  The detection of anatomical landmarks is a vital step for medical image
analysis and applications for diagnosis, interpretation and guidance. Manual
annotation of landmarks is a tedious process that requires domain-specific
expertise and introduces inter-observer variability. This paper proposes a new
detection approach for multiple landmarks based on multi-agent reinforcement
learning. Our hypothesis is that the position of all anatomical landmarks is
interdependent and non-random within the human anatomy, thus finding one
landmark can help to deduce the location of others. Using a Deep Q-Network
(DQN) architecture we construct an environment and agent with implicit
inter-communication such that we can accommodate K agents acting and learning
simultaneously, while they attempt to detect K different landmarks. During
training the agents collaborate by sharing their accumulated knowledge for a
collective gain. We compare our approach with state-of-the-art architectures
and achieve significantly better accuracy by reducing the detection error by
50%, while requiring fewer computational resources and time to train compared
to the naive approach of training K agents separately.
