Estimating Total Lung Volume from Pixel-Level Thickness Maps of Chest Radiographs Using Deep Learning

2025/05/28 by Tina Dorosti, Manuel Schultheiß, Philipp Schmette +11 · 1 voice
Medicine · #COVID-19 diagnosis using AI #Lung Cancer Diagnosis and Treatment #Radiomics and Machine Learning in Medical Imaging

paper · doi:10.1148/ryai.240484

openalex publication_date 2025/05/28 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28

Abstract

A deep learning model successfully estimated lung thickness maps and total lung volumes from chest radiographs, demonstrating strong correlation with CT-derived measurements.

Citations

• SciPy 1.0: fundamental algorithms for scientific computing in Python
• Array programming with NumPy

Discussions

• Pixel-level lung thickness maps for real and synthetic radiographs generated with a U-Net trained on synthetic data https://doi.org/10.1148/ryai.240484 #LungVolume #MachineLearning #ChestXray [bsky, 6 points, 0 comments]

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