title: A general framework for context-specific image segmentation using reinforcement learning.
abstract: This paper presents an online reinforcement learning framework for medical image segmentation. The concept of context-specific segmentation is introduced such that the model is adaptive not only to a defined objective function but also to the user's intention and prior knowledge. Based on this concept, a general segmentation framework using reinforcement learning is proposed, which can assimilate specific user intention and behavior seamlessly in the background. The method is able to establish an implicit model for a large state-action space and generalizable to different image contents or segmentation requirements based on learning in situ. In order to demonstrate the practical value of the method, example applications of the technique to four different segmentation problems are presented. Detailed validation results have shown that the proposed framework is able to significantly reduce user interaction, while maintaining both segmentation accuracy and consistency.
authors.affiliation: Hamlyn Centre for Robotic Surgery, Imperial College London, SW7 2AZ London, UK.
authors.forename: Lichao
authors.initials: L
authors.lastname: Wang
authors.forename: Karim
authors.initials: K
authors.lastname: Lekadir
authors.forename: Su-Lin
authors.initials: SL
authors.lastname: Lee
authors.forename: Robert
authors.initials: R
authors.lastname: Merrifield
authors.forename: Guang-Zhong
authors.initials: GZ
authors.lastname: Yang
mesh_terms: D000465:Algorithms; D001185:Artificial Intelligence; D057791:Cardiac Imaging Techniques / Q000379:methods*; D002312:Cardiomyopathy, Hypertrophic / Q000473:pathology; D006801:Humans; D007091:Image Processing, Computer-Assisted / Q000379:methods*; D008279:Magnetic Resonance Imaging; D008955:Models, Cardiovascular; D015203:Reproducibility of Results