# Consistency Regularization Improves Placenta Segmentation in Fetal EPI MRI Time Series

arXiv: https://arxiv.org/abs/2310.03870 · HTML: https://mlsanity.com/paper/2310.03870/

## Key facts

- Cited by 1 papers 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.

## Builds on (most cited first, 16 of 16)

- [Adam: A Method for Stochastic Optimization](https://mlsanity.com/paper/1412.6980/) (2014)
- [U-Net: Convolutional Networks for Biomedical Image Segmentation](https://mlsanity.com/paper/1505.04597/) (2015)
- [Exploring Simple Siamese Representation Learning](https://mlsanity.com/paper/2011.10566/) (2020)
- [VoxelMorph: A Learning Framework for Deformable Medical Image Registration](https://mlsanity.com/paper/1809.05231/) (2018)
- [Fully-Convolutional Siamese Networks for Object Tracking](https://mlsanity.com/paper/1606.09549/) (2016)
- [Self-Supervised Pre-Training of Swin Transformers for 3D Medical Image Analysis](https://mlsanity.com/paper/2111.14791/) (2021)
- [Transformation Consistent Self-ensembling Model for Semi-supervised Medical Image Segmentation](https://mlsanity.com/paper/1903.00348/) (2019)
- [3D Self-Supervised Methods for Medical Imaging](https://mlsanity.com/paper/2006.03829/) (2020)
- [Unsupervised domain adaptation in brain lesion segmentation with adversarial networks](https://mlsanity.com/paper/1612.08894/) (2016)
- [Semi-Supervised Brain Lesion Segmentation with an Adapted Mean Teacher Model](https://mlsanity.com/paper/1903.01248/) (2019)
- [Local Spatiotemporal Representation Learning for Longitudinally-consistent Neuroimage Analysis](https://mlsanity.com/paper/2206.04281/) (2022)
- [Semi-Supervised Deep Learning for Fully Convolutional Networks](https://mlsanity.com/paper/1703.06000/) (2017)
- [Semi-Supervised Learning for Fetal Brain MRI Quality Assessment with ROI consistency](https://mlsanity.com/paper/2006.12704/) (2020)
- [Placental Flattening via Volumetric Parameterization](https://mlsanity.com/paper/1903.05044/) (2019)
- [Automatic Segmentation of the Placenta in BOLD MRI Time Series](https://mlsanity.com/paper/2208.02895/) (2022)
- [Boundary-weighted logit consistency improves calibration of segmentation networks](https://mlsanity.com/paper/2307.08163/) (2023)

## Built on by (most cited first, 1 of 1)

- [Fetuses Made Simple: Modeling and Tracking of Fetal Shape and Pose](https://mlsanity.com/paper/2506.17858/) (2025)

## 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.