Deep Reinforcement Learning for L3 Slice Localization in Sarcopenia Assessment

TL;DR

This paper introduces a deep reinforcement learning approach using a Deep Q-Network to accurately localize the L3 CT slice for sarcopenia assessment, mimicking radiologist behavior and outperforming existing methods.

Contribution

The paper presents a novel deep reinforcement learning method for L3 slice localization, demonstrating improved accuracy and data efficiency over prior techniques.

Findings

Outperforms state-of-the-art methods in L3 localization

Effective with limited training data

Mimics radiologist scrolling behavior

Abstract

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.