marquet, E. Cuche, Y. Emery, C. Depeursinge, and P. Magistretti, “Measurement of the integral refractive index and dynamic cell morphometry of living cells with digital holographic microscopy,” Opt. Express 13(23), 9361–9373 (2005).
[Crossref]

P. Marquet, B. Rappaz, P. J. Magistretti, E. Cuche, Y. Emery, T. Colomb, and C. Depeursinge, “Digital holographic microscopy: a noninvasive contrast imaging technique allowing quantitative visualization of living cells with subwavelength axial accuracy,” Opt. Lett. 30(5), 468–470 (2005).
[Crossref]

Ray, A.

Y. Wu, A. Ray, Q. Wei, A. Feizi, X. Tong, E. Chen, Y. Luo, and A. Ozcan, “Deep Learning Enables High-Throughput Analysis of Particle-Aggregation-Based Biosensors Imaged Using Holography,” ACS Photonics 6(2), 294–301 (2019).
[Crossref]

Rinke, I.

A. Brüggemann, C. Haarmann, M. Rapedius, T. Goetze, I. Rinke, M. George, and N. Fertig, “Characterization of iPS Derived Cardiomyocytes in Voltage Clamp and Current Clamp by Automated Patch Clamp,” Biophys. J. 112(3), 236a (2017).
[Crossref]

Rodriguez, J.

A. Garcia, S. Escolano, S. Oprea, V. Martinez, P. Gonzalez, and J. Rodriguez, “A survey on deep learning techniques for image and video semantic segmentation,” Appl. Soft Comput. J. 70, 41–65 (2018).
[Crossref]

Ronneberger, O.

O. Ronneberger, P. Fischer, and T. Brox, “U-net: Convolutional networks for biomedical image segmentation,” Lect. Notes Comput. Sci. 9351, 234–241 (2015).
[Crossref]

Rueckert, D.

L. Chen, P. Bentley, K. Mori, K. Misawa, M. Fujiwara, and D. Rueckert, “DRINet for Medical Image Segmentation,” IEEE Trans. Med. Imaging 37(11), 2453–2462 (2018).
[Crossref]

Rühe, J.

E. Grespan, S. Martewicz, E. Serena, V. Houerou, J. Rühe, and N. Elvassore, “Analysis of calcium transients and uniaxial contraction force in single human embryonic stem cell-derived cardiomyocytes on microstructured elastic substrate with spatially controlled surface chemistries,” Langmuir 32(46), 12190–12201 (2016).
[Crossref]

Russell, C.

N. Huebsch, P. Loskill, M. Mandegar, N. Marks, A. Sheehan, Z. Ma, A. Mathur, T. Nguyen, J. Yoo, L. Judge, C. Spencer, A. Chukka, C. Russell, P. So, B. Conklin, and K. Healy, “Automated video-based analysis of contractility and calcium flux in human-induced pluripotent stem cell-derived cardiomyocytes cultured over different spatial scales,” Tissue Eng., Part C 21(5), 467–479 (2015).
[Crossref]

Sánchez, C.

G. Litjens, T. Kooi, B. Bejnordi, A. Setio, F. Ciompi, M. Ghafoorian, J. Laak, B. Ginneken, and C. Sánchez, “A survey on deep learning in medical image analysis,” Med. Image Anal. 42, 60–88 (2017).
[Crossref]

Satterwhite, L.

Schnars, U.

U. Schnars and W. Juptner, “Digital recording and numerical reconstruction of holograms,” Meas. Sci. Technol. 13(9), R85–R101 (2002).
[Crossref]

Seiler, A.

Y. Abassi, B. Xi, N. Li, W. Ouyang, A. Seiler, M. Watzele, R. Kettenhofen, H. Bohlen, A. Ehlich, E. Kolossov, X. Wang, and X. Xu, “Dynamic monitoring of beating periodicity of stem cell-derived cardiomyocytes as a predictive tool for preclinical safety assessment,” Br. J. Pharmacol. 165(5), 1424–1441 (2012).
[Crossref]

Serena, E.

E. Grespan, S. Martewicz, E. Serena, V. Houerou, J. Rühe, and N. Elvassore, “Analysis of calcium transients and uniaxial contraction force in single human embryonic stem cell-derived cardiomyocytes on microstructured elastic substrate with spatially controlled surface chemistries,” Langmuir 32(46), 12190–12201 (2016).
[Crossref]

Setio, A.

G. Litjens, T. Kooi, B. Bejnordi, A. Setio, F. Ciompi, M. Ghafoorian, J. Laak, B. Ginneken, and C. Sánchez, “A survey on deep learning in medical image analysis,” Med. Image Anal. 42, 60–88 (2017).
[Crossref]

Shaked, N.

Sheehan, A.

N. Huebsch, P. Loskill, M. Mandegar, N. Marks, A. Sheehan, Z. Ma, A. Mathur, T. Nguyen, J. Yoo, L. Judge, C. Spencer, A. Chukka, C. Russell, P. So, B. Conklin, and K. Healy, “Automated video-based analysis of contractility and calcium flux in human-induced pluripotent stem cell-derived cardiomyocytes cultured over different spatial scales,” Tissue Eng., Part C 21(5), 467–479 (2015).
[Crossref]

Simanis, V.

B. Rappaz, E. Cano, T. Colomb, J. Kühn, C. Depeursinge, V. Simanis, P. J. Magistretti, and P. Marquet, “Noninvasive characterization of the fission yeast cell cycle by monitoring dry mass with digital holographic microscopy,” J. Biomed. Opt. 14(3), 034049 (2009).
[Crossref]

So, P.

N. Huebsch, P. Loskill, M. Mandegar, N. Marks, A. Sheehan, Z. Ma, A. Mathur, T. Nguyen, J. Yoo, L. Judge, C. Spencer, A. Chukka, C. Russell, P. So, B. Conklin, and K. Healy, “Automated video-based analysis of contractility and calcium flux in human-induced pluripotent stem cell-derived cardiomyocytes cultured over different spatial scales,” Tissue Eng., Part C 21(5), 467–479 (2015).
[Crossref]

Spencer, C.

N. Huebsch, P. Loskill, M. Mandegar, N. Marks, A. Sheehan, Z. Ma, A. Mathur, T. Nguyen, J. Yoo, L. Judge, C. Spencer, A. Chukka, C. Russell, P. So, B. Conklin, and K. Healy, “Automated video-based analysis of contractility and calcium flux in human-induced pluripotent stem cell-derived cardiomyocytes cultured over different spatial scales,” Tissue Eng., Part C 21(5), 467–479 (2015).
[Crossref]

Tissot, J.

Tong, X.

Y. Wu, A. Ray, Q. Wei, A. Feizi, X. Tong, E. Chen, Y. Luo, and A. Ozcan, “Deep Learning Enables High-Throughput Analysis of Particle-Aggregation-Based Biosensors Imaged Using Holography,” ACS Photonics 6(2), 294–301 (2019).
[Crossref]

Totaro, A.

A. Totaro, F. Urciuolo, G. Imparato, and P. Netti, “Engineered cardiac micromodules for the in vitro fabrication of 3D endogenous macro-tissues,” Biofabrication 8(2), 025014 (2016).
[Crossref]

Turcatti, G.

Tzlil, S.

I. Nitsan, S. Drori, Y. Lewis, S. Cohen, and S. Tzlil, “Mechanical communication in cardiac cell synchronized beating,” Nat. Phys. 12(5), 472–477 (2016).
[Crossref]

Urciuolo, F.

A. Totaro, F. Urciuolo, G. Imparato, and P. Netti, “Engineered cardiac micromodules for the in vitro fabrication of 3D endogenous macro-tissues,” Biofabrication 8(2), 025014 (2016).
[Crossref]

Verma, V.

S. Jastrzębski, D. Arpit, N. Ballas, V. Verma, T. Che, and Y. Bengio, “Residual Connections Encourage Iterative Inference,” ICLR 2018, 1–14 (2018).

Vörös, J.

D. Ossola, M. Amarouch, P. Behr, J. Vörös, H. Abriel, and T. Zambelli, “Force-controlled patch clamp of beating cardiac cells,” Nano Lett. 15(3), 1743–1750 (2015).
[Crossref]

Wang, X.

Y. Abassi, B. Xi, N. Li, W. Ouyang, A. Seiler, M. Watzele, R. Kettenhofen, H. Bohlen, A. Ehlich, E. Kolossov, X. Wang, and X. Xu, “Dynamic monitoring of beating periodicity of stem cell-derived cardiomyocytes as a predictive tool for preclinical safety assessment,” Br. J. Pharmacol. 165(5), 1424–1441 (2012).
[Crossref]

Watzele, M.

Y. Abassi, B. Xi, N. Li, W. Ouyang, A. Seiler, M. Watzele, R. Kettenhofen, H. Bohlen, A. Ehlich, E. Kolossov, X. Wang, and X. Xu, “Dynamic monitoring of beating periodicity of stem cell-derived cardiomyocytes as a predictive tool for preclinical safety assessment,” Br. J. Pharmacol. 165(5), 1424–1441 (2012).
[Crossref]

Wax, A.

Wei, Q.

Y. Wu, A. Ray, Q. Wei, A. Feizi, X. Tong, E. Chen, Y. Luo, and A. Ozcan, “Deep Learning Enables High-Throughput Analysis of Particle-Aggregation-Based Biosensors Imaged Using Holography,” ACS Photonics 6(2), 294–301 (2019).
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Weinberger, K.

G. Huang, Z. Liu, L. Maaten, and K. Weinberger, “Densely connected convolutional networks,” IEEE Conf. Comput. Vis. Pattern Recognition, CVPR 2017, 2261–2269 (2017).

Wernicke, G.

Wheeler-Jones, C.

C. Wheeler-Jones, “Cell signaling in the cardiovascular system: An overview,” Heart 91(10), 1366–1374 (2005).
[Crossref]

Wu, Y.

Y. Wu, A. Ray, Q. Wei, A. Feizi, X. Tong, E. Chen, Y. Luo, and A. Ozcan, “Deep Learning Enables High-Throughput Analysis of Particle-Aggregation-Based Biosensors Imaged Using Holography,” ACS Photonics 6(2), 294–301 (2019).
[Crossref]

Xi, B.

Y. Abassi, B. Xi, N. Li, W. Ouyang, A. Seiler, M. Watzele, R. Kettenhofen, H. Bohlen, A. Ehlich, E. Kolossov, X. Wang, and X. Xu, “Dynamic monitoring of beating periodicity of stem cell-derived cardiomyocytes as a predictive tool for preclinical safety assessment,” Br. J. Pharmacol. 165(5), 1424–1441 (2012).
[Crossref]

Xu, X.

Y. Abassi, B. Xi, N. Li, W. Ouyang, A. Seiler, M. Watzele, R. Kettenhofen, H. Bohlen, A. Ehlich, E. Kolossov, X. Wang, and X. Xu, “Dynamic monitoring of beating periodicity of stem cell-derived cardiomyocytes as a predictive tool for preclinical safety assessment,” Br. J. Pharmacol. 165(5), 1424–1441 (2012).
[Crossref]

Yeom, S.

Yi, F.

Yoo, J.

N. Huebsch, P. Loskill, M. Mandegar, N. Marks, A. Sheehan, Z. Ma, A. Mathur, T. Nguyen, J. Yoo, L. Judge, C. Spencer, A. Chukka, C. Russell, P. So, B. Conklin, and K. Healy, “Automated video-based analysis of contractility and calcium flux in human-induced pluripotent stem cell-derived cardiomyocytes cultured over different spatial scales,” Tissue Eng., Part C 21(5), 467–479 (2015).
[Crossref]

Zambelli, T.

D. Ossola, M. Amarouch, P. Behr, J. Vörös, H. Abriel, and T. Zambelli, “Force-controlled patch clamp of beating cardiac cells,” Nano Lett. 15(3), 1743–1750 (2015).
[Crossref]

ACS Appl. Nano Mater. (1)

M. Mugnano, G. Lama, R. Castaldo, V. Marchesano, F. Merola, D. Giudice, A. Calabuig, G. Gentile, V. Ambrogi, P. Cerruti, P. Memmolo, V. Pagliarulo, P. Ferraro, and S. Grilli, “Cellular Uptake of Mildly Oxidized Nanographene for Drug-Delivery Applications,” ACS Appl. Nano Mater. 3(1), 428–439 (2020).
[Crossref]

ACS Photonics (2)

K. Jaferzadeh, B. Rappaz, F. Kuttler, B. K. Kim, I. Moon, P. Marquet, and G. Turcatti, “Marker-Free Automatic Quantification of Drug-Treated Cardiomyocytes with Digital Holographic Imaging,” ACS Photonics 7(1), 105–113 (2020).
[Crossref]

Y. Wu, A. Ray, Q. Wei, A. Feizi, X. Tong, E. Chen, Y. Luo, and A. Ozcan, “Deep Learning Enables High-Throughput Analysis of Particle-Aggregation-Based Biosensors Imaged Using Holography,” ACS Photonics 6(2), 294–301 (2019).
[Crossref]

Appl. Opt. (3)

Appl. Soft Comput. J. (1)

A. Garcia, S. Escolano, S. Oprea, V. Martinez, P. Gonzalez, and J. Rodriguez, “A survey on deep learning techniques for image and video semantic segmentation,” Appl. Soft Comput. J. 70, 41–65 (2018).
[Crossref]

Biofabrication (1)

A. Totaro, F. Urciuolo, G. Imparato, and P. Netti, “Engineered cardiac micromodules for the in vitro fabrication of 3D endogenous macro-tissues,” Biofabrication 8(2), 025014 (2016).
[Crossref]

Biomed. Eng. (1)

A. Ahola, A. L. Kiviaho, K. Larsson, M. Honkanen, K. Aalto-Setälä, and J. Hyttinen, “Video image-based analysis of single human induced pluripotent stem cell derived cardiomyocyte beating dynamics using digital image correlation,” Biomed. Eng. 13(1), 1–18 (2014).
[Crossref]

Biomed. Opt. Express (3)

Biophys. J. (1)

A. Brüggemann, C. Haarmann, M. Rapedius, T. Goetze, I. Rinke, M. George, and N. Fertig, “Characterization of iPS Derived Cardiomyocytes in Voltage Clamp and Current Clamp by Automated Patch Clamp,” Biophys. J. 112(3), 236a (2017).
[Crossref]

Br. J. Pharmacol. (1)

Y. Abassi, B. Xi, N. Li, W. Ouyang, A. Seiler, M. Watzele, R. Kettenhofen, H. Bohlen, A. Ehlich, E. Kolossov, X. Wang, and X. Xu, “Dynamic monitoring of beating periodicity of stem cell-derived cardiomyocytes as a predictive tool for preclinical safety assessment,” Br. J. Pharmacol. 165(5), 1424–1441 (2012).
[Crossref]

Heart (1)

C. Wheeler-Jones, “Cell signaling in the cardiovascular system: An overview,” Heart 91(10), 1366–1374 (2005).
[Crossref]

ICLR (1)

S. Jastrzębski, D. Arpit, N. Ballas, V. Verma, T. Che, and Y. Bengio, “Residual Connections Encourage Iterative Inference,” ICLR 2018, 1–14 (2018).

IEEE Trans. Med. Imaging (2)

L. Chen, P. Bentley, K. Mori, K. Misawa, M. Fujiwara, and D. Rueckert, “DRINet for Medical Image Segmentation,” IEEE Trans. Med. Imaging 37(11), 2453–2462 (2018).
[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]

Int. J. Biomed. Imaging (1)

C. Bazan, D. Barba, P. Blomgren, and P. Paolini, “Image processing techniques for assessing contractility in isolated neonatal cardiac myocytes,” Int. J. Biomed. Imaging 2011, 729732 (2011).
[Crossref]

J. Biomed. Opt. (2)

B. Rappaz, E. Cano, T. Colomb, J. Kühn, C. Depeursinge, V. Simanis, P. J. Magistretti, and P. Marquet, “Noninvasive characterization of the fission yeast cell cycle by monitoring dry mass with digital holographic microscopy,” J. Biomed. Opt. 14(3), 034049 (2009).
[Crossref]

E. Ahmadzadeh, K. Jaferzadeh, J. Lee, and I. Moon, “Automated three-dimensional morphology-based clustering of human erythrocytes with regular shapes: stomatocytes, discocytes, and echinocytes,” J. Biomed. Opt. 22(7), 076015 (2017).
[Crossref]

J. Biophotonics (1)

M. Mugnano, P. Memmolo, L. Miccio, S. Grilli, F. Merola, A. Calabuig, A. Bramanti, E. Mazzon, and P. Ferraro, “In vitro cytotoxicity evaluation of cadmium by label-free holographic microscopy,” J. Biophotonics 11(12), e201800099 (2018).
[Crossref]

Langmuir (1)

E. Grespan, S. Martewicz, E. Serena, V. Houerou, J. Rühe, and N. Elvassore, “Analysis of calcium transients and uniaxial contraction force in single human embryonic stem cell-derived cardiomyocytes on microstructured elastic substrate with spatially controlled surface chemistries,” Langmuir 32(46), 12190–12201 (2016).
[Crossref]

Lect. Notes Comput. Sci. (1)

O. Ronneberger, P. Fischer, and T. Brox, “U-net: Convolutional networks for biomedical image segmentation,” Lect. Notes Comput. Sci. 9351, 234–241 (2015).
[Crossref]

Meas. Sci. Technol. (1)

U. Schnars and W. Juptner, “Digital recording and numerical reconstruction of holograms,” Meas. Sci. Technol. 13(9), R85–R101 (2002).
[Crossref]

Med. Image Anal. (2)

G. Litjens, T. Kooi, B. Bejnordi, A. Setio, F. Ciompi, M. Ghafoorian, J. Laak, B. Ginneken, and C. Sánchez, “A survey on deep learning in medical image analysis,” Med. Image Anal. 42, 60–88 (2017).
[Crossref]

M. Khened, V. Kollerathu, and G. Krishnamurthi, “Fully convolutional multi-scale residual DenseNets for cardiac segmentation and automated cardiac diagnosis using ensemble of classifiers,” Med. Image Anal. 51, 21–45 (2019).
[Crossref]

Nano Lett. (1)

D. Ossola, M. Amarouch, P. Behr, J. Vörös, H. Abriel, and T. Zambelli, “Force-controlled patch clamp of beating cardiac cells,” Nano Lett. 15(3), 1743–1750 (2015).
[Crossref]

Nat. Phys. (1)

I. Nitsan, S. Drori, Y. Lewis, S. Cohen, and S. Tzlil, “Mechanical communication in cardiac cell synchronized beating,” Nat. Phys. 12(5), 472–477 (2016).
[Crossref]

Opt. Express (3)

Opt. Lett. (1)

Tissue Eng., Part C (1)

N. Huebsch, P. Loskill, M. Mandegar, N. Marks, A. Sheehan, Z. Ma, A. Mathur, T. Nguyen, J. Yoo, L. Judge, C. Spencer, A. Chukka, C. Russell, P. So, B. Conklin, and K. Healy, “Automated video-based analysis of contractility and calcium flux in human-induced pluripotent stem cell-derived cardiomyocytes cultured over different spatial scales,” Tissue Eng., Part C 21(5), 467–479 (2015).
[Crossref]

Other (1)

G. Huang, Z. Liu, L. Maaten, and K. Weinberger, “Densely connected convolutional networks,” IEEE Conf. Comput. Vis. Pattern Recognition, CVPR 2017, 2261–2269 (2017).

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Figures (12)

Fig. 1.
Fig. 1. Schematic representation of off-axis digital holographic microscopy used in this experiment to acquire cardiomyocyte phase image.
Fig. 2.
Fig. 2. (a) A recorded hologram of cardiomyocytes. Inset shows in the 3D portion of the hologram. (b) Phase image after numerical reconstruction. The phase image provides high contrast data for the quantitative analysis. (c) A single cardiac cell with a nucleus section marked with a red line defined as ROI to be extracted for dynamic beating profile quantification.
Fig. 3.
Fig. 3. Beating activity comparison of ROI versus non-ROI for precise ROI identification.
Fig. 4.
Fig. 4. The proposed FCN-based network architecture for cardiac cells ROI extraction has been made up of several modular blocks which are explained as follows: Parallel multi-pathway features concatenation (blue box) which takes the advantage of different kernel sizes: 1×1, 3×3, and 5×5. Each pathway is a composition of a convolutional layer + batch normalization layer + rectified linear unit. The features are concatenated at the end. 2×2 max-pooling layer with a stride of two is used for down-sampling data. The dense connection technique is used for efficient gradient propagation to prevent vanishing gradient. The residual connections are denoted with dotted horizontal red arrows representing residual skip connections.
Fig. 5.
Fig. 5. Sliding window patch extraction method for training data preparation. (a) Original QPI of cardiomyocytes containing multiple cells (yellow bar represents 20 µm). (b) Magnified portion of the original phase image with indicated patches. (c) Corresponding ground truth patches with ROI (yellow color) and non-ROI (dark blue color) portions.
Fig. 6.
Fig. 6. Results of ROI extraction using the proposed FCN-based method in comparison with the U-Net network model. (a) Original phase image of multiple cardiac cells obtained by DHM. (b) Predicted mask using our trained FCN-based model. (c) Predicted mask using the U-Net network model. (d) Ground truth mask extracted manually.
Fig. 7.
Fig. 7. Example of CM beating profile reconstruction before and after ROI extraction. (a) and (b) QPI containing multiple CMs before ROI extraction and corresponding beating activity profile respectively. (c) and (d) QPI after ROI extraction using the proposed method (green outline) and corresponding beating activity profile.
Fig. 8.
Fig. 8. (a) Original phase image of multiple cardiomyocytes in which single cells are denoted for further quantification. (b) The beating activity profile derived from cells #1 (30 seconds is shown) and (c) details on quantification parameters explained in Table 1.
Fig. 9.
Fig. 9. Beating profiles of single cells extracted from sample number five (Cell #1, Cell #2, Cell #3, Cell #4, Cell #5, and Cell #6). The sample is recorded with a 10 Hz sampling frequency for 54 seconds.
Fig. 10.
Fig. 10. Quantification results of the single extracted CMs beating profile.
Fig. 11.
Fig. 11. Comparison of the learning curves and confusion matrix for pixel classification of the proposed FCN-based model versus the U-Net model. (a) and (b) Training process accuracy in 55 epochs and corresponding loss curves respectively. (c) Confusion matrix of pixel classification prediction accuracy for the proposed model. (d) Confusion matrix of pixel classification prediction accuracy for the U-Net model.
Fig. 12.
Fig. 12. (a) Segmentation result of original QPI of multiple cardiomyocytes before Hoechst nuclei staining, (b) The original QPI with nuclei staining is overlaid on the segmented image. The segmented sections (ROI) mostly include the nuclei part. The inset shows a single cardiomyocyte with a nuclei section marked with a green line defined as ROI to be extracted for dynamic beating profile quantification.

Tables (2)

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Table 1. Description of cardiomyocyte dynamic parameters quantification.

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Table 2. Segmentation performance evaluation using the Dice coefficient analysis of the proposed method against the U-Net model.

Equations (3)

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I H ( x , y ) = | R | 2 + | O | 2 + R O + R O ,
o p d v a r = var [ o p d i o p d i 1 ] ,
D S C = 2 T P F P + 2 T P + F N ,

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