# Deep Reinforcement Learning for L3 Slice Localization in Sarcopenia Assessment

arXiv: https://arxiv.org/abs/2107.12800 · HTML: https://mlsanity.com/paper/2107.12800/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 8 of its 28 references that are in the map.
- First posted on arXiv 27 Jul 2021.

## Builds on (most cited first, 8 of 8)

- [Bootstrap your own latent: A new approach to self-supervised Learning](https://mlsanity.com/paper/2006.07733/) (2020)
- [Playing Atari with Deep Reinforcement Learning](https://mlsanity.com/paper/1312.5602/) (2013)
- [Dueling Network Architectures for Deep Reinforcement Learning](https://mlsanity.com/paper/1511.06581/) (2015)
- [No-Regret Exploration in Goal-Oriented Reinforcement Learning](https://mlsanity.com/paper/1912.03517/) (2019)
- [Multiple Landmark Detection using Multi-Agent Reinforcement Learning](https://mlsanity.com/paper/1907.00318/) (2019)
- [Automatic View Planning with Multi-scale Deep Reinforcement Learning Agents](https://mlsanity.com/paper/1806.03228/) (2018)
- [Deep Reinforcement Learning for Organ Localization in CT](https://mlsanity.com/paper/2005.04974/) (2020)
- [Automatic L3 slice detection in 3D CT images using fully-convolutional networks](https://mlsanity.com/paper/1811.09244/) (2018)

## Abstract (arXiv)

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.