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Abstract #4357

Automatic Segmentation of Lung Anatomy from Proton MRI based on a Deep Convolutional Neural Network

Xue Feng1, Nicholas J. Tustison1, Renkun Ni1, Zixuan Lin1, John P. Mugler, III1, Craig H. Meyer1, Talissa A. Altes1,2, Joanne M. Cassani2, Y. Michael Shim1, and Kun Qing1

1University of Virginia, Charlottesville, VA, United States, 2University of Missouri School of Medicine, Columbia, MO, United States

With rapid development of pulmonary MRI techniques, increasingly useful morphological and functional information can be obtained, such as pulmonary perfusion, ventilation and gas uptake through hyperpolarized-gas MRI. Identification of lung anatomy is usually the first step for quantitative analysis. In the work, we proposed and validated a new approach for automatic segmentation of lung anatomy from proton MRI based on 3D U-Net structure. The new method had a relatively consistent performance in all subjects (dice overlap 0.90-0.97). Its future application for anatomical based analysis of structural and/or functional pulmonary MRI data needs further validation in larger number of data.

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