Using Artificial Intelligence for Automatic Segmentation of CT Lung Images in Acute Respiratory Distress Syndrome - Herrmann, Peter; Busana, Mattia; Cressoni, Massimo; Lotz, Joachim; Moerer, Onnen; Saager, Leif; Meissner, Konrad; Quintel, Michael; Gattinoni, Luciano (Frontiers in Physiology, 2021)
Knowledge of gas volume, tissue mass and recruitability measured by the quantitative CT scan analysis (CT-qa) is important when setting the mechanical ventilation in Acute Respiratory Distress Syndrome (ARDS). Yet, the manual segmentation of the lung requires a considerable workload. Our goal was to provide an automatic, clinically applicable and reliable lung segmentation procedure. Therefore, a modified Convolutional Neural Network was used to train an Artificial Intelligence (AI) algorithm on 15 healthy subjects (1302 slices), 100 ARDS patients (12279 slices) and 20 COVID-19 (1817 slices). Eighty percent of this populations was used for training, 20% for testing. The AI and manual segmentation at slice level were compared by Intersection over Union (IoU). The CT-qa variables were compared by regression and Bland Altman analysis. The AI-segmentation of a single patient required 5-10 seconds vs 1-2 hours of the manual. At slice level, the algorithm showed on the test set a IoU of 91.8±1.0%, 85.8±3,9% and 84.7±5,7% in normal, ARDS and COVID-19 lungs respectively, with a U-shape in the performance: better in the lung middle region, worse at the apex and base. At patient level, on the test set, the total lung volume measured by AI and manual segmentation had a R2 of 0.99 and a bias -9.8 ml [CI:+56.0/-75.7 ml]. The recruitability measured with manual and AI-segmentation, as change in non-aerated tissue fraction had a bias of +0.3% [CI:+6.2/-5.5%] and -0.5% [CI:+2.3/-3.3%] expressed as change in well-aerated tissue fraction. The AI-powered lung segmentation provided fast and clinically reliable results. It is able to segment the lungs of seriously ill ARDS patients fully automatically.