The authors of this study developed a 3D nnU-Net-based model for automatic lung segmentation in computed tomography pulmonary angiography (CTPA) imaging that was found to be highly accurate, clinically evaluated, and externally tested in patient cohorts with a spread of lung disease. Key points Accurate, externally validated CT pulmonary angiography (CTPA) lung segmentation model tested in two large heterogeneous clinical cohorts (pulmonary hypertension and interstitial lung disease). No segmentation failures and robust review of model outputs by radiologists found 1 (0.5%) clinically significant segmentation error. Intended clinical use of this model is a necessary step in techniques such as lung volume, parenchymal disease quantification, or pulmonary vessel analysis. Article: External validation, radiological evaluation, and development of deep learning automatic lung segmentation in contrast-enhanced chest CT Authors: Krit Dwivedi, Michael Sharkey, Samer Alabed, Curtis P. Langlotz, Andy J. Swift & Christian Bluethgen
Impact of deep learning reconstruction on radiation dose reduction and cancer risk in CT examinations
Deep‑learning reconstruction (DLR) shifts CT image formation from a hardware‑limited process to a data‑driven one. In our real‑world cohort of >10,000 body scans, we observed a
28. April 2025
