We propose a novel machine learning approach for segmentation of lung cystic structures using MRI. Following our recent development on improved structural lung imaging at low-field MRI we use a combination of generative adverserial networks and modified UNet for segmentation of cyst and lung tissues. This provides a non-ionizing radiation free alternative for patients with Lymphangioleiomyomatosis who are evaluated using CT imaging. We employ cross-modality image synthesis and segmentation approaches which work synergistically to take advantage of available CT data. In this work we demonstrate the potential of MRI for quantitative analysis of cystic lung .
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