Medical images are generally acquired with limited field-of-view (FOV), which could lead to incomplete regions of interest (ROI), and thus impose a great challenge on medical image analysis. This is particularly evident for the learning-based multi-target landmark detection, where algorithms could be misleading to learn primarily the variation of background due to the varying FOV, failing the detection of targets. Based on learning a navigation policy, instead of predicting targets directly, reinforcement learning (RL)-based methods have the potential totackle this challenge in an efficient manner. Inspired by this, in this work we propose a multi-agent RL framework for simultaneous multi-target landmark detection. This framework is aimed to learn from incomplete or (and) complete images to form an implicit knowledge of global structure, which is consolidated during the training stage for the detection of targets from either complete or incomplete test images. To further explicitly exploit the global structural information from incomplete images, we propose to embed a shape model into the RL process. With this prior knowledge, the proposed RL model can not only localize dozens of targetssimultaneously, but also work effectively and robustly in the presence of incomplete images. We validated the applicability and efficacy of the proposed method on various multi-target detection tasks with incomplete images from practical clinics, using body dual-energy X-ray absorptiometry (DXA), cardiac MRI and head CT datasets. Results showed that our method could predict whole set of landmarks with incomplete training images up to 80% missing proportion (average distance error 2.29 cm on body DXA), and could detect unseen landmarks in regions with missing image information outside FOV of target images (average distance error 6.84 mm on 3D half-head CT).

翻译：医学图像通常以有限的实地观察(FOV)获得,这可能导致感兴趣的地区不完全,从而给医学图像分析带来巨大的挑战。对于基于学习的多目标标志性检测来说,这一点尤其明显,因为算法可能会误导主要了解背景差异,因为不同的视野、无法检测目标。根据学习导航政策,而不是直接预测目标,基于强化学习(RL)的方法有可能以有效的方式使这一挑战变得不完全。在这项工作中,我们提出了一个多试剂RL框架,用于同时检测多目标的图像。这个框架旨在从不完整或(和)完整的图像中学习,以便形成对全球结构的隐性了解,而这种了解主要是由于不同的视野,因此无法检测目标。 根据学习一项导航政策,而不是直接预测目标,基于强化学习(RL)的方法有可能以不完全的方式将这一挑战化。基于这一认识,拟议的RLCT模型不仅将数十个目标同级的RL(RL)框架用于同时检测多目标点的图像。这个框架旨在从不完全的、不完全的、不完全的、不完全的、不完全的、不完全的、不精确的、不精确的、不精确的、不精确的、不精确的、不精确的、不精确的图像显示的、不完全的、不完全的、不完全的、不精确的、不精确的、不完全的、不精确的、不精确的、不精确的、不精确的、不精确的、不精确的、不精确的、不完全的、不精确的、不精确的、不精确的、不精确的、不完全的、不精确的、不精确的、不精确的、不精确的、不精确的、不精确的、不精确的、不精确的、不精确的、不精确的、不精确的、不完全的、不精确的、不精确的、不精确的、不精确的、不完全的、不精确的、不精确的、不精确的、不精确的、不精确的、不精确的、不精确的、不精确的、不精确的、不精确的、不精确的、不精确的、不完全的、不精确的、不精确的、不精确的、不精确的、不精确的、不精确的、不精确的、不精确的、不精确的、不精确的、不精确的、