We evaluate a fully-automated generalizable deep learning (DL) approach for lung segmentation using a 3D convolutional neural network on a large and diverse proton (1H) MRI dataset, containing images acquired at different resolutions and inflation levels. The dataset comprised of 336 1H-MR images from healthy subjects and patients with respiratory diseases. Our trained model was able to accurately segment scans of markedly different resolutions (3x3x3mm3, 4x4x5mm3 and 4x4x10mm3), achieving a mean±SD Dice similarity coefficient of 0.94±0.02. In addition, it was shown that DL generates more accurate segmentations compared to state-of-the-art solutions.
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