Cartesian genetic programming for image analysis of the developing drosophila eye

Capa

Citar

Texto integral

Acesso aberto Acesso aberto
Acesso é fechado Acesso está concedido
Acesso é fechado Somente assinantes

Resumo

Automatic feature extraction methods have gained increasing attention in modern image processing. The confocal images of the single-layered epithelium of the developing Drosophila eye may form an excellent model system to develop methods for complex feature extraction. The aim of this work was to explore Cartesian genetic programming for determination of the boundaries of ommatidia, the light-sensitive units in the presumptive eye region. Application of Cartesian genetic programming for the analysis of Fasciclin III expression has shown good results. This opens interesting perspectives for further use of this technology in the automatic analysis of confocal images.

Sobre autores

N. Danilov

Peter the Great St. Petersburg Polytechnic University

St. Petersburg, Russia

K. Kozlov

Peter the Great St. Petersburg Polytechnic University

St. Petersburg, Russia

S. Surkova

Peter the Great St. Petersburg Polytechnic University

Email: surkova_syu@spbstu.ru
St. Petersburg, Russia

M. Samsonova

Peter the Great St. Petersburg Polytechnic University

St. Petersburg, Russia

Bibliografia

  1. И. А. Русанова, в сб. матер. Всероссийской школы-семинара (Саратов, 01 октября 2018 г.), под ред. Д. А. Усанова (Изд-во "Саратовский источник", Саратов, 2018), сс. 78-81.
  2. К. Н. Козлов, Е. В. Голубкова, Л. А. Мамон и др., Биофизика, 67, 283 (2022). DOI: 10.31857/ S0006302922020119
  3. J. P. Kumar, Devel. Dynamics, 241, 136 (2012). doi: 10.1002/dvdy.23707
  4. S. Surkova, J. Gorne, S. Nuzhdin, et al., Devel. Biol., 476, 41 (2021). doi: 10.1016/j.ydbio.2021.03.005.
  5. J. Y. Roignant and J. E Treisman, Int. J. Devel. Biol. 53, 795 (2009). doi: 10.1387/ijdb.072483jr
  6. J. E. Treisman, Wiley Interdisc. Rev. Devel. Biol., 2, 545 (2013). doi: 10.1002/wdev.100
  7. S. Ali, S. A. Signor, K. Kozlov, et al., Evolution & Development, 21, 157 (2019). doi: 10.1111/ede.12283
  8. L. Liu, L. Shao and X. Li, Inf. Sci., 316, 567 (2015). doi: 10.1016/j.ins.2014.06.030
  9. A. Lensen, H. Al-Sahaf, M. Zhang, et al., in EuroGP 2016. LNCS, Ed. by M. I. Heywood, J. McDermott, M. Castelli et al. (Springer, Cham, 2016), v. 9594, pp. 51-67. doi: 10.1007/978-3-319-30668-1_4
  10. 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, Cham, 2020), pp. 63-78. doi: 10.1007/978-3-030-58115-2_5
  11. C. B. Perez and G. Olague, Intell. Data Anal., 17, 561 (2013). doi: 10.3233/IDA-130594
  12. W. A. Albukhanajer and J. A. Briffa, IEEE Trans. Cybern., 45, 1757 (2015). doi: 10.1109/TCYB. 2014.2360074
  13. 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.
  14. M. A. Kramer, AIChE J. 37, 233 (1991). doi: 10.1002/aic.690370209
  15. 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
  16. P. Vincent, H. Larochelle, Y. Bengio, et al., in Proc.Int. Conf. on Machine Learning, ICML 2008 (2008). pp. 1096-1103. doi: 10.1145/1390156.1390294
  17. P. M. Snow, A. J. Bieber, and C. S. Goodman, Cell, 59, 313 (1989). doi: 10.1016/0092-8674(89)90293-6
  18. K. Kozlov, A. Pisarev, J. Kaandorp, et al., in Abstr. Bookof the 9th Int. Conf. Syst. Biol. (Goteborg, 2008), p. 191.

Arquivos suplementares

Arquivos suplementares
Ação
1. JATS XML

Declaração de direitos autorais © Russian Academy of Sciences, 2023