Table of Contents Author Guidelines Submit a Manuscript
Computational and Mathematical Methods in Medicine
Volume 2014, Article ID 628312, 7 pages
Research Article

Automatic Blastomere Recognition from a Single Embryo Image

1College of Information Science and Technology, BNU, Beijing 100875, China
2Assisted Reproductive Medical Center, Navy General Hospital, Beijing 100048, China
3Obstetrics and Gynecology Department, Navy General Hospital, Beijing 100048, China

Received 7 May 2014; Accepted 23 June 2014; Published 14 July 2014

Academic Editor: Shenyong Chen

Copyright © 2014 Yun Tian et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Linked References

  1. S. J. Dennis, M. A. Thomas, D. B. Williams, and J. C. Robins, “Embryo morphology score on day 3 is predictive of implantation and live birth rates,” Journal of Assisted Reproduction and Genetics, vol. 23, no. 4, pp. 171–175, 2006. View at Publisher · View at Google Scholar
  2. A. E. Baxter Bendus, J. F. Mayer, S. K. Shipley, and W. H. Catherino, “Interobserver and intraobserver variation in day 3 embryo grading,” Fertility and Sterility, vol. 86, no. 6, pp. 1608–1615, 2006. View at Google Scholar
  3. C. Manna, L. Nanni, A. Lumini et al., “Artificial intelligence techniques for embryo and oocyte classification,” Reproductive BioMedicine Online, vol. 26, no. 1, pp. 42–49, 2013. View at Publisher · View at Google Scholar
  4. E. Santos Filho, J. A. Noble, and D. Wells, “A review on automatic analysis of human embryo microscope images,” The Open Biomedical Engineering Journal, vol. 4, pp. 170–177, 2010. View at Google Scholar
  5. C. W. Connie, E. L. Kevin, L. B. Nancy et al., “Non-invasive imaging of human embryos before embryonic genome activation predicts development to the blastocyst stage,” Nature Biotechnology, vol. 28, no. 10, pp. 1115–1124, 2010. View at Google Scholar
  6. C. Siristatidis, A. Pouliakis, C. Chrelias et al., “Artificial intelligence in IVF: a need,” Systems Biology in Reproductive Medicine, vol. 57, no. 4, pp. 179–185, 2011. View at Publisher · View at Google Scholar
  7. G. Paternot, S. Debrock, D. de Neubourg et al., “Semi-automated morphometric analysis of human embryos can reveal correlations between total embryo volume and clinical pregnancy,” Human Reproduction, vol. 28, no. 3, pp. 627–633, 2013. View at Publisher · View at Google Scholar
  8. E. van Royen, K. Mangelschots, D. de Neubourg et al., “Characterization of a top quality embryo, a step towards single-embryo transfer,” Human Reproduction, vol. 14, no. 9, pp. 2345–2349, 1999. View at Publisher · View at Google Scholar
  9. N. N. Desai, J. Goldstein, Y. D. Rowland et al., “Morphological evaluation of human embryos and derivation of an embryo quality scoring system specific for day 3 embryos: a preliminary study,” Human Reproduction, vol. 15, no. 10, pp. 2190–2196, 2000. View at Publisher · View at Google Scholar
  10. A. Giusti, G. Corani, L. Gambardella et al., “Blastomere segmentation and 3d morphology measurements of early embryos from hoffman modulation contrast image stacks,” in Proceedings of the IEEE International Symposium on Biomedical Imaging: From Nano to Macro, pp. 1261–1264, 2010.
  11. U. D. Pedersen, O. F. Olsen, and H. N. Olsen, “A multiphase variational level set approach for modelling human embryos,” in Proceedings of the 2nd IEEE Workshop on Variational, Geometric and Level Set Methods, pp. 25–32, 2003.
  12. F. Ning, D. Delhomme, Y. LeCun et al., “Toward automatic phenotyping of developing embryos from videos,” IEEE Transactions on Image Processing, vol. 14, no. 9, pp. 1360–1371, 2005. View at Google Scholar
  13. E. Santos Filho, J. Noble A, M. Poli et al., “A method for semi-automatic grading of human blastocyst microscope images,” Human Reproduction, vol. 27, no. 9, pp. 2641–2648, 2012. View at Google Scholar
  14. D. A. Morales, E. Bengoetxea, P. Larrañaga et al., “Bayesian classification for the selection of in vitro human embryos using morphological and clinical data,” Computer Methods and Programs in Biomedicine, vol. 90, no. 2, pp. 104–116, 2008. View at Publisher · View at Google Scholar
  15. C. Hnida, E. Engenheio, and S. Ziebe, “Computer-controlled, multilevel, morphometric analysis of blastomere size as biomarker of fragmentation and multinuclearity in human embryos,” Human Reproduction, vol. 19, no. 2, pp. 288–293, 2004. View at Publisher · View at Google Scholar
  16. A. Uyar, A. Bener, H. Ciray et al., “A frequency based encoding technique for transformation of categorical variables in mixed IVF dataset,” in Proceedings of 31st Annual International Conference of the IEEE EMBS, pp. 6214–6217, 2009.
  17. P. Braude, “Selecting the “best” embryos: prospects for improvement,” Reproductive BioMedicine Online, vol. 27, no. 6, pp. 644–653, 2013. View at Google Scholar
  18. R. Machtinger and C. Racowsky, “Morphological systems of human embryo assessment and clinical evidence,” Reproductive BioMedicine Online, vol. 26, no. 3, pp. 210–221, 2013. View at Google Scholar
  19. D. J. Kerbyson and T. J. Atherton, “Circle detection using Hough transform filters,” in Proceedings of the 5th International Conference on Image Processing and Its Applications, pp. 370–374, 1995.