Authors:
Wided Miled
1
;
2
;
Sana Chtourou
3
;
Nozha Chakroun
3
and
Khadija Berjeb
3
Affiliations:
1
LIMTIC Laboratory, Higher Institute of Computer Science, University of Tunis El-Manar, Ariana, Tunisia
;
2
National Institute of Applied Science and Technology, University of Carthage, Centre Urbain Nord, Tunisia
;
3
University of Medicine of Tunis, Lab. of Reproductive Biology and Cytogenetic, Aziza Othmana Hospital, Tunisia
Keyword(s):
IVF, Pronuclei Detection, Embryo Selection, Computer Vision, Classification, Deep Learning, Sequential Models.
Abstract:
In Vitro Fertilisation (IVF) is a procedure used to overcome a range of fertility issues, giving many couples the chance of having a baby. Accurate selection of embryos with the highest implantation potentials is a necessary step toward enhancing the effectiveness of IVF. The detection and determination of pronuclei number during the early stages of embryo development in IVF treatments help embryologists with decision-making regarding valuable embryo selection for implantation. Current manual visual assessment is prone to observer subjectivity and is a long and difficult process. In this study, we build a CNN-LSTM deep learning model to automatically detect pronuclear-stage in IVF embryos, based on Time-Lapse Images (TLI) of their early development stages. The experimental results proved possible the automation of pronuclei determination as the proposed deep learning based method achieved a high accuracy of 85% in the detection of pronuclear-stage embryo.