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

TL;DR

This paper introduces an automated deep learning pipeline for fetal lung segmentation and maturity evaluation from diffusion-weighted MRI, improving efficiency and supporting clinical decision-making in fetal growth restriction cases.

Contribution

It presents a novel automated pipeline combining 3D nnU-Net segmentation with IVIM parameter fitting for fetal lung assessment, reducing manual effort and enhancing clinical applicability.

Findings

Segmentation achieved a mean Dice coefficient of 82.14%.

Automated IVIM parameter estimation matched manual segmentation results.

Pipeline supports non-invasive fetal lung maturity assessment.

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.