Automated Quantitative Analysis of Pulmonary Vasculature in Congenital Diaphragmatic Hernia using Deep Learning

Abstract

Congenital diaphragmatic hernia (CDH) is characterized by pulmonary hypoplasia and vascular underdevelopment, leading to impaired gas exchange and high neonatal mortality. Accurate and quantitative assessment of pulmonary vasculature is crucial for understanding disease severity, but manual segmentation of three-dimensional vascular networks in medical images is time-consuming and operator-dependent. This protocol presents a fully automated deep learning-based method for pulmonary vessel segmentation and morphometric analysis using postnatal computed tomography (CT) scans. The pipeline includes standardized preprocessing steps-conversion to Hounsfield units, windowing, isotropic resampling, and contrast-limited adaptive histogram equalization (CLAHE)-to normalize imaging data and enhance vascular visibility. A U-Net convolutional neural network (CNN) architecture is then trained to segment the pulmonary vasculature, followed by a three-dimensional skeletonization algorithm to quantify morphometric parameters such as branch number, mean branch length, and generational depth. Representative results demonstrate that the proposed model achieves high segmentation accuracy, with the transfer learning configuration yielding the best performance. Quantitative morphometric analysis reveals markedly reduced vascular complexity in CDH compared with control lungs, consistent with the known pathological features of pulmonary hypoplasia. This automated approach enables reproducible, quantitative, and non-invasive evaluation of pulmonary vascular morphology in CDH. The method can be adapted to other imaging modalities and applied to studies of fetal and neonatal lung development, facilitating translational research and future clinical integration.