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Abstract #2231

Deep Learning-Based Automatic Lung Segmentation for MR Images at 0.55T

Rachel Chae1,2, Ahsan Javed1, Rajiv Ramasawmy1, Hui Xue1, Marcus Carlsson1, Adrienne E Campbell-Washburn1, and Felicia Seemann1
1Cardiovascular Branch, National Heart, Lung, and Blood Institute, National Institutes of Health, Bethesda, MD, United States, 2Electrical Engineering and Computer Science, Massachusetts Institute of Technology, Cambridge, MA, United States


High-performance 0.55T systems are well suited for structural lung imaging due to reduced susceptibility and prolonged T2*. Lung segmentation is required to derive metrics of pulmonary function from lung MRI. Convolutional neural networks are effective for lung segmentation at higher field strengths, but they do not generalize to images acquired at 0.55T. This study develops a neural network for automated lung segmentation of T1 and proton density weighted ultrashort-TE MRI at 0.55T. Training data was generated using segmentations by active contours and manual corrections. The proposed network was fast (1.07s) and as accurate as existing semi-automated segmentation (dice coefficient 0.93).

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