He, Chloe;
Karpaviciute, Neringa;
Hariharan, Rishabh;
Jacques, Celine;
Chambost, Jerome;
Malmsten, Jonas;
Zaninovic, Nikica;
... Vasconcelos, Francisco; + view all
(2024)
Embryo Graphs: Predicting Human Embryo Viability from 3D Morphology.
In: Linguraru, MG and Dou, Q and Feragen, A and Giannarou, S and Glocker, B and Lekadir, K and Schnabel, JA, (eds.)
Medical Image Computing and Computer Assisted Intervention – MICCAI 2024.
(pp. pp. 80-90).
Springer: Cham, Switzerland.
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Abstract
Embryo selection is a critical step in the process of in-vitro fertilisation in which embryologists choose the most viable embryos for transfer into the uterus. In recent years, numerous works have used computer vision to perform embryo selection. However, many of these works have neglected the fact that the embryo is a 3D structure, instead opting to analyse embryo images captured at a single focal plane. In this paper we present a method for the 3D reconstruction of cleavage-stage human embryos. Through a user study, we validate that our reconstructions align with expert assessments. Furthermore, we demonstrate the utility of our approach by generating graph representations that capture biologically relevant features of the embryos. In pilot experiments, we train a graph neural network on these representations and show that it outperforms existing methods in predicting live birth from euploid embryo transfers. Our findings suggest that incorporating 3D reconstruction and graph-based analysis can improve automated embryo selection.
| Type: | Proceedings paper |
|---|---|
| Title: | Embryo Graphs: Predicting Human Embryo Viability from 3D Morphology |
| Event: | 27th International Conference on Medical Image Computing and Computer Assisted Intervention (MICCAI) |
| Location: | MOROCCO, Palmeraie Conf Ctr, Marrakesh |
| Dates: | 6 Oct 2024 - 10 Oct 2024 |
| ISBN-13: | 978-3-031-72082-6 |
| DOI: | 10.1007/978-3-031-72083-3_8 |
| Publisher version: | https://doi.org/10.1007/978-3-031-72083-3_8 |
| Language: | English |
| Additional information: | This version is the author accepted manuscript. For information on re-use, please refer to the publisher’s terms and conditions. |
| Keywords: | 3D Reconstruction, Computer Science, Computer Science, Artificial Intelligence, Computer Science, Theory & Methods, Embryology, Engineering, Engineering, Biomedical, Graph Neural Networks, Microscopy, Science & Technology, Technology |
| UCL classification: | UCL UCL > Provost and Vice Provost Offices > UCL BEAMS UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Engineering Science > Dept of Computer Science |
| URI: | https://discovery.ucl.ac.uk/id/eprint/10203451 |
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