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A General Framework for Context-Specific Image Segmentation Using Reinforcement Learning | IEEE Journals & Magazine | IEEE Xplore

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 mo...Show More

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.
Published in: IEEE Transactions on Medical Imaging ( Volume: 32, Issue: 5, May 2013)
Page(s): 943 - 956
Date of Publication: 14 March 2013

ISSN Information:

PubMed ID: 23508261

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References

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