| Title |
Towards the automation of early-stage human embryo development detection / |
| Authors |
Raudonis, Vidas ; Paulauskaite‑Taraseviciene, Agne ; Sutiene, Kristina ; Jonaitis, Domas |
| DOI |
10.1186/s12938-019-0738-y |
| Full Text |
|
| Is Part of |
Biomedical engineering online.. London : BioMed Central. 2019, vol. 18, art. no. 120, p. 1-20.. ISSN 1475-925X |
| Keywords [eng] |
deep learning ; location detection ; embryo development ; image recognition ; multi-class prediction |
| Abstract [eng] |
Infertility and subfertility affect a significant proportion of humanity. Assisted reproductive technology has been proven capable of alleviating infertility issues. In vitro fertilisation is one such option whose success is highly dependent on the selection of a high-quality embryo for transfer. This is typically done manually by analysing embryos under a microscope. However, evidence has shown that the success rate of manual selection remains low. The use of new incubators with integrated time-lapse imaging system is providing new possibilities for embryo assessment. As such, we address this problem by proposing an approach based on deep learning for automated embryo quality evaluation through the analysis of time-lapse images. Automatic embryo detection is complicated by the topological changes of a tracked object. Moreover, the algorithm should process a large number of image files of different qualities in a reasonable amount of time. |
| Published |
London : BioMed Central |
| Type |
Journal article |
| Language |
English |
| Publication date |
2019 |
| CC license |
|
