448 Deformable Medial Modeling to Generate Novel Shape Features of the Placenta Using Automated versus Manual Segmentations

OBJECTIVES/GOALS: In this study, we implemented deformable medial modeling as a morphometric approach in first trimester placentas to characterize morphometric differences between fully automated and manual segmentations. METHODS/STUDY POPULATION: Twenty placentas from singleton pregnancies between...

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Bibliographic Details
Published in:Journal of clinical and translational science 2024-04, Vol.8 (s1), p.132-133
Main Authors: Arenas, Gabriel, Pouch, Alison, Oguz, Ipek, Oguz, Baris, Byram, Brett, Yao, Xing, Schwartz, Nadav
Format: Article
Language:English
Subjects:
Automation
Biomarkers
Empathy
Flow cytometry
Genetic engineering
Homeostasis
Inflammatory bowel disease
Insomnia
Machine learning
Muscle pain
Neural networks
Placenta
Plasma
Population studies
Precision Medicine/Health
Proteins
Remission (Medicine)
Respiratory distress syndrome
Statistical analysis
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Summary:OBJECTIVES/GOALS: In this study, we implemented deformable medial modeling as a morphometric approach in first trimester placentas to characterize morphometric differences between fully automated and manual segmentations. METHODS/STUDY POPULATION: Twenty placentas from singleton pregnancies between 11-14 weeks’ gestation were manually and automatically segmented from 3D ultrasound volumes. Automated segmentations were produced by a trained convolutional neural network pipeline. Dice overlap scores and volumes were computed between manual and automated segmentations. Deformable medial modeling was applied to both manual and automated segmentations to produce the following metrics: maternal and chorionic surface areas (SA), thickness, circumference, and diameter along the generated medial surface. Placental non-planarity was also determined as the greatest medial surface height difference. A paired t-test and simple linear regression was performed between manual and automated segmentations for each shape metric. RESULTS/ANTICIPATED RESULTS: Mean placental volume measurements between manual and automated segmentations were similar, with a percent difference of 3.28% and a mean Dice overlap score of 0.85 ± 0.07. There were strong, statistically significant (p
ISSN:2059-8661
2059-8661
DOI:10.1017/cts.2024.383