Malignant gliomas represent an aggressive class of central nervous system neoplasms which are often treated by maximal surgical resection. Herein, we seek to improve the methods available to quantify the extent of tumors as seen on magnetic resonance imaging using Internet-based, collaborative labeling. In a study of clinically acquired images, we demonstrate that teams of minimally trained human raters are able to reliably characterize the gadolinium-enhancing core and edema tumor regions (Dice ≈ 0.9). The collaborative approach is highly parallel and efficient in terms of time (the total time spent by the collective is equivalent to that of a single expert) and resources (only minimal training and no hardware is provided to the participants). Hence, collaborative labeling is a very promising new technique with potentially wide applicability to facilitate cost-effective manual labeling of medical imaging data.
Keywords: Collaborative Labeling; Malignant Glioma; Segmentation; Statistical Fusion.