# Automatic View Planning with Multi-scale Deep Reinforcement Learning Agents

arXiv: https://arxiv.org/abs/1806.03228 · HTML: https://mlsanity.com/paper/1806.03228/

## Key facts

- Cited by 20 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 8 Jun 2018.

## Built on by (most cited first, 20 of 20)

- [Deep reinforcement learning in medical imaging: A literature review](https://mlsanity.com/paper/2103.05115/) (2021)
- [Autonomous Navigation of an Ultrasound Probe Towards Standard Scan Planes with Deep Reinforcement Learning](https://mlsanity.com/paper/2103.00718/) (2021)
- [Agent with Warm Start and Adaptive Dynamic Termination for Plane Localization in 3D Ultrasound](https://mlsanity.com/paper/2103.14502/) (2021)
- [Searching Collaborative Agents for Multi-plane Localization in 3D Ultrasound](https://mlsanity.com/paper/2105.10626/) (2021)
- [Selective experience replay compression using coresets for lifelong deep reinforcement learning in medical imaging](https://mlsanity.com/paper/2302.11510/) (2023)
- [Image-Guided Navigation of a Robotic Ultrasound Probe for Autonomous Spinal Sonography Using a Shadow-aware Dual-Agent Framework](https://mlsanity.com/paper/2111.02167/) (2021)
- [Training Automatic View Planner for Cardiac MR Imaging via Self-Supervision by Spatial Relationship between Views](https://mlsanity.com/paper/2109.11715/) (2021)
- [Agent with Tangent-based Formulation and Anatomical Perception for Standard Plane Localization in 3D Ultrasound](https://mlsanity.com/paper/2207.00475/) (2022)
- [Strategising template-guided needle placement for MR-targeted prostate biopsy](https://mlsanity.com/paper/2207.10784/) (2022)
- [Multi-Target Landmark Detection with Incomplete Images via Reinforcement Learning and Shape Prior](https://mlsanity.com/paper/2301.05392/) (2023)
- [Uncertainty-aware Diffusion and Reinforcement Learning for Joint Plane Localization and Anomaly Diagnosis in 3D Ultrasound](https://mlsanity.com/paper/2506.23538/) (2025)
- [Asynchronous Decentralized Federated Lifelong Learning for Landmark Localization in Medical Imaging](https://mlsanity.com/paper/2303.06783/) (2023)
- [Automatic view plane prescription for cardiac magnetic resonance imaging via supervision by spatial relationship between views](https://mlsanity.com/paper/2309.12805/) (2023)
- [Artificial Intelligence for Neuro MRI Acquisition: A Review](https://mlsanity.com/paper/2406.05982/) (2024)
- [Automated Interpretable 2D Video Extraction from 3D Echocardiography](https://mlsanity.com/paper/2511.15946/) (2025)
- [Deep Reinforcement Learning for L3 Slice Localization in Sarcopenia Assessment](https://mlsanity.com/paper/2107.12800/) (2021)
- [Cross Modality 3D Navigation Using Reinforcement Learning and Neural Style Transfer](https://mlsanity.com/paper/2111.03485/) (2021)
- [Multi-environment lifelong deep reinforcement learning for medical imaging](https://mlsanity.com/paper/2306.00188/) (2023)
- [A framework for dynamically training and adapting deep reinforcement learning models to different, low-compute, and continuously changing radiology deployment environments](https://mlsanity.com/paper/2306.05310/) (2023)
- [Adaptive Plane Reformatting for 4D Flow MRI using Deep Reinforcement Learning](https://mlsanity.com/paper/2506.00727/) (2025)

## Abstract (arXiv)

We propose a fully automatic method to find standardized view planes in 3D image acquisitions. Standard view images are important in clinical practice as they provide a means to perform biometric measurements from similar anatomical regions. These views are often constrained to the native orientation of a 3D image acquisition. Navigating through target anatomy to find the required view plane is tedious and operator-dependent. For this task, we employ a multi-scale reinforcement learning (RL) agent framework and extensively evaluate several Deep Q-Network (DQN) based strategies. RL enables a natural learning paradigm by interaction with the environment, which can be used to mimic experienced operators. We evaluate our results using the distance between the anatomical landmarks and detected planes, and the angles between their normal vector and target. The proposed algorithm is assessed on the mid-sagittal and anterior-posterior commissure planes of brain MRI, and the 4-chamber long-axis plane commonly used in cardiac MRI, achieving accuracy of 1.53mm, 1.98mm and 4.84mm, respectively.