Hamlyn Centre for Robotic Surgery, Imperial College London, SW7 2AZ London, UK.
IEEE Trans Med Imaging. 2013 May;32(5):943-56. doi: 10.1109/TMI.2013.2252431. Epub 2013 Mar 14.
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.
本文提出了一种用于医学图像分割的在线强化学习框架。引入了特定于上下文的分割概念，使得模型不仅适应于定义的目标函数，而且适应于用户的意图和先验知识。基于这个概念，提出了一个使用强化学习的通用分割框架，它可以在后台无缝地吸收特定的用户意图和行为。该方法能够为大型状态-动作空间建立一个隐式模型，并能够根据现场学习推广到不同的图像内容或分割要求。为了展示该方法的实用价值，本文提出了该技术在四个不同分割问题中的应用示例。详细的验证结果表明，所提出的框架能够显著减少用户交互，同时保持分割的准确性和一致性。
