Consistency Regularization Improves Placenta Segmentation in Fetal EPI MRI Time Series

TL;DR

This paper introduces a semi-supervised consistency regularization approach that enhances placenta segmentation accuracy and temporal coherence in fetal EPI MRI time series, aiding prenatal diagnosis.

Contribution

It presents a novel semi-supervised method using consistency regularization for improved placenta segmentation in fetal MRI, especially for outliers and hard samples.

Findings

Improved segmentation accuracy over baseline methods.

Enhanced temporal consistency in predictions.

Better performance on outliers and difficult samples.

Abstract

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.