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WeLineation: crowdsourcing delineations for reliable ground truth estimation

Crowdsourcing is a concept to encourage humans all over the world to generate ground truth for classification data such as images. While frameworks for binary and multi-label classification exist, crowdsourcing of medical image segmentation is covered only by few work. In this paper, we present a web-based platform supporting scientists of various domains to obtain segmentations, which are close to ground-truth references. The system is composed of frontend, authentication, management, processing, and persistence layers which are implemented combining various javascript tools, the django web framework, an asynchronous celery task, and a PostgreSQL database, respectively. It is deployed on a kubernetes cluster. A set of image data accompanied by a task instruction can be uploaded. Users can be invited or subscribe to join in. After passing a guided tutorial of pre- segmented example images, segmentations can be obtained from non-expert users from all over the world. The Simultaneous Truth and Performance Level Estimation (STAPLE) algorithm generates estimated ground truth segmentation masks and evaluates the users performance continuously in the backend. As a proof of concept, a test-study with 75 photographs of human eyes was performed by 44 users. In just a few days, 2,060 segmentation masks with a total of 52,826 vertices along the mask contour have been collected.