Journal
MEDICAL IMAGE ANALYSIS
Volume 72, Issue -, Pages -Publisher
ELSEVIER
DOI: 10.1016/j.media.2021.102145
Keywords
Placental MRI; Relaxometry; Acquisition; Pre-eclampsia; U-Net; Segmentation; Regression; Gaussian process
Categories
Funding
- NIH Human Placenta Project [1U01HD087202-01]
- European Research Council under the European Union [319456]
- Wellcome EPSRC Centre for Medical Engineering at Kings College London [WT 203148/Z/16/Z]
- Wellcome Trust Sir Henry Wellcome Fellowship
- Open Science Enrichment award
- MRC strategic grant [MR/K006355/1]
- National Institute for Health Research (NIHR) Biomedical Research Centre based at Guy's and St Thomas' NHS Foundation Trust and King's College London
- MRC [MR/K006355/1] Funding Source: UKRI
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The study proposes a fully automatic pipeline for predicting the biological age and health of the placenta from rapid MRI scans. Automatic segmentation shows lower performance than manual segmentation but is sufficient for estimating placental mean T2*. The prediction of placental health is able to differentiate cases of placental insufficiency between 27 and 33 weeks.
Purpose: Artificial-intelligence population-based automated quantification of placental maturation and health from a rapid functional Magnetic Resonance scan. The placenta plays a crucial role for any successful human pregnancy. Deviations from the normal dynamic maturation throughout gestation are closely linked to major pregnancy complications. Antenatal assessment in-vivo using T2 * relaxometry has shown great promise to inform management and possible interventions but clinical translation is hampered by time consuming manual segmentation and analysis techniques based on comparison against normative curves over gestation. Methods: This study proposes a fully automatic pipeline to predict the biological age and health of the placenta based on a free-breathing rapid (sub-30 second) T2 * scan in two steps: Automatic segmentation using a U-Net and a Gaussian process regression model to characterize placental maturation and health. These are trained and evaluated on 108 3T MRI placental data sets, the evaluation included 20 high-risk pregnancies diagnosed with pre-eclampsia and/or fetal growth restriction. An independent cohort imaged at 1.5 T is used to assess the generalization of the training and evaluation pipeline. Results: Across low-and high-risk groups, automatic segmentation performs worse than inter-rater performance (mean Dice coefficients of 0.58 and 0.68, respectively) but is sufficient for estimating placental mean T2 * (0.986 Pearson Correlation Coefficient). The placental health prediction achieves an excellent ability to differentiate cases of placental insufficiency between 27 and 33 weeks. High abnormality scores correlate with low birth weight, premature birth and histopathological findings. Retrospective application on a different cohort imaged at 1.5 T illustrates the ability for direct clinical translation. Conclusion: The presented automatic pipeline facilitates a fast, robust and reliable prediction of placental maturation. It yields human-interpretable and verifiable intermediate results and quantifies uncertainties on the cohort-level and for individual predictions. The proposed machine-learning pipeline runs in close to real-time and, deployed in clinical settings, has the potential to become a cornerstone of diagnosis and intervention of placental insufficiency. APPLAUSE generalizes to an independent cohort imaged at 1.5 T, demonstrating robustness to different operational and clinical environments. (c) 2021 The Authors. Published by Elsevier B.V. This is an open access article under the CC BY license ( http://creativecommons.org/licenses/by/4.0/ )
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