Deep Learning-Based Fetal Lung Segmentation from Diffusion-weighted MRI Images and Lung Maturity Evaluation for Fetal Growth Restriction

• URL: http://arxiv.org/abs/2507.13106v1
• Date: Thu, 17 Jul 2025 13:21:42 GMT
• Title: Deep Learning-Based Fetal Lung Segmentation from Diffusion-weighted MRI Images and Lung Maturity Evaluation for Fetal Growth Restriction
• Authors: Zhennan Xiao, Katharine Brudkiewicz, Zhen Yuan, Rosalind Aughwane, Magdalena Sokolska, Joanna Chappell, Trevor Gaunt, Anna L. David, Andrew P. King, Andrew Melbourne,
• Abstract summary: Intra-voxel incoherent motion analysis has shown promising results for non-invasive assessment of fetal lung development.<n>Our work shows that a fully automated pipeline is possible for supporting fetal lung maturity assessment and clinical decision-making.
• Score: 3.0052797079637075
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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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