  Robust localization of organs in computed tomography scans is a constant
pre-processing requirement for organ-specific image retrieval, radiotherapy
planning, and interventional image analysis. In contrast to current solutions
based on exhaustive search or region proposals, which require large amounts of
annotated data, we propose a deep reinforcement learning approach for organ
localization in CT. In this work, an artificial agent is actively self-taught
to localize organs in CT by learning from its asserts and mistakes. Within the
context of reinforcement learning, we propose a novel set of actions tailored
for organ localization in CT. Our method can use as a plug-and-play module for
localizing any organ of interest. We evaluate the proposed solution on the
public VISCERAL dataset containing CT scans with varying fields of view and
multiple organs. We achieved an overall intersection over union of 0.63, an
absolute median wall distance of 2.25 mm, and a median distance between
centroids of 3.65 mm.
