Abstract

A single-beam acoustic trapping technique has been shown to be very useful for determining the invasiveness of suspended breast cancer cells in an acoustic trap with a manual calcium analysis method. However, for the rapid translation of the technology into the clinic, the development of an efficient/accurate analytical method is needed. We, therefore, develop a fully-automatic deep learning-based calcium image analysis algorithm for determining the invasiveness of suspended breast cancer cells using a single-beam acoustic trapping system. The algorithm allows to segment cells, find trapped cells, and quantify their calcium changes over time. For better segmentation of calcium fluorescent cells even with vague boundaries, a novel deep learning architecture with multi-scale/multi-channel convolution operations (MM-Net) is devised and constructed by a target inversion training method. The MM-Net outperforms other deep learning models in the cell segmentation. Also, a detection/quantification algorithm is developed and implemented to automatically determine the invasiveness of a trapped cell. For the evaluation of the algorithm, it is applied to quantify the invasiveness of breast cancer cells. The results show that the algorithm offers similar performance to the manual calcium analysis method for determining the invasiveness of cancer cells, suggesting that it may serve as a novel tool to automatically determine the invasiveness of cancer cells with high-efficiency.

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

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2019 (1)

S. Youn, J. W. Choi, J. S. Lee, J. Kim, I.-H. Yang, J. H. Chang, H. C. Kim, and J. Y. Hwang, “Acoustic trapping technique for studying calcium response of a suspended breast cancer cell: Determination of its invasion potentials,” IEEE Trans. Ultrason., Ferroelect., Freq. Contr. 66(4), 737–746 (2019).
[Crossref]

2018 (1)

A. Arbelle, J. Reyes, J.-Y. Chen, G. Lahav, and T. R. Raviv, “A probabilistic approach to joint cell tracking and segmentation in high-throughput microscopy videos,” Med. Image Anal. 47, 140–152 (2018).
[Crossref]

2017 (2)

A. Muñoz Barrutia, S. Wolf, R. Mikut, C. C. Reyes-Aldasoro, H. M. Blau, L. Malmström, S.-Y. Cho, V. Ulman, P. Matula, F. Jug, M. Kozubek, E. Meijering, P. Quelhas, O. Dzyubachyk, J.-C. Olivo-Marin, F. a. Hamprecht, T. Esteves, J. Jaldén, D. Svoboda, M. Maška, O. Ronneberger, R. Bensch, T. Brox, P. Tomancak, J. a. Solis-Lemus, C. Haubold, A. C. Dufour, P. Matula, B. Lelieveldt, J. Stegmaier, C. Ortiz-de Solorzano, O. Demirel, Y. Li, I. Smal, N. Harder, K. Rohr, K. E. G. Magnusson, P. Xiao, and M. Radojevic, “An objective comparison of cell-tracking algorithms,” Nat. Methods 14(12), 1141–1152 (2017).
[Crossref]

S. K. Sadanandan, P. Ranefall, S. Le Guyader, and C. Wählby, “Automated training of deep convolutional neural networks for cell segmentation,” Sci. Rep. 7(1), 7860 (2017).
[Crossref]

2016 (2)

D. A. Van Valen, T. Kudo, K. M. Lane, D. N. Macklin, N. T. Quach, M. M. DeFelice, I. Maayan, Y. Tanouchi, E. A. Ashley, and M. W. Covert, “Deep learning automates the quantitative analysis of individual cells in live-cell imaging experiments,” PLoS Comput. Biol. 12(11), e1005177 (2016).
[Crossref]

J. Y. Hwang, J. Kim, J. M. Park, C. Lee, H. Jung, J. Lee, and K. K. Shung, “Cell deformation by single-beam acoustic trapping: a promising tool for measurements of cell mechanics,” Sci. Rep. 6(1), 27238 (2016).
[Crossref]

2013 (1)

J. Y. Hwang, N. S. Lee, C. Lee, K. H. Lam, H. H. Kim, J. Woo, M.-Y. Lin, K. Kisler, H. Choi, and Q. Zhou, “Investigating contactless high frequency ultrasound microbeam stimulation for determination of invasion potential of breast cancer cells,” Biotechnol. Bioeng. 110(10), 2697–2705 (2013).
[Crossref]

2011 (4)

A. A. Dima, J. T. Elliott, J. J. Filliben, M. Halter, A. Peskin, J. Bernal, M. Kociolek, M. C. Brady, H. C. Tang, and A. L. Plant, “Comparison of segmentation algorithms for fluorescence microscopy images of cells,” Cytometry, Part A 79A(7), 545–559 (2011).
[Crossref]

A. Jemal, F. Bray, M. M. Center, J. Ferlay, E. Ward, and D. Forman, “Global cancer statistics,” CA: a cancer journal for clinicians 61, 69–90 (2011).

D. Padfield, J. Rittscher, and B. Roysam, “Coupled minimum-cost flow cell tracking for high-throughput quantitative analysis,” Med. Image Anal. 15(4), 650–668 (2011).
[Crossref]

A. Kan, R. Chakravorty, J. Bailey, C. Leckie, J. Markham, and M. Dowling, “Automated and semi-automated cell tracking: addressing portability challenges,” J. Microsc. 244(2), 194–213 (2011).
[Crossref]

2008 (1)

M. Dienerowitz, M. Mazilu, and K. Dholakia, “Optical manipulation of nanoparticles: a review,” J. Nanophotonics 2(1), 021875 (2008).
[Crossref]

2006 (2)

A. W. Orr, B. P. Helmke, B. R. Blackman, and M. A. Schwartz, “Mechanisms of mechanotransduction,” Dev. Cell 10(1), 11–20 (2006).
[Crossref]

Z. Bao, J. I. Murray, T. Boyle, S. L. Ooi, M. J. Sandel, and R. H. Waterston, “Automated cell lineage tracing in caenorhabditis elegans,” Proc. Natl. Acad. Sci. 103(8), 2707–2712 (2006).
[Crossref]

2005 (4)

O. Debeir, P. Van Ham, R. Kiss, and C. Decaestecker, “Tracking of migrating cells under phase-contrast video microscopy with combined mean-shift processes,” IEEE Trans. Med. Imaging 24(6), 697–711 (2005).
[Crossref]

M. J. Paszek, N. Zahir, K. R. Johnson, J. N. Lakins, G. I. Rozenberg, A. Gefen, C. A. Reinhart-King, S. S. Margulies, M. Dembo, D. Boettiger, D. A. Hammer, and V. M. Weaver, “Tensional homeostasis and the malignant phenotype,” Cancer Cell 8(3), 241–254 (2005).
[Crossref]

K. Kunzelmann, “Ion channels and cancer,” J. Membrane Biol. 205(3), 159–173 (2005).
[Crossref]

S. P. Fraser, J. K. Diss, A.-M. Chioni, M. E. Mycielska, H. Pan, R. F. Yamaci, F. Pani, Z. Siwy, M. Krasowska, and Z. Grzywna, “Voltage-gated sodium channel expression and potentiation of human breast cancer metastasis,” Clin. Cancer Res. 11(15), 5381–5389 (2005).
[Crossref]

2004 (1)

F. Chang, C.-J. Chen, and C.-J. Lu, “A linear-time component-labeling algorithm using contour tracing technique,” Comput. Vis. Image Und. 93(2), 206–220 (2004).
[Crossref]

2003 (1)

F. J. Giessibl, “Advances in atomic force microscopy,” Rev. Mod. Phys. 75(3), 949–983 (2003).
[Crossref]

1993 (1)

N. Wang, J. P. Butler, and D. E. Ingber, “Mechanotransduction across the cell surface and through the cytoskeleton,” Science 260(5111), 1124–1127 (1993).
[Crossref]

Abadi, M.

M. Abadi, P. Barham, J. Chen, Z. Chen, A. Davis, J. Dean, M. Devin, S. Ghemawat, G. Irving, and M. Isard et al., “Tensorflow: A system for large-scale machine learning, 12th {USENIX} Symposium on Operating Systems Design and Implementation ({OSDI} 16), (2016), pp. 265–283.

Aharoni, A.

A. S. Aydin, A. Dubey, D. Dovrat, A. Aharoni, and R. Shilkrot, “Cnn based yeast cell segmentation in multi-modal fluorescent microscopy data,” in 2017 IEEE Conference on Computer Vision and Pattern Recognition Workshops (CVPRW), (IEEE, 2017), pp. 753–759.

Alemi, A. A.

C. Szegedy, S. Ioffe, V. Vanhoucke, and A. A. Alemi, “Inception-v4, inception-resnet and the impact of residual connections on learning,” in Thirty-first AAAI conference on artificial intelligence, (2017).

Arbelle, A.

A. Arbelle, J. Reyes, J.-Y. Chen, G. Lahav, and T. R. Raviv, “A probabilistic approach to joint cell tracking and segmentation in high-throughput microscopy videos,” Med. Image Anal. 47, 140–152 (2018).
[Crossref]

Ashley, E. A.

D. A. Van Valen, T. Kudo, K. M. Lane, D. N. Macklin, N. T. Quach, M. M. DeFelice, I. Maayan, Y. Tanouchi, E. A. Ashley, and M. W. Covert, “Deep learning automates the quantitative analysis of individual cells in live-cell imaging experiments,” PLoS Comput. Biol. 12(11), e1005177 (2016).
[Crossref]

Aydin, A. S.

A. S. Aydin, A. Dubey, D. Dovrat, A. Aharoni, and R. Shilkrot, “Cnn based yeast cell segmentation in multi-modal fluorescent microscopy data,” in 2017 IEEE Conference on Computer Vision and Pattern Recognition Workshops (CVPRW), (IEEE, 2017), pp. 753–759.

Bacarian, T.

T. Bacarian, M. Elowitz, E. Mjolsness, and V. Gor, “Tracking cell signals in fluorescent images,” in 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR’05)-Workshops, (2005), pp. 142.

Bailey, J.

A. Kan, R. Chakravorty, J. Bailey, C. Leckie, J. Markham, and M. Dowling, “Automated and semi-automated cell tracking: addressing portability challenges,” J. Microsc. 244(2), 194–213 (2011).
[Crossref]

Bao, Z.

Z. Bao, J. I. Murray, T. Boyle, S. L. Ooi, M. J. Sandel, and R. H. Waterston, “Automated cell lineage tracing in caenorhabditis elegans,” Proc. Natl. Acad. Sci. 103(8), 2707–2712 (2006).
[Crossref]

Barham, P.

M. Abadi, P. Barham, J. Chen, Z. Chen, A. Davis, J. Dean, M. Devin, S. Ghemawat, G. Irving, and M. Isard et al., “Tensorflow: A system for large-scale machine learning, 12th {USENIX} Symposium on Operating Systems Design and Implementation ({OSDI} 16), (2016), pp. 265–283.

Bensch, R.

A. Muñoz Barrutia, S. Wolf, R. Mikut, C. C. Reyes-Aldasoro, H. M. Blau, L. Malmström, S.-Y. Cho, V. Ulman, P. Matula, F. Jug, M. Kozubek, E. Meijering, P. Quelhas, O. Dzyubachyk, J.-C. Olivo-Marin, F. a. Hamprecht, T. Esteves, J. Jaldén, D. Svoboda, M. Maška, O. Ronneberger, R. Bensch, T. Brox, P. Tomancak, J. a. Solis-Lemus, C. Haubold, A. C. Dufour, P. Matula, B. Lelieveldt, J. Stegmaier, C. Ortiz-de Solorzano, O. Demirel, Y. Li, I. Smal, N. Harder, K. Rohr, K. E. G. Magnusson, P. Xiao, and M. Radojevic, “An objective comparison of cell-tracking algorithms,” Nat. Methods 14(12), 1141–1152 (2017).
[Crossref]

Bernal, J.

A. A. Dima, J. T. Elliott, J. J. Filliben, M. Halter, A. Peskin, J. Bernal, M. Kociolek, M. C. Brady, H. C. Tang, and A. L. Plant, “Comparison of segmentation algorithms for fluorescence microscopy images of cells,” Cytometry, Part A 79A(7), 545–559 (2011).
[Crossref]

Binnig, G.

G. Binnig, C. Quate, and C. Gerber, “Atomic force microscope,” Scanning Tunneling Microscopy, (Springer, 1993), pp. 55–58.

Bischof, H.

C. Payer, D. Štern, T. Neff, H. Bischof, and M. Urschler, “Instance segmentation and tracking with cosine embeddings and recurrent hourglass networks,” in International Conference on Medical Image Computing and Computer-Assisted Intervention, (Springer, 2018), pp. 3–11.

Blackman, B. R.

A. W. Orr, B. P. Helmke, B. R. Blackman, and M. A. Schwartz, “Mechanisms of mechanotransduction,” Dev. Cell 10(1), 11–20 (2006).
[Crossref]

Blau, H. M.

A. Muñoz Barrutia, S. Wolf, R. Mikut, C. C. Reyes-Aldasoro, H. M. Blau, L. Malmström, S.-Y. Cho, V. Ulman, P. Matula, F. Jug, M. Kozubek, E. Meijering, P. Quelhas, O. Dzyubachyk, J.-C. Olivo-Marin, F. a. Hamprecht, T. Esteves, J. Jaldén, D. Svoboda, M. Maška, O. Ronneberger, R. Bensch, T. Brox, P. Tomancak, J. a. Solis-Lemus, C. Haubold, A. C. Dufour, P. Matula, B. Lelieveldt, J. Stegmaier, C. Ortiz-de Solorzano, O. Demirel, Y. Li, I. Smal, N. Harder, K. Rohr, K. E. G. Magnusson, P. Xiao, and M. Radojevic, “An objective comparison of cell-tracking algorithms,” Nat. Methods 14(12), 1141–1152 (2017).
[Crossref]

Boettiger, D.

M. J. Paszek, N. Zahir, K. R. Johnson, J. N. Lakins, G. I. Rozenberg, A. Gefen, C. A. Reinhart-King, S. S. Margulies, M. Dembo, D. Boettiger, D. A. Hammer, and V. M. Weaver, “Tensional homeostasis and the malignant phenotype,” Cancer Cell 8(3), 241–254 (2005).
[Crossref]

Boyle, T.

Z. Bao, J. I. Murray, T. Boyle, S. L. Ooi, M. J. Sandel, and R. H. Waterston, “Automated cell lineage tracing in caenorhabditis elegans,” Proc. Natl. Acad. Sci. 103(8), 2707–2712 (2006).
[Crossref]

Brady, M. C.

A. A. Dima, J. T. Elliott, J. J. Filliben, M. Halter, A. Peskin, J. Bernal, M. Kociolek, M. C. Brady, H. C. Tang, and A. L. Plant, “Comparison of segmentation algorithms for fluorescence microscopy images of cells,” Cytometry, Part A 79A(7), 545–559 (2011).
[Crossref]

Bray, F.

A. Jemal, F. Bray, M. M. Center, J. Ferlay, E. Ward, and D. Forman, “Global cancer statistics,” CA: a cancer journal for clinicians 61, 69–90 (2011).

Brox, T.

A. Muñoz Barrutia, S. Wolf, R. Mikut, C. C. Reyes-Aldasoro, H. M. Blau, L. Malmström, S.-Y. Cho, V. Ulman, P. Matula, F. Jug, M. Kozubek, E. Meijering, P. Quelhas, O. Dzyubachyk, J.-C. Olivo-Marin, F. a. Hamprecht, T. Esteves, J. Jaldén, D. Svoboda, M. Maška, O. Ronneberger, R. Bensch, T. Brox, P. Tomancak, J. a. Solis-Lemus, C. Haubold, A. C. Dufour, P. Matula, B. Lelieveldt, J. Stegmaier, C. Ortiz-de Solorzano, O. Demirel, Y. Li, I. Smal, N. Harder, K. Rohr, K. E. G. Magnusson, P. Xiao, and M. Radojevic, “An objective comparison of cell-tracking algorithms,” Nat. Methods 14(12), 1141–1152 (2017).
[Crossref]

O. Ronneberger, P. Fischer, and T. Brox, “U-net: Convolutional networks for biomedical image segmentation,” in International Conference on Medical Image Computing and Computer-assisted Intervention, (Springer, 2015),pp. 234–241.

Butler, J. P.

N. Wang, J. P. Butler, and D. E. Ingber, “Mechanotransduction across the cell surface and through the cytoskeleton,” Science 260(5111), 1124–1127 (1993).
[Crossref]

Center, M. M.

A. Jemal, F. Bray, M. M. Center, J. Ferlay, E. Ward, and D. Forman, “Global cancer statistics,” CA: a cancer journal for clinicians 61, 69–90 (2011).

Chakravorty, R.

A. Kan, R. Chakravorty, J. Bailey, C. Leckie, J. Markham, and M. Dowling, “Automated and semi-automated cell tracking: addressing portability challenges,” J. Microsc. 244(2), 194–213 (2011).
[Crossref]

Chang, F.

F. Chang, C.-J. Chen, and C.-J. Lu, “A linear-time component-labeling algorithm using contour tracing technique,” Comput. Vis. Image Und. 93(2), 206–220 (2004).
[Crossref]

Chang, J. H.

S. Youn, J. W. Choi, J. S. Lee, J. Kim, I.-H. Yang, J. H. Chang, H. C. Kim, and J. Y. Hwang, “Acoustic trapping technique for studying calcium response of a suspended breast cancer cell: Determination of its invasion potentials,” IEEE Trans. Ultrason., Ferroelect., Freq. Contr. 66(4), 737–746 (2019).
[Crossref]

Chen, C.-J.

F. Chang, C.-J. Chen, and C.-J. Lu, “A linear-time component-labeling algorithm using contour tracing technique,” Comput. Vis. Image Und. 93(2), 206–220 (2004).
[Crossref]

Chen, J.

M. Abadi, P. Barham, J. Chen, Z. Chen, A. Davis, J. Dean, M. Devin, S. Ghemawat, G. Irving, and M. Isard et al., “Tensorflow: A system for large-scale machine learning, 12th {USENIX} Symposium on Operating Systems Design and Implementation ({OSDI} 16), (2016), pp. 265–283.

Chen, J.-Y.

A. Arbelle, J. Reyes, J.-Y. Chen, G. Lahav, and T. R. Raviv, “A probabilistic approach to joint cell tracking and segmentation in high-throughput microscopy videos,” Med. Image Anal. 47, 140–152 (2018).
[Crossref]

Chen, Z.

M. Abadi, P. Barham, J. Chen, Z. Chen, A. Davis, J. Dean, M. Devin, S. Ghemawat, G. Irving, and M. Isard et al., “Tensorflow: A system for large-scale machine learning, 12th {USENIX} Symposium on Operating Systems Design and Implementation ({OSDI} 16), (2016), pp. 265–283.

Chioni, A.-M.

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Figures (9)

Fig. 1.
Fig. 1. Conceptual diagram of a single beam acoustic trapping system with deep learning-based calcium analysis of breast cancer cells in an acoustic trap: (a) Single-beam acoustic trapping fluorescence microscopic system (b) Deep learning-based calcium analysis algorithm for the determination of invasion potentials of a trapped cell.
Fig. 2.
Fig. 2. Characteristics of a press-focused single-crystal $LiNbO_3$ ultrasound transducer: (a) Photographic image of the ultrasound transducer (b) Lateral (left) and axial acoustic beam profile (right) (c) Pulse-echo characteristics of the transducer (d) Acoustic pressure versus input voltage to the transducer.
Fig. 3.
Fig. 3. Detailed architecture of MM-Net (a) Encoder (Downsampling) and Decoder (Upsampling) block of the proposed model. (b) Multi-scale/ multi-channel deep learning model (MM-Net) for the segmentation of fluorescence cells (the number of a training set: 1,264, the number of a validation set: 624, the number of a test set: 640).
Fig. 4.
Fig. 4. Procedures of a fully-automatic deep learning-based analysis algorithm.
Fig. 5.
Fig. 5. Segmented images of breast cancer cells obtained by the MM-Net, U-Net, FusionNet, FCN, and Mask R-CNN.
Fig. 6.
Fig. 6. Detection of a trapped cell using the developed algorithm. (a) Segmented images before and after acoustic trapping of MDA-MB-231 (upper) and MCF-7 cells (lower); (b) Trajectory of the trapped and non-trapped cells.
Fig. 7.
Fig. 7. Measurements of calcium changes in MDA-MB-231 and MCF-7 cells due to acoustic trapping by using our developed algorithm. (a) Original fluorescence (left), segmented(middle), and labeled (right) images for trapped and non-trapped cells (MCF-7: upper, MDA-MB-231: lower); in each image, the trapped cell is labeled in red whereas the non-trapped cell is labeled in blue. (b) Normalized calcium fluorescence profiles of trapped MCF-7 (black color) and MDA-MB-231 cells (blue color) extracted by our algorithm and manual calcium analysis method; The solid circle indicates our developed method whereas the symbol, ’X’ indicates the manual calcium analysis method
Fig. 8.
Fig. 8. Cumulative frequency of calcium response of MDA-MB-231 (n=17) and MCF-7 (n=17) cells to acoustic trapping force at different pressures of 1.01, 1.31, 1.75, and 2.12MPa.
Fig. 9.
Fig. 9. Quantitative analysis of calcium levels of MDA-MB-231 (n=22) and MCF-7 cells (n=22) in an acoustic trap at 1.01 MPa obtained (a) by using the developed algorithm (b) and the manual calcium analysis method.

Tables (3)

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Table 1. Architecture of the MM-Net model.

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Table 2. Pseudo-code of the algorithm for identifying a trapped cell

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Table 3. Intersection of Union values and Dice coefficients of MM-Net and other deep learning models (U-Net, FusionNet, FCN, and Mask R-CNN) for cell segmentation

Equations (6)

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A(i,j)=αD(i,j)+βI(i,j)+γH(i,j)iImg(Ct),jImg(Ct+1)
Cell_Indext+1(i | iImg(Ct))=argminjImg(Ct+1)A(i,j)
A(i,j)thresholdCell_Indext+1(i),iImg(Ct+1)
Sk:={Snk:S1kC1}Sn:={S1kC1,Next(Sn1k),
TCellk(Sk)1=δ×std(D(Sk))+ε×std(G(Sk))+ϵ×std(G(Sk))
Tcellk(Sk)threshold the cell forSk is trapped

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