一致性正则化改善胎儿EPI MRI时间序列中的胎盘分割
Consistency Regularization Improves Placenta Segmentation in Fetal EPI MRI Time Series
AI总结：
本文提出一种基于一致性正则化的半监督学习方法，用于改善胎儿EPI MRI时间序列中的3D胎盘分割，提升整体准确性、异常值处理和时间连贯性。
摘要：
胎盘在胎儿发育中起着至关重要的作用。从胎儿EPI MRI中自动进行3D胎盘分割有望推进产前护理。本文提出了一种有效的半监督学习方法，用于改善胎儿EPI MRI时间序列中的胎盘分割。我们采用一致性正则化损失，该损失促进同一图像在空间变换下的一致性以及时间序列中相邻图像之间的时间一致性。实验结果表明，该方法提高了整体分割准确性，并为异常值和难样本提供了更好的性能。评估还表明，我们的方法提高了预测的时间连贯性，这可能有助于更准确地计算时间胎盘生物标志物。这项工作有助于胎盘研究和产前临床决策。代码可在 https://github.com/firstmover/cr-seg 获取。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
