Velo-CT – A Collaborative 3D Racing Game as Incentive for Medical Image Annotation - Sukesh, Richin; Goshika, Sai Kishore; Narayanan, Srividhya Sathya; Bhombore, Vineet Vinay; Fu, Weilin; Kordon, Florian; Maier, Andreas (HEALTHINF - 14th International Conference on Health Informatics, 2021)
Introduction Recent advances in algorithmic processing of medical data are largely based on the use of deep learning algorithms that rely on large amounts of annotated data [1]. To cope with the often complex and time-consuming nature of data acquisition and annotation, data donation and distributed crowd-labeling are an appealing approach [2]. In this paper, we follow this idea and present a single/multi-player mobile game which aids in the annotation process of CT or MR scan data. Methods To provide users with an exciting and rewarding annotation experience, our game "Velo-CT" integrates labeling of 3D volume slices into a classical racing game. This is achieved by using data annotation as a key to unlock more advanced racing tracks in the game. To this end, the game has two game modes: 1) Track Racing, and 2) Image Annotation. In “Track Racing”, players compete by steering a vehicle through a racetrack. The main task is to overtake the opponents and try to remain first in position until the end of the race. Checkpoints along the track allow additional guidance if players accidentally leave the racetrack. To provide a more challenging and diverse experience, respawning pick-ups can be found at random locations along the track in the form of floating CT scanners. Such pick-up can either be a speed multiplier (multiplies car speed by 1.2) or a size enhancer (increases the car size by 1.5 to threat opponents and impede their view). The racetracks are modeled based on the segmentation contour of clinical CT data, e.g. showing the respiratory region. For slice segmentation we use a simple K-means classifier. Annotation points earned after beating a track allow the player to unlock new data for the second game mode “Image Annotation”. This is where the medical expertise of the player comes into play. Based on the current progress, the player is presented several medical images which may contain abnormalities. If an abnormality is spotted, the precise location can be marked by a finger tap on the area. The more images a player annotates, the more points can be earned to unlock a new track – and eventually a more intricate anatomy to race on. Results Our game was prototyped in Unity3D [3]. As a first anatomy, respiratory tracks based on the “Low Dose CT Grand Challenge” data were constructed. First experiences by players of different gaming background were reported for the single player setting. Users report a fast-paced racing experience with a healthy amount of challenge provided by pick-ups and competitive AI opponents. The organ-aligned layouts result in tracks similar in shape, which should be addressed in the future by adding multiple anatomical regions. Conclusion Velo-CT provides an intuitive and extendable integration of the often tedious data annotation process for 3D medical data into a rewarding gaming context. The prototype is available for free download and serves as a basis for further extensions to related game modes. References [1] Maier, A., Syben, C., Lasser, T., & Riess, C. (2019). A gentle introduction to deep learning in medical image processing. Zeitschrift für Medizinische Physik, 29(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. [3] Murray, J. W. (2014). C# game programming cookbook for Unity 3D. AK Peters/CRC Press. <br
Keywords: Gamification; Medical Image Annotation; Deep Learning.