  Sarcopenia is a medical condition characterized by a reduction in muscle mass
and function. A quantitative diagnosis technique consists of localizing the CT
slice passing through the middle of the third lumbar area (L3) and segmenting
muscles at this level. In this paper, we propose a deep reinforcement learning
method for accurate localization of the L3 CT slice. Our method trains a
reinforcement learning agent by incentivizing it to discover the right
position. Specifically, a Deep Q-Network is trained to find the best policy to
follow for this problem. Visualizing the training process shows that the agent
mimics the scrolling of an experienced radiologist. Extensive experiments
against other state-of-the-art deep learning based methods for L3 localization
prove the superiority of our technique which performs well even with a limited
amount of data and annotations.
