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Hyperpolarized gas MRI enables visualization of regional lung ventilation with high spatial resolution. Segmentation of the ventilated lung is required to calculate clinically relevant biomarkers. Recent research in deep learning (DL) has shown promising results for numerous segmentation problems. In this work, we evaluate a 3D V-Net to segment ventilated lung regions on hyperpolarized gas MRI scans. The dataset consists of 743 helium-3 (³He) or xenon-129 (¹²⁹Xe) volumetric scans and corresponding expert segmentations from 326 healthy subjects and patients with a wide range of pathologies. We evaluated segmentation performance for several DL experimental methods via overlap, distance and error metrics and compared them to conventional segmentation methods, namely, spatial fuzzy c-means (SFCM) and K-means clustering. We observed that training on combined ³He and ¹²⁹Xe MRI …