用深度学习自动分割胎儿肺部，评估生长受限胎儿肺成熟度
Deep Learning-Based Fetal Lung Segmentation from Diffusion-weighted MRI Images and Lung Maturity Evaluation for Fetal Growth Restriction
- 基于3D nnU-Net实现胎儿肺部自动分割
- 平均Dice系数达82.14%，与人工分割结果无显著差异
- 全流程自动化，适合产科临床快速决策
胎儿肺成熟度是预测新生儿预后及产后干预需求的关键指标，尤其对胎儿生长受限的妊娠尤为重要。体素内不相干运动（IVIM）分析在无创评估胎儿肺发育方面表现良好，但依赖人工分割，耗时长，限制了临床应用。本文提出一种基于深度学习的胎儿肺成熟度评估流程，包含一个深度学习肺部分割模型和模型拟合的成熟度评估模块。使用4D扩散加权MRI基线帧中手动分割的图像训练3D nnU-Net模型，分割性能稳健，平均Dice系数达82.14%。基于nnU-Net预测和人工分割结果进行体素级模型拟合，量化反映组织微结构和灌注的IVIM参数。结果显示两者无显著差异。本研究证明了全自动胎儿肺成熟度评估流程的可行性，可支持临床决策。
原文摘要 · Abstract (English)
Fetal lung maturity is a critical indicator for predicting neonatal outcomes and the need for post-natal intervention, especially for pregnancies affected by fetal growth restriction. Intra-voxel incoherent motion analysis has shown promising results for non-invasive assessment of fetal lung development, but its reliance on manual segmentation is time-consuming, thus limiting its clinical applicability. In this work, we present an automated lung maturity evaluation pipeline for diffusion-weighted magnetic resonance images that consists of a deep learning-based fetal lung segmentation model and a model-fitting lung maturity assessment. A 3D nnU-Net model was trained on manually segmented images selected from the baseline frames of 4D diffusion-weighted MRI scans. The segmentation model demonstrated robust performance, yielding a mean Dice coefficient of 82.14%. Next, voxel-wise model fitting was performed based on both the nnU-Net-predicted and manual lung segmentations to quantify IVIM parameters reflecting tissue microstructure and perfusion. The results suggested no differences between the two. Our work shows that a fully automated pipeline is possible for supporting fetal lung maturity assessment and clinical decision-making.
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