  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.
