  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).
