This repository contains the code for the paper: Deep Reinforcement Learning for L3 Slice Localization in Sarcopenia Assessment. This work was published in Machine Learning for Medical Imaging at MICCAI 2021.

In short, a Deep Q-Network (DQN) is trained to detect a single slice in a CT volume initially projected using a frontal maximum intensity projection (MIP). The reinforcement learning agent has been applied to the task of localizing the L3 slice which corresponds to the computation of sarcopenia scores.

A complete video is provided in the folder assets

During the beginning of the training, the agent is exploring the environment.

At this stage, the agent has learned a valid policy and is able to locate the L3 zone. However it is still not very accurate.

The agent is able to locate the L3 slice accurately.

If you use this code, please cite our work

@InProceedings{10.1007/978-3-030-87589-3_33,
	author="Laousy, Othmane
	and Chassagnon, Guillaume
	and Oyallon, Edouard
	and Paragios, Nikos
	and Revel, Marie-Pierre
	and Vakalopoulou, Maria",
	title="Deep Reinforcement Learning for L3 Slice Localization in Sarcopenia Assessment",
	booktitle="Machine Learning in Medical Imaging @ MICCAI 2021",
	year="2021",
	publisher="Springer International Publishing",
	address="Cham",
	pages="317--326",
	isbn="978-3-030-87589-3"
}