Consistency Regularization Improves Placenta Segmentation in Fetal EPI MRI Time Series
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Abstract (arXiv)
The placenta plays a crucial role in fetal development. Automated 3D placenta segmentation from fetal EPI MRI holds promise for advancing prenatal care. This paper proposes an effective semi-supervised learning method for improving placenta segmentation in fetal EPI MRI time series. We employ consistency regularization loss that promotes consistency under spatial transformation of the same image and temporal consistency across nearby images in a time series. The experimental results show that the method improves the overall segmentation accuracy and provides better performance for outliers and hard samples. The evaluation also indicates that our method improves the temporal coherency of the prediction, which could lead to more accurate computation of temporal placental biomarkers. This work contributes to the study of the placenta and prenatal clinical decision-making. Code is available at https://github.com/firstmover/cr-seg.
Key facts. Cited by 1 paper in the map (data through 14 Oct 2026). Builds on 16 of its 25 references that are in the map. First posted on arXiv 5 Oct 2023.
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4 more
- 2020Semi-Supervised Learning for Fetal Brain MRI Quality Assessment with ROI consistencycited by 10
- 2019Placental Flattening via Volumetric Parameterizationcited by 7
- 2022Automatic Segmentation of the Placenta in BOLD MRI Time Seriescited by 5
- 2023Boundary-weighted logit consistency improves calibration of segmentation networkscited by 4
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