Blood oxygen level dependent (BOLD) MRI time series with maternal hyperoxia can assess placental oxygenation and function. Measuring precise BOLD changes in the placenta requires accurate temporal placental segmentation and is confounded by fetal and maternal motion, contractions, and hyperoxia-induced intensity changes. Current BOLD placenta segmentation methods warp a manually annotated subject-specific template to the entire time series. However, as the placenta is a thin, elongated, and highly non-rigid organ subject to large deformations and obfuscated edges, existing work cannot accurately segment the placental shape, especially near boundaries. In this work, we propose a machine learning segmentation framework for placental BOLD MRI and apply it to segmenting each volume in a time series. We use a placental-boundary weighted loss formulation and perform a comprehensive evaluation across several popular segmentation objectives. Our model is trained and tested on a cohort of 91 subjects containing healthy fetuses, fetuses with fetal growth restriction, and mothers with high BMI. Biomedically, our model performs reliably in segmenting volumes in both normoxic and hyperoxic points in the BOLD time series. We further find that boundary-weighting increases placental segmentation performance by 8.3% and 6.0% Dice coefficient for the cross-entropy and signed distance transform objectives, respectively. Our code and trained model is available at https://github.com/mabulnaga/automatic-placenta-segmentation.

翻译：血氧水平依赖（BOLD）MRI时间序列结合母亲高氧干预可评估胎盘氧合状况与功能。准确测量胎盘中BOLD信号变化需要对胎盘进行精确的时间序列分割，但这一过程受到胎儿与母亲运动、子宫收缩以及高氧诱发的信号强度变化等因素干扰。现有BOLD胎盘分割方法通过将人工标注的个体化模板变形配准至整个时间序列。然而，胎盘作为形态细长、高度非刚性的器官，存在大形变且边缘模糊，现有方法难以准确分割胎盘形状，尤其边界区域。本文提出一种面向胎盘BOLD MRI的机器学习分割框架，并应用于时间序列中每个体素的分割。我们采用基于胎盘边界加权的损失函数，并在多种主流分割目标函数下进行全面评估。模型在包含91例受试者的队列中进行训练与测试，涵盖健康胎儿、胎儿生长受限胎儿及高BMI母亲。在生物医学应用方面，该模型在BOLD时间序列的常氧和高氧阶段均能可靠分割体素。进一步研究发现，边界加权方法能使交叉熵与符号距离变换目标函数的Dice系数分别提升8.3%和6.0%。我们的代码与预训练模型已开源至：https://github.com/mabulnaga/automatic-placenta-segmentation