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Abstract #3224

Generalizable deep learning for multi-resolution proton MRI lung segmentation in multiple diseases

Joshua R Astley1,2, Alberto M Biancardi1, Helen Marshall1, Laurie J Smith1, Guilhem J Collier1, Paul J Hughes1, Michael Walker1, Matthew Q Hatton2, Jim M Wild1, and Bilal A Tahir1,2
1POLARIS, University of Sheffield, Sheffield, United Kingdom, 2Oncology and Metabolism, University of Sheffield, Sheffield, United Kingdom

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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