Congenital Diaphragmatic Hernia: automatic lung and liver MRI segmentation with nnU-Net, reproducibility of pyradiomics features, and a Machine Learning application for the classification of liver herniation.

Abstract

Purpose Prenatal assessment of lung size and liver position is essential to stratify Congenital Diaphragmatic Hernia (CDH) fetuses in risk categories, guiding counseling and patient management. Manual segmentation on fetal MRI provides a quantitative estimation of total lung volume and liver herniation. However, it is time-consuming and operator-dependent. Methods In this study, we utilized a publicly available Deep Learning (DL) segmentation system (nnU-Net) for automatic contouring of CDH-affected fetal lungs and liver on MRI sections. Reproducibility was assessed calculating the Jaccard coefficient for manual and automatic segmentation. Pyradiomics standard features were then extracted from both manually and automatically segmented regions. Features reproducibility between the two groups was evaluated through the Wilcoxon rank-sum test and Intraclass Correlation Coefficients (ICCs). We finally tested the reliability of the automatic-segmentation approach by building a ML classifier system for the prediction of liver herniation, based on Support Vector Machines (SVM) and trained on shape features computed both in the manual and nnU-Net-segmented organs. Results We compared the area under the classifier Receiver Operating Characteristics curve (AUC) in the two cases. Pyradiomics features calculated in the manual ROIs were partly reproducible by the same features calculated in nnU-Net segmented ROIs and, when used in the ML procedure to predict liver herniation (both AUC around 0.85). Conclusions Our results suggest that automatic MRI segmentation is feasible, with good reproducibility of pyradiomics features, and that a ML system for liver herniation prediction offers good reliability. Trial registration URL: https://clinicaltrials.gov/ct2/show/NCT04609163?term=NCT04609163&draw=2&rank=1 Clinical Trial Identification n° NCT04609163