Interactive extraction of 3D trees from medical images supporting gaming and crowdsourcing

Analysis of vascular and airway trees of circulatory and respiratory systems is important for a wide range of clinical applications. Automatic segmentation of these tree-like structures from 3D image data remains challenging due to complex branching patterns, geometrical diversity, and pathology. Existing automated techniques are sensitive to parameters setting, may leak into nearby structures, or miss true bifurcating branches; while interactive methods for segmenting vascular trees are hard to design and use, making them impractical to extend to 3D and to vascular trees with many branches (e.g., tens or hundreds). We propose SwifTree, an interactive software to facilitate this tree extraction task while exploring crowdsourcing and gamification. Our experiments demonstrate that: (i) aggregating the results of multiple SwifTree crowdsourced sessions can achieve more accurate segmentation; (ii) using the proposed game-mode can reduce time needed to achieve a pre-set tree segmentation accuracy; and (iii) SwifTree outperforms automatic segmentation methods especially with respect to noise robustness.