SwifTree: Interactive Extraction of 3D Trees Supporting Gaming and Crowdsourcing

Analysis of vascular and airway trees of circulatory and respiratory systems is important for many clinical applications. Automatic segmentation of these tree-like structures from 3D data remains an open problem due to their complex branching patterns, geometrical diversity, and pathology. On the other hand, it is challenging to design intuitive interactive methods that are practical to use in 3D for trees with tens or hundreds of branches. We propose SwifTree, an interactive software for tree extraction that supports crowdsourcing and gamification. Our experiments demonstrate that: (i) aggregating the results of multiple SwifTree crowdsourced sessions achieves more accurate segmentation; (ii) using the proposed game-mode reduces time needed to achieve a pre-set tree segmentation accuracy; and (iii) SwifTree outperforms automatic segmentation methods especially with respect to noise robustness.