This paper presents the use of Multi-Agent Reinforcement Learning (MARL) to perform navigation in 3D anatomical volumes from medical imaging. We utilize Neural Style Transfer to create synthetic Computed Tomography (CT) agent gym environments and assess the generalization capabilities of our agents to clinical CT volumes. Our framework does not require any labelled clinical data and integrates easily with several image translation techniques, enabling cross modality applications. Further, we solely condition our agents on 2D slices, breaking grounds for 3D guidance in much more difficult imaging modalities, such as ultrasound imaging. This is an important step towards user guidance during the acquisition of standardised diagnostic view planes, improving diagnostic consistency and facilitating better case comparison.

翻译：本文介绍利用多机构强化学习(MARL)进行医学成像3D解剖量的导航;我们利用神经风格传输来创建合成计算表成像代理体操环境,并评估我们的代理体对临床CT量的概括能力;我们的框架不要求任何有标签的临床数据,并容易地与若干图像翻译技术、使跨模式应用相结合;此外,我们仅将我们的代理体用2D切片作为条件,用诸如超声波成像等更困难的成像模式破解3D指导的底部;这是在采购标准化诊断视图机、提高诊断一致性和促进更好的案例比较过程中向用户提供指导的一个重要步骤。