Automated contour extraction for light-sheet microscopy images of zebrafish embryos based on object edge detection algorithm.
computer-assisted image analysis
digital image processing
embryonic development
fluorescence microscopy
zebrafish
Journal
Development, growth & differentiation
ISSN: 1440-169X
Titre abrégé: Dev Growth Differ
Pays: Japan
ID NLM: 0356504
Informations de publication
Date de publication:
Aug 2023
Aug 2023
Historique:
revised:
01
06
2023
received:
07
11
2022
accepted:
16
06
2023
medline:
24
8
2023
pubmed:
23
6
2023
entrez:
23
6
2023
Statut:
ppublish
Résumé
Embryo contour extraction is the initial step in the quantitative analysis of embryo morphology, and it is essential for understanding the developmental process. Recent developments in light-sheet microscopy have enabled the in toto time-lapse imaging of embryos, including zebrafish. However, embryo contour extraction from images generated via light-sheet microscopy is challenging owing to the large amount of data and the variable sizes, shapes, and textures of objects. In this report, we provide a workflow for extracting the contours of zebrafish blastula and gastrula without contour labeling of an embryo. This workflow is based on the edge detection method using a change point detection approach. We assessed the performance of the edge detection method and compared it with widely used edge detection and segmentation methods. The results showed that the edge detection accuracy of the proposed method was superior to those of the Sobel, Laplacian of Gaussian, adaptive threshold, Multi Otsu, and k-means clustering-based methods, and the noise robustness of the proposed method was superior to those of the Multi Otsu and k-means clustering-based methods. The proposed workflow was shown to be useful for automating small-scale contour extractions of zebrafish embryos that cannot be specifically labeled owing to constraints, such as the availability of microscopic channels. This workflow may offer an option for contour extraction when deep learning-based approaches or existing non-deep learning-based methods cannot be applied.
Types de publication
Journal Article
Langues
eng
Sous-ensembles de citation
IM
Pagination
311-320Subventions
Organisme : Japan Society for the Promotion of Science
ID : KAKENHI/JP16H06280
Organisme : Japan Society for the Promotion of Science
ID : KAKENHI/JP16K08456
Organisme : Japan Society for the Promotion of Science
ID : KAKENHI/JP17H06258
Organisme : National Institute for Basic Biology Collaborative Research Program for DSLM
ID : 15-706
Informations de copyright
© 2023 Japanese Society of Developmental Biologists.
Références
Abdou, I. E., & Pratt, W. K. (1979). Quantitative design and evaluation of enhancement/thresholding edge detectors. Proceedings of the IEEE, 67(5), 753-763. https://doi.org/10.1109/PROC.1979.11325
Amat, F., Höckendorf, B., Wan, Y., Lemon, W. C., McDole, K., & Keller, P. J. (2015). Efficient processing and analysis of large-scale light-sheet microscopy data. Nature Protocols, 10(11), 1679-1696. https://doi.org/10.1038/nprot.2015.111
Amat, F., Lemon, W., Mossing, D. P., McDole, K., Wan, Y., Branson, K., Myers, E. W., & Keller, P. J. (2014). Fast, accurate reconstruction of cell lineages from large-scale fluorescence microscopy data. Nature Publishing Group, 11(9), 951-958. https://doi.org/10.1038/nmeth.3036
Arthur, D., & Vassilvitskii, S. (2007). k-means++: The advantages of careful seeding. Proceedings of the Eighteenth Annual ACM-SIAM Symposium on Discrete Algorithms, 1027-1035. USA: Society for Industrial and Applied Mathematics.
Bhadouria, V. (2022). Pratt's Figure of Merit. Retrieved from MATLAB Central File Exchange. website: https://www.mathworks.com/matlabcentral/fileexchange/60473-pratt-s-figure-of-merit.
Bradley, D., & Roth, G. (2007). Adaptive thresholding using the integral image. Journal of Graphics Tools, 12(2), 13-21. https://doi.org/10.1080/2151237X.2007.10129236
Celisse, A., Marot, G., Pierre-Jean, M., & Rigaill, G. J. (2018). New efficient algorithms for multiple change-point detection with reproducing kernels. Computational Statistics & Data Analysis, 128, 200-220. https://doi.org/10.1016/j.csda.2018.07.002
Duda, R., & Hart, P. E. (1973). Pattern classification and scene analysis. Wiley.
Harris, C. R., Millman, K. J., van der Walt, S. J., Gommers, R., Virtanen, P., Cournapeau, D., Wieser, E., Taylor, J., Berg, S., Smith, N. J., Kern, R., Picus, M., Hoyer, S., van Kerkwijk, M. H., Brett, M., Haldane, A., del Río, J. F., Wiebe, M., Peterson, P., … Oliphant, T. E. (2020). Array programming with NumPy. Nature, 585(7825), 357-362. https://doi.org/10.1038/s41586-020-2649-2
He, K., Gkioxari, G., Dollár, P., & Girshick, R. B. (2017). Mask R-CNN. IEEE International Conference on Computer Vision (ICCV), 2017, 2980-2988.
Huisken, J., & Stainier, D. Y. R. (2007). Even fluorescence excitation by multidirectional selective plane illumination microscopy (mSPIM). Optics Letters, 32(17), 2608-2610. https://doi.org/10.1364/OL.32.002608
Ichikawa, T., Nakazato, K., Keller, P. J., Kajiura-Kobayashi, H., Stelzer, E. H. K., Mochizuki, A., & Nonaka, S. (2013). Live imaging of whole mouse embryos during gastrulation: Migration analyses of epiblast and mesodermal cells. PLoS One, 8(7), e64506. https://doi.org/10.1371/journal.pone.0064506
Kass, M., Witkin, A., & Terzopoulos, D. (1988). Snakes: Active contour models. International Journal of Computer Vision, 1(4), 321-331. https://doi.org/10.1007/BF00133570
Keller, P. J., Schmidt, A. D., Wittbrodt, J., & Stelzer, E. H. K. (2008). Reconstruction of zebrafish early embryonic development by scanned light sheet microscopy. Science, 322(5904), 1065-1069. https://doi.org/10.1126/science.1162493
Khan, A., Gould, S., & Salzmann, M. (2016). Segmentation of developing human embryo in time-lapse microscopy. In 2016 IEEE 13th international symposium on biomedical imaging (ISBI) (pp. 930-934). Prague, Czech Republic. https://doi.org/10.1109/ISBI.2016.7493417
Killick, R., Fearnhead, P., & Eckley, I. A. (2012). Optimal detection of changepoints with a linear computational cost. Journal of the American Statistical Association, 107(500), 1590-1598. https://doi.org/10.1080/01621459.2012.737745
Kimmel, C. B., Ballard, W. W., Kimmel, S. R., Ullmann, B., & Schilling, T. F. (1995). Stages of embryonic development of the zebrafish. Developmental Dynamics, 203(3), 253-310. https://doi.org/10.1002/aja.1002030302
Kondow, A., Ohnuma, K., Kamei, Y., Taniguchi, A., Bise, R., Sato, Y., Yamaguchi, H., Nonaka, S., & Hashimoto, K. (2020). Light-sheet microscopy-based 3D single-cell tracking reveals a correlation between cell cycle and the start of endoderm cell internalization in early zebrafish development. Development, Growth & Differentiation, 62(7-8), 495-502. https://doi.org/10.1111/dgd.12695
Laine, R. F., Jacquemet, G., & Krull, A. (2021). Imaging in focus: An introduction to denoising bioimages in the era of deep learning. The International Journal of Biochemistry & Cell Biology, 140, 106077. https://doi.org/10.1016/j.biocel.2021.106077
Li, Q., & Gong, Y. (2017). A geometric method for contour extraction of drosophila embryos. BMC Systems Biology, 11(6), 102. https://doi.org/10.1186/s12918-017-0478-1
Liao, P.-S., Chen, T.-S., & Chung, P. C. (2001). A fast algorithm for multilevel thresholding. Journal of Information Science and Engineering, 17, 713-727.
Malladi, R., Sethian, J. A., & Vemuri, B. C. (1995). Shape modeling with front propagation: A level set approach. IEEE Transactions on Pattern Analysis and Machine Intelligence, 17(2), 158-175. https://doi.org/10.1109/34.368173
McKinney, W. (2010). Data structures for statistical computing in Python. In S. van der Walt & J. Millman (Eds.), Proceedings of the 9th Python in Science Conference (pp. 56-61). SciPy. https://doi.org/10.25080/Majora-92bf1922-00a
Mizoguchi, T., Verkade, H., Heath, J. K., Kuroiwa, A., & Kikuchi, Y. (2008). Sdf1/Cxcr4 signaling controls the dorsal migration of endodermal cells during zebrafish gastrulation. Development, 135(15), 2521-2529. https://doi.org/10.1242/dev.020107
Nunez-Iglesias, J., Blanch, A. J., Looker, O., Dixon, M. W., & Tilley, L. (2018). A new python library to analyse skeleton images confirms malaria parasite remodelling of the red blood cell membrane skeleton. PeerJ, 6, e4312. https://doi.org/10.7717/peerj.4312
Nüsslein-Volhard, C., & Dahm, R. (2002). Zebrafish. Oxford University Press.
Otsu, N. (1978). A threshold selection method from gray-level histogram. IEEE Trans. Syst. Man Cybern, SMC-9(1), 62-66.
Parker, J. R. (2011). Algorithms for image processing and computer vision (2nd ed.). Wiley Computer Pub.
Peng, S., Jiang, W., Pi, H., Li, X., Bao, H., & Zhou, X. (2020). Deep Snake for Real-Time Instance Segmentation. arXiv. Retrieved from http://arxiv.org/abs/2001.01629.
Pratt, W. K. (2006). Digital image processing: PIKS scientific inside (Fourth ed.). John Wiley & Sons, Inc. Retrieved from https://www.onlinelibrary.wiley.com/doi/book/10.1002/0470097434.
Ronneberger, O., Fischer, P., & Brox, T. (2015). U-net: Convolutional networks for biomedical image segmentation. In N. Navab, J. Hornegger, W. M. Wells, & A. F. Frangi (Eds.), Medical image computing and computer-assisted intervention - MICCAI 2015 (pp. 234-241). Springer International Publishing. https://doi.org/10.1007/978-3-319-24574-4_28
Tomer, R., Khairy, K., Amat, F., & Keller, P. J. (2012). Quantitative high-speed imaging of entire developing embryos with simultaneous multiview light-sheet microscopy. Nature Methods, 9(7), 755-763. https://doi.org/10.1038/nmeth.2062
Truong, C., Oudre, L., & Vayatis, N. (2020). Selective review of offline change point detection methods. Signal Processing, 167, 107299. https://doi.org/10.1016/j.sigpro.2019.107299
van der Walt, S., Schönberger, J. L., Nunez-Iglesias, J., Boulogne, F., Warner, J. D., Yager, N., Scikit-image contributors, Gouillart, E., & Yu, T. (2014). Scikit-image: Image processing in Python. PeerJ, 2, e453. https://doi.org/10.7717/peerj.453
Waskom, M. (2021). seaborn: Statistical data visualization. Journal of Open Source Software, 6(60), 3021. https://doi.org/10.21105/joss.03021
Westerfield, M. (2000). The zebrafish book. A guide for the laboratory use of zebrafish (Danio rerio) (4th ed.). University of Oregon Press.
Wu, Y., Ghitani, A., Christensen, R., Santella, A., Du, Z., Rondeau, G., Bao, Z., Colón-Ramos, D., & Shroff, H. (2011). Inverted selective plane illumination microscopy (iSPIM) enables coupled cell identity lineaging and neurodevelopmental imaging in Caenorhabditis elegans. Proceedings of the National Academy of Sciences, 108(43), 17708-17713. https://doi.org/10.1073/pnas.1108494108