# Deep Reinforcement Learning for Organ Localization in CT

arXiv: https://arxiv.org/abs/2005.04974 · HTML: https://mlsanity.com/paper/2005.04974/

## Key facts

- Cited by 7 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 11 May 2020.

## Built on by (most cited first, 7 of 7)

- [Reinforcement Learning in Medical Image Analysis: Concepts, Applications, Challenges, and Future Directions](https://mlsanity.com/paper/2206.14302/) (2022)
- [Machine Learning Models Disclosure from Trusted Research Environments (TRE), Challenges and Opportunities](https://mlsanity.com/paper/2111.05628/) (2021)
- [A unified 3D framework for Organs at Risk Localization and Segmentation for Radiation Therapy Planning](https://mlsanity.com/paper/2203.00624/) (2022)
- [Deep Learning in Healthcare: An In-Depth Analysis](https://mlsanity.com/paper/2302.10904/) (2023)
- [Deep Reinforcement Learning for L3 Slice Localization in Sarcopenia Assessment](https://mlsanity.com/paper/2107.12800/) (2021)
- [A Deep Learning Approach to Predicting Collateral Flow in Stroke Patients Using Radiomic Features from Perfusion Images](https://mlsanity.com/paper/2110.12508/) (2021)
- [Interactive Whole Slide Images for RL-based Tumour Segmentation](https://mlsanity.com/paper/2608.16607/) (2026)

## Abstract (arXiv)

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.