Gamification concept for acquisition of medical image segmentation via crowdsourcing

Abstract

In many fields of medical imaging, image segmentation is required as a basis for further analysis and diagnosis. Convolutional neural networks are a promising approach providing high accuracy. However, large-scale annotated datasets are necessary to train these networks. As expert annotations are costly, crowdsourcing has shown to be an adequate alternative. In previous work, we examined how the workforce of a crowd should be distributed for obtaining annotations with an optimal trade-off between quantity and quality. In this work, we present a gamification approach by transforming the tedious task of image segmentation into a game. This approach aims at motivating users by having fun but nevertheless generating annotations of adequate quality. Therefore, this work presents a gamified crowdsourcing concept for medical image segmentation. We give an overview of incentives applied in state-of-the-art literature and propose two different gamification approaches on how the image segmentation task can be realized as a game. Finally, we propose a integrated game concept that combines both approaches with the following incentives: (a) points / scoring to reward users instantly for accurate segmentations, (b) leaderboard / rankings to let users accumulate scores for long-term motivation, (c) badges / achievements to give users a visual representation of their ”strength” in segmentation, and (d) levels to visualize the learning curve of users in performing the segmentation. We give details on a first prototype implementation and describe how the game concept complies with the guidelines from our prior work.

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