ScanRacer – Fast Track Annotation of Segmentation Masks in Volumetric Medical Images – Game Teaser Video

Authors: Jessica Diehm, Robert Hermann, Tobias Pertlwieser, Wei-Cheng, Weilin Fu, Florian Kordon, Andreas Maier


With the rise of deep learning [1], we see a dramatic need for curated and annotated medical image data. In particular, in volumetric images, such annotations are extremely costly, as each slice has to be outlined individually to generate ground truth for the training of deep learning algorithms. Recently, a new initiative was founded that aims at encouraging patients to donate their medical image data [2]. In particular, this initiative also asks for permission to crowd-source the data annotation. This forms a basis to generate sufficient data for large-scale training of deep learning algorithms in medical image analysis.

In this video, we introduce ScanRacer, a game to segment medical images following a racing game paradigm. The game is available for download here.


[1] Maier, A., Syben, C., Lasser, T., & Riess, C. (2019). A gentle introduction to deep learning in medical image processing. Zeitschrift für Medizinische Physik29(2), 86-101.
[2] Servadei, L., Schmidt, R., Eidelloth, C., & Maier, A. (2017, October). Medical Monkeys: A Crowdsourcing Approach to Medical Big Data. In OTM Confederated International Conferences” On the Move to Meaningful Internet Systems” (pp. 87-97). Springer, Cham.