Partial Policy-based Reinforcement Learning for Anatomical Landmark Localization in 3D Medical Images

TL;DR

This paper introduces a partial policy-based reinforcement learning approach for 3D medical image landmark localization, enabling efficient learning in large state-action spaces with fewer trials.

Contribution

It proposes a novel partial policy reinforcement learning framework with independent actors and critics for effective 3D landmark localization.

Findings

Requires fewer trials than traditional methods

Achieves robust and efficient localization

Utilizes independent sub-agents for decision making

Abstract

Deploying the idea of long-term cumulative return, reinforcement learning has shown remarkable performance in various fields. We propose a formulation of the landmark localization in 3D medical images as a reinforcement learning problem. Whereas value-based methods have been widely used to solve similar problems, we adopt an actor-critic based direct policy search method framed in a temporal difference learning approach. Successful behavior learning is challenging in large state and/or action spaces, requiring many trials. We introduce a partial policy-based reinforcement learning to enable solving the large problem of localization by learning the optimal policy on smaller partial domains. Independent actors efficiently learn the corresponding partial policies, each utilizing their own independent critic. The proposed policy reconstruction from the partial policies ensures a robust and efficient localization utilizing the sub-agents solving simple binary decision problems in their corresponding partial action spaces. The proposed reinforcement learning requires a small number of trials to learn the optimal behavior compared with the original behavior learning scheme.