Abstract

A custom convolutional neural network (CNN) integrated with convolutional long short-term memory (LSTM) achieves accurate 3D (2D + time) segmentation in cross-sectional videos of the Drosophila heart acquired by an optical coherence microscopy (OCM) system. While our previous FlyNet 1.0 model utilized regular CNNs to extract 2D spatial information from individual video frames, convolutional LSTM, FlyNet 2.0, utilizes both spatial and temporal information to improve segmentation performance further. To train and test FlyNet 2.0, we used 100 datasets including 500,000 fly heart OCM images. OCM videos in three developmental stages and two heartbeat situations were segmented achieving an intersection over union (IOU) accuracy of 92%. This increased segmentation accuracy allows morphological and dynamic cardiac parameters to be better quantified.

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

Full Article  |  PDF Article

Corrections

6 March 2020: A typographical correction was made to the body text.


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References

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2018 (1)

L. Duan, X. Qin, Y. He, X. Sang, J. Pan, T. Xu, J. Men, R. Tanzi, A. Li, Y. Ma, and C. Zhou, “Segmentation of Drosophila Heart in Optical Coherence Microscopy Images Using Convolutional Neural Networks,” J. Biophoton. 11, e201800146 (2018).
[Crossref]

2017 (3)

E. Shelhamer, J. Long, and T. Darrell, “Fully Convolutional Networks for Semantic Segmentation,” IEEE Trans. Pattern Anal. Mach. Intell. 39(4), 640–651 (2017).
[Crossref]

A. Krizhevsky, I. Sutskever, and G. E. Hinton, “ImageNet classification with deep convolutional neural networks,” Commun. ACM 60(6), 84–90 (2017).
[Crossref]

V. Badrinarayanan, A. Kendall, and R. Cipolla, “SegNet: A Deep Convolutional Encoder-Decoder Architecture for Image Segmentation,” IEEE Trans. Pattern Anal. Mach. Intell. 39(12), 2481–2495 (2017).
[Crossref]

2016 (2)

J. Men, J. Jerwick, P. Wu, M. Chen, A. Alex, Y. Ma, R. E. Tanzi, A. Li, and C. Zhou, “Drosophila Preparation and Longitudinal Imaging of Heart Function In Vivo Using Optical Coherence Microscopy (OCM),” J. Visualized Exp. 118, 55002 (2016).
[Crossref]

J. Men, Y. Huang, J. Solanki, X. Zeng, A. Alex, J. Jerwick, Z. Zhang, R. E. Tanzi, A. Li, and C. Zhou, “Optical Coherence Tomography for Brain Imaging and Developmental Biology,” IEEE J. Sel. Top. Quantum Electron. 22(4), 1–13 (2016).
[Crossref]

2015 (1)

A. Alex, A. Li, R. E. Tanzi, and C. Zhou, “Optogenetic pacing in Drosophila melanogaster,” Sci. Adv. 1(9), e1500639 (2015).
[Crossref]

2013 (2)

2012 (2)

2011 (5)

T. Klein, W. Wieser, C. M. Eigenwillig, B. R. Biedermann, and R. Huber, “Megahertz OCT for ultrawide-field retinal imaging with a 1050 nm Fourier domain mode-locked laser,” Opt. Express 19(4), 3044–3062 (2011).
[Crossref]

J. Reiber, S. Tu, J. Tuinenburg, G. Koning, J. Janssen, and J. Dijkstra, “QCA, IVUS and OCT in interventional cardiology in 2011,” Cardiovasc. Diagn. Ther. 1(1), 57–70 (2011).
[Crossref]

A. Li, C. Zhou, J. Moore, P. Zhang, T. H. Tsai, H. C. Lee, D. M. Romano, M. L. McKee, D. A. Schoenfeld, M. J. Serra, K. Raygor, H. F. Cantiello, J. G. Fujimoto, and R. E. Tanzi, “Changes in the expression of the Alzheimer’s disease-associated presenilin gene in drosophila heart leads to cardiac dysfunction,” Curr. Alzheimer Res. 8(3), 313–322 (2011).
[Crossref]

N. Piazza and R. J. Wessells, “Drosophila models of cardiac disease,” Prog. Mol. Biol. Transl. Sci. 100, 155–210 (2011).
[Crossref]

U. B. Pandey and C. D. Nichols, “Human disease models in Drosophila melanogaster and the role of the fly in therapeutic drug discovery,” Pharmacol. Rev. 63(2), 411–436 (2011).
[Crossref]

2010 (1)

2006 (1)

M. Wolf, H. Amrein, J. Izatt, M. Choma, M. Reedy, and H. Rockman, “Drosophila as a model for the identification of genes causing adult human heart disease,” Proc. Natl. Acad. Sci. U. S. A. 103(5), 1394–1399 (2006).
[Crossref]

2005 (1)

T. Gambichler, G. Moussa, M. Sand, D. Sand, P. Altmeyer, and K. Hoffmann, “Applications of optical coherence tomography in dermatology,” J. Dermatol. Sci. 40(2), 85–94 (2005).
[Crossref]

2004 (1)

D. G. Lowe, “Distinctive Image Features from Scale-Invariant Keypoints,” Int. J. Comput. Vis. 60(2), 91–110 (2004).
[Crossref]

2003 (3)

2001 (3)

L. T. Reiter, L. Potocki, S. Chien, M. Gribskov, and E. Bier, “A systematic analysis of human disease-associated gene sequences in Drosophila melanogaster,” Genome Res. 11(6), 1114–1125 (2001).
[Crossref]

S. Hu, E. Hoffman, and J. Reinhardt, “Automatic lung segmentation for accurate quantitation of volumetric X-ray CT images,” IEEE Trans. Med. Imaging 20(6), 490–498 (2001).
[Crossref]

J. Welzel, “Optical coherence tomography in dermatology: a review,” Skin Res. Technol. 7(1), 1–9 (2001).
[Crossref]

2000 (1)

L. L. Otis, M. J. Everett, U. S. Sathyam, and B. W. Colston, “Optical coherence tomography: a new imaging: technology for dentistry,” J. Am. Dent. Assoc. 131(4), 511–514 (2000).
[Crossref]

1999 (1)

J. A. K. Suykens and J. Vandewalle, “Least Squares Support Vector Machine Classifiers,” Neural Process. Lett. 9(3), 293–300 (1999).
[Crossref]

1998 (2)

S. Hochreiter, “The Vanishing Gradient Problem During Learning Recurrent Neural Nets and Problem Solutions,” Int. J. Unc. Fuzz. Knowl. Based Syst. 6(2), 107–116 (1998).
[Crossref]

R. Bodmer and T. V. Venkatesh, “Heart development in Drosophila and vertebrates: Conservation of molecular mechanisms,” Dev. Genet. 22(3), 181–186 (1998).
[Crossref]

1991 (1)

D. Huang, E. A. Swanson, C. P. Lin, J. S. Schuman, W. G. Stinson, W. Chang, M. R. Hee, T. Flotte, K. Gregory, C. A. Puliafito, and J. G. Fujimoto, “Optical Coherence Tomography,” Science 254(5035), 1178–1181 (1991).
[Crossref]

1990 (1)

J. L. Elman, “Finding structure in time,” Cogn. Sci. 14(2), 179–211 (1990).
[Crossref]

Alex, A.

J. Men, J. Jerwick, P. Wu, M. Chen, A. Alex, Y. Ma, R. E. Tanzi, A. Li, and C. Zhou, “Drosophila Preparation and Longitudinal Imaging of Heart Function In Vivo Using Optical Coherence Microscopy (OCM),” J. Visualized Exp. 118, 55002 (2016).
[Crossref]

J. Men, Y. Huang, J. Solanki, X. Zeng, A. Alex, J. Jerwick, Z. Zhang, R. E. Tanzi, A. Li, and C. Zhou, “Optical Coherence Tomography for Brain Imaging and Developmental Biology,” IEEE J. Sel. Top. Quantum Electron. 22(4), 1–13 (2016).
[Crossref]

A. Alex, A. Li, R. E. Tanzi, and C. Zhou, “Optogenetic pacing in Drosophila melanogaster,” Sci. Adv. 1(9), e1500639 (2015).
[Crossref]

Altmeyer, P.

T. Gambichler, G. Moussa, M. Sand, D. Sand, P. Altmeyer, and K. Hoffmann, “Applications of optical coherence tomography in dermatology,” J. Dermatol. Sci. 40(2), 85–94 (2005).
[Crossref]

Amrein, H.

M. Wolf, H. Amrein, J. Izatt, M. Choma, M. Reedy, and H. Rockman, “Drosophila as a model for the identification of genes causing adult human heart disease,” Proc. Natl. Acad. Sci. U. S. A. 103(5), 1394–1399 (2006).
[Crossref]

Antoine, B.

G. Xavier, B. Antoine, and B. Yoshua, “Deep Sparse Rectifier Neural Networks,” (PMLR, 2011), pp. 315–323.

Arbelle, A.

A. Arbelle and T. R. Raviv, “Microscopy Cell Segmentation via Convolutional LSTM Networks,” arXiv preprint arXiv:1805.11247 (2018).

Asano, S.

S. Asano, T. Maruyama, and Y. Yamaguchi, “Performance comparison of FPGA, GPU and CPU in image processing,” in 2009 International Conference on Field Programmable Logic and Applications, 2009), 126–131.

Ba, J.

D. Kingma and J. Ba, “Adam: A Method for Stochastic Optimization,” International Conference on Learning Representations (2014).

Badrinarayanan, V.

V. Badrinarayanan, A. Kendall, and R. Cipolla, “SegNet: A Deep Convolutional Encoder-Decoder Architecture for Image Segmentation,” IEEE Trans. Pattern Anal. Mach. Intell. 39(12), 2481–2495 (2017).
[Crossref]

Bao, Y.

L. Zheng, G. Li, and Y. Bao, “Improvement of grayscale image 2D maximum entropy threshold segmentation method,” in 2010 International Conference on Logistics Systems and Intelligent Management (ICLSIM), 2010), 324–328.

Becnel, J.

C. D. Nichols, J. Becnel, and U. B. Pandey, “Methods to assay Drosophila behavior,” J. Visualized Exp. 61, 3795 (2012).
[Crossref]

Bengio, Y.

J. Chung, Ç. Gülçehre, K. Cho, and Y. Bengio, “Empirical Evaluation of Gated Recurrent Neural Networks on Sequence Modeling,” CoRR abs/1412.3555 (2014).

Biedermann, B. R.

Bier, E.

L. T. Reiter, L. Potocki, S. Chien, M. Gribskov, and E. Bier, “A systematic analysis of human disease-associated gene sequences in Drosophila melanogaster,” Genome Res. 11(6), 1114–1125 (2001).
[Crossref]

Bodmer, R.

R. Bodmer and T. V. Venkatesh, “Heart development in Drosophila and vertebrates: Conservation of molecular mechanisms,” Dev. Genet. 22(3), 181–186 (1998).
[Crossref]

Bouma, B. E.

Bourdev, L.

L. Bourdev, S. Maji, T. Brox, and J. Malik, “Detecting people using mutually consistent poselet activations,” in Proceedings of the 11th European conference on Computer vision: Part VI, (Springer-Verlag, Heraklion, Crete, Greece, 2010), pp. 168–181.

Brox, T.

L. Bourdev, S. Maji, T. Brox, and J. Malik, “Detecting people using mutually consistent poselet activations,” in Proceedings of the 11th European conference on Computer vision: Part VI, (Springer-Verlag, Heraklion, Crete, Greece, 2010), pp. 168–181.

O. Ronneberger, P. Fischer, and T. Brox, “U-Net: Convolutional Networks for Biomedical Image Segmentation,” in Medical Image Computing and Computer-Assisted Intervention – MICCAI 2015, (Springer International Publishing, 2015), 234–241.

Cable, A. E.

Cantiello, H. F.

A. Li, C. Zhou, J. Moore, P. Zhang, T. H. Tsai, H. C. Lee, D. M. Romano, M. L. McKee, D. A. Schoenfeld, M. J. Serra, K. Raygor, H. F. Cantiello, J. G. Fujimoto, and R. E. Tanzi, “Changes in the expression of the Alzheimer’s disease-associated presenilin gene in drosophila heart leads to cardiac dysfunction,” Curr. Alzheimer Res. 8(3), 313–322 (2011).
[Crossref]

Cense, B.

Chang, W.

D. Huang, E. A. Swanson, C. P. Lin, J. S. Schuman, W. G. Stinson, W. Chang, M. R. Hee, T. Flotte, K. Gregory, C. A. Puliafito, and J. G. Fujimoto, “Optical Coherence Tomography,” Science 254(5035), 1178–1181 (1991).
[Crossref]

Chartrand, G.

M. Drozdzal, E. Vorontsov, G. Chartrand, S. Kadoury, and C. Pal, “The importance of skip connections in biomedical image segmentation,” in Deep Learning and Data Labeling for Medical Applications (Springer, 2016), pp. 179–187.

Chen, M.

J. Men, J. Jerwick, P. Wu, M. Chen, A. Alex, Y. Ma, R. E. Tanzi, A. Li, and C. Zhou, “Drosophila Preparation and Longitudinal Imaging of Heart Function In Vivo Using Optical Coherence Microscopy (OCM),” J. Visualized Exp. 118, 55002 (2016).
[Crossref]

Chen, T.

B. Xu, N. Wang, T. Chen, and M. Li, “Empirical Evaluation of Rectified Activations in Convolutional Network,” (2015).

Chen, Z.

X. Shi, Z. Chen, H. Wang, D.-Y. Yeung, W.-K. Wong, and W.-C. Woo, “Convolutional LSTM Network: a machine learning approach for precipitation nowcasting,” in Proceedings of the 28th International Conference on Neural Information Processing Systems - Volume 1, (MIT Press, Montreal, Canada, 2015), pp. 802–810.

Chien, S.

L. T. Reiter, L. Potocki, S. Chien, M. Gribskov, and E. Bier, “A systematic analysis of human disease-associated gene sequences in Drosophila melanogaster,” Genome Res. 11(6), 1114–1125 (2001).
[Crossref]

Cho, K.

J. Chung, Ç. Gülçehre, K. Cho, and Y. Bengio, “Empirical Evaluation of Gated Recurrent Neural Networks on Sequence Modeling,” CoRR abs/1412.3555 (2014).

Choma, M.

M. Wolf, H. Amrein, J. Izatt, M. Choma, M. Reedy, and H. Rockman, “Drosophila as a model for the identification of genes causing adult human heart disease,” Proc. Natl. Acad. Sci. U. S. A. 103(5), 1394–1399 (2006).
[Crossref]

Choma, M. A.

Chung, J.

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Supplementary Material (5)

NameDescription
» Visualization 1       Segmentation video of a larval fly heart.
» Visualization 2       Segmentation video of an early pupal fly heart.
» Visualization 3       Visualization_3: Segmentation video of an adult fly heart.
» Visualization 4       Segmentation video of a pupal fly heart beating at the resting heart rate.
» Visualization 5       Segmentation video of a larval fly heart beating at the optogenetically paced rate.

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Figures (7)

Fig. 1.
Fig. 1. The OCM setup uses a super continuum light source with a central wavelength of 840 nm. A rod mirror splits the laser beam into sample and reference arms.
Fig. 2.
Fig. 2. FlyNet 2.0 structure for fruit fly heart segmentation. The left four plate blocks indicate the encoder, and the right four plate blocks indicate the decoder. Each plate block in the encoder includes the LSTM layer to extract time sequence information from fly heart videos. Time sequenced input OCM images are connected by the LSTM cells.
Fig. 3.
Fig. 3. FlyNet 2.0 segmentation results for larva heartbeat cycles (see Visualization 1). Twelve frames from Visualization 1 are shown.
Fig. 4.
Fig. 4. FlyNet 2.0 segmentation results for early pupa heartbeat cycles (see Visualization 2). Twelve frames from Visualization 2 are shown.
Fig. 5.
Fig. 5. FlyNet 2.0 segmentation results for adult heartbeat cycles (see Visualization 3). Twelve frames from Visualization 3 are shown.
Fig. 6.
Fig. 6. Fly heart OCM image, area plot, and heart rate plot with a uniform fly heartbeat frequency (see Visualization 4). (a) M-mode image generated from a 2D OCM video of an early pupa. (b) Fly heart area plot. (c) Fly heart rate plot.
Fig. 7.
Fig. 7. Fly heart OCM image, area plot, and heart rate plot with fly heartbeat frequency changes (see Visualization 5). (a) M-mode image generated from a 2D OCM video of a larva. (b) Fly heart area plot. (c) Fly heart rate plot.

Tables (2)

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Table 1. Datasets for different developmental stages of fly

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Table 2. IOU accuracy of three different network structures

Equations (2)

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g ( i , j ) = α f ( i , j ) + β ,
I O U = I p r e d i c t e d I G T I p r e d i c t e d I G T ,

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