Abstract
Methods of automatic feature extraction attract increasing attention when solving modern image processing problems. Confocal images of the single-layer epithelium of the developing eye of the fruit fly drosophila are a convenient model system for the development of methods for the identification of complex features. The aim of this study was to use Cartesian genetic programming to identify the boundaries of ommatidia, the photosensitive units of the developing eye. The use of Cartesian genetic programming to analyze the expression patterns of the Fasciclin III marker showed good results. This provides interesting prospects for further application of this technology for the automatic analysis of images obtained using confocal microscopy.
REFERENCES
I. A. Rusanova, in Collection of Papers of the All-Russian School-Seminar, Ed. by D. A. Usanov (Saratovskii Istochnik, Saratov, 2018), pp. 78–81.
K. N. Kozlov, E. V. Golubkova, L. A. Mamon, et al., Biofizika 67, 283 (2022). https://doi.org/10.31857/S0006302922020119
J. P. Kumar, Dev. Dyn. 241, 136 (2012). https://doi.org/10.1002/dvdy.23707
S. Surkova, J. Görne, S. Nuzhdin, et al., Dev. Biol. 476, 41 (2021). https://doi.org/10.1016/j.ydbio.2021.03.005
J. Y. Roignant and J. E Treisman, Int. J. Dev. Biol. 53, 795 (2009). https://doi.org/10.1387/ijdb.072483jr
J. E. Treisman, Wiley Interdiscip. Rev.: Dev. Biol. 2, 545 (2013). https://doi.org/10.1002/wdev.100
S. Ali, S. A. Signor, K. Kozlov, et al., Evol. Dev. 21, 157 (2019). https://doi.org/10.1111/ede.12283
L. Liu, L. Shao and X. Li, Inf. Sci. 316, 567 (2015). https://doi.org/10.1016/j.ins.2014.06.030
A. Lensen, H. Al-Sahaf, M. Zhang, et al., in EuroGP 2016. LNCS, Ed. by M. I. Heywood, J. McDermott, M. Castelli (Springer-Verlag, Cham, 2016), Vol. 9594, pp. 51–67. https://doi.org/10.1007/978-3-319-30668-1_4
S. Ruberto, V. Terragni, and J. Moore, in Parallel Problem Solving from Nature. Lecture Notes in Computer Science Image Feature Learning with Genetic Programming (Springer-Verlag, Cham, 2020), pp. 63–78. https://doi.org/10.1007/978-3-030-58115-2_5
C. B. Perez and G. Olague, Intell. Data Anal. 17, 561 (2013). https://doi.org/10.3233/IDA-130594
W. A. Albukhanajer and J. A. Briffa, IEEE Trans. Cybern. 45, 1757 (2015).https://doi.org/10.1109/TCYB.2014.2360074
J. F. Miller, P. Thomson, and T.C. Fogarty, in Genetic Algorithms and Evolution Strategies in Engineering and Computer Science: Recent Advancements and Industrial Applications, Ed. by D. Quagliarella, J. Periaux, C. Poloni, and G. Winter (Wiley, 1998), pp. 105–131.
M. A. Kramer, AIChE J. 37, 233 (1991). https://doi.org/10.1002/aic.690370209
A. Makhzani and B. J. Frey, in Advances in Neural Information Processing Systems, Ed. by C. Cortes, N. Lawrence, D. Lee, et al. (2015), pp. 2791–2799
P. Vincent, H. Larochelle, Y. Bengio, et al., in Proceedings of the International Conference on Machine Learning, ICML 2008 (2008), pp. 1096–1103. https://doi.org/10.1145/1390156.1390294
P. M. Snow, A. J. Bieber, and C. S. Goodman, Cell 59, 313 (1989). https://doi.org/10.1016/0092-8674(89)90293-6
K. Kozlov, A. Pisarev, J. Kaandorp, et al., in Proceedings of the 9th International Conference on Systems Biology (Goteborg, 2008).
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The work was carried out with the financial support of the Russian Foundation for Basic Research, project no. 20-04-01047-a.
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This paper does not describe any studies using humans and animals as objects.
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Translated by E. Puchkov
Abbreviation: CGP, Cartesian genetic programming.
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Danilov, N.A., Kozlov, K.N., Surkova, S.Y. et al. Cartesian Genetic Programming for Image Analysis of the Developing Drosophila Eye. BIOPHYSICS 68, 462–467 (2023). https://doi.org/10.1134/S0006350923030077
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DOI: https://doi.org/10.1134/S0006350923030077