Yuan, Z;
Schabel, MC;
David, AL;
Roberts, VHJ;
Melbourne, A;
(2025)
The Effect of Deep Learning Segmentation on Placental T2∗ Estimation.
In:
Proceedings - 2025 IEEE 22nd International Symposium on Biomedical Imaging (ISBI).
(pp. pp. 1-5).
IEEE: Houston, TX, USA.
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Abstract
The placenta plays a crucial role in ensuring adequate oxygen delivery to the fetus. Despite being one of the most important organs in pregnancy, the placenta remains poorly understood. Research into placental developmental structure and function has accelerated in recent years. The use of a preclinical pregnant nonhuman primate model allows researchers to quantify blood and oxygen transfer through the placenta and potentially improve our understanding of it. Furthermore, deploying Magnetic Resonance Imaging (MRI) to better understand the explicit function and mechanism of the placental vascular supply has become commonplace. Among these MRI techniques, T2, T2* and diffusivity are the most commonly deployed for accessing the pseudo-quantitative physical properties of the placenta. To enable better analysis of the placenta from MR images, and reduce the labour-intensive workload of manually delineating regions of interest, here we deployed a robust deep learning model trained on T2* MR images acquired from pregnant rhesus macaques, with the ground truth placenta segmentation drawn by our experts. After obtaining the trained model, we experimented with three segmentation fusion strategies aimed at merging the estimated segmentations from multi-echo times into one final outcome. For each fusion method, we conducted experiments to evaluate the effect of the automated segmentation on T2* estimation, including varying the echo time and the number of imaging slices. The best Dice score (averaging fusion method over 6 segmentations from different TE in one scan) reached 0.869. This work highlights the robustness of our trained segmentation model on nonhuman primate datasets and its potential for application in future preclinical studies. With appropriate fine-tuning, it could also be adapted for analysing human placental datasets.
| Type: | Proceedings paper |
|---|---|
| Title: | The Effect of Deep Learning Segmentation on Placental T2∗ Estimation |
| Event: | 2025 IEEE 22nd International Symposium on Biomedical Imaging (ISBI) |
| Dates: | 14 Apr 2025 - 17 Apr 2025 |
| ISBN-13: | 979-8-3315-2052-6 |
| Open access status: | An open access version is available from UCL Discovery |
| DOI: | 10.1109/ISBI60581.2025.10980703 |
| Publisher version: | https://doi.org/10.1109/ISBI60581.2025.10980703 |
| 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: | Pregnancy, Deep learning, Image segmentation, Translation, Placenta, Magnetic resonance imaging, Pipelines, Estimation, Manuals, Robustness |
| UCL classification: | UCL UCL > Provost and Vice Provost Offices > School of Life and Medical Sciences UCL > Provost and Vice Provost Offices > School of Life and Medical Sciences > Faculty of Population Health Sciences > UCL EGA Institute for Womens Health UCL > Provost and Vice Provost Offices > School of Life and Medical Sciences > Faculty of Population Health Sciences > UCL EGA Institute for Womens Health > Maternal and Fetal Medicine |
| URI: | https://discovery.ucl.ac.uk/id/eprint/10209864 |
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