Automatic View Planning with Multi-scale Deep Reinforcement Learning Agents

  title={Automatic View Planning with Multi-scale Deep Reinforcement Learning Agents},
  author={Amir Alansary and Lo{\"i}c Le Folgoc and Ghislain Vaillant and Ozan Oktay and Yuanwei Li and Wenjia Bai and Jonathan Passerat-Palmbach and Ricardo Guerrero and Konstantinos Kamnitsas and Benjamin Hou and Steven G. McDonagh and Ben Glocker and Bernhard Kainz and Daniel Rueckert},
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… 

Autonomous Navigation of an Ultrasound Probe Towards Standard Scan Planes with Deep Reinforcement Learning

  • Keyu LiJian Wang M. Meng
  • Computer Science
    2021 IEEE International Conference on Robotics and Automation (ICRA)
  • 2021
A deep reinforcement learning framework to autonomously control the 6-D pose of a virtual US probe based on real-time image feedback to navigate towards the standard scan planes under the restrictions in real-world US scans is proposed.

Agent with Tangent-based Formulation and Anatomical Perception for Standard Plane Localization in 3D Ultrasound

A novel reinforcement learning (RL) framework for automatic SP localization in 3D US achieves a high localization accuracy as well as robust performance and proposes a spatial-anatomical reward to effectively guide learning trajectories by exploiting spatial and anatomical information simultaneously.

Evaluating reinforcement learning agents for anatomical landmark detection

Multiple Landmark Detection using Multi-Agent Reinforcement Learning

A new detection approach for multiple landmarks based on multi-agent reinforcement learning based on the hypothesis that the position of all anatomical landmarks is interdependent and non-random within the human anatomy, thus finding one landmark can help to deduce the location of others.

Image-Guided Navigation of a Robotic Ultrasound Probe for Autonomous Spinal Sonography Using a Shadow-Aware Dual-Agent Framework

A novel dual-agent framework is proposed that integrates a reinforcement learning (RL) agent and a deep learning (DL) agent to jointly determine the movement of the US probe based on the real-time US images, in order to mimic the decision-making process of an expert sonographer to achieve autonomous standard view acquisitions in spinal sonography.

Cross Modality 3D Navigation Using Reinforcement Learning and Neural Style Transfer

This paper presents the use of Multi-Agent Reinforcement Learning (MARL) to perform navigation in 3D anatomical volumes from medical imaging. We utilize Neural Style Transfer to create synthetic

Agent with Warm Start and Active Termination for Plane Localization in 3D Ultrasound

A novel reinforcement learning (RL) framework to automatically localize fetal brain standard planes in 3D US using a recurrent neural network based strategy for active termination of the agent's interaction procedure, which improves both the accuracy and efficiency of the localization system.

Reinforced Redetection of Landmark in Pre- and Post-operative Brain Scan Using Anatomical Guidance for Image Alignment

By defining landmarks for each patient individually, this work aims to obtain a patient-specific representation of its differential radiomic features across different time points for enhancing image alignment.

Deep reinforcement learning in medical imaging: A literature review

Searching Collaborative Agents for Multi-plane Localization in 3D Ultrasound

The proposed novel multi-agent reinforcement learning (MARL) framework is the first to realize automatic SP localization in pelvic US volumes, and the approach can handle both normal and abnormal uterus cases.



Multi-Scale Deep Reinforcement Learning for Real-Time 3D-Landmark Detection in CT Scans

This work couple the modeling of the anatomy appearance and the object search in a unified behavioral framework, using the capabilities of deep reinforcement learning and multi-scale image analysis, and significantly outperforms state-of-the-art solutions on detecting several anatomical structures with no failed cases from a clinical acceptance perspective.

An Artificial Agent for Robust Image Registration

This paper demonstrates on two 3-D/3-D medical image registration examples with drastically different nature of challenges, that the artificial agent outperforms several state-of-the-art registration methods by a large margin in terms of both accuracy and robustness.

Automatic View Planning for Cardiac MRI Acquisition

A new approach to automating and accelerating the acquisition process to improve the clinical workflow is proposed and a highly accelerated static 3D full-chest volume is captured through parallel imaging within one breath-hold.

Deep Reinforcement Learning for Active Breast Lesion Detection from DCE-MRI

The deep Q-network approach is extended, previously demonstrated on simpler problems such as anatomical landmark detection, in order to detect lesions that have a significant variation in shape, appearance, location and size.

Mid-sagittal plane and mid-sagittal surface optimization in brain MRI using a local symmetry measure

An optimization strategy which fits a thin-plate spline surface to the brain data using a robust least median of squares estimator demonstrated convincingly better partitioning of curved brains into cerebral hemispheres.

Human-level control through deep reinforcement learning

This work bridges the divide between high-dimensional sensory inputs and actions, resulting in the first artificial agent that is capable of learning to excel at a diverse array of challenging tasks.

Deep Reinforcement Learning with Double Q-Learning

This paper proposes a specific adaptation to the DQN algorithm and shows that the resulting algorithm not only reduces the observed overestimations, as hypothesized, but that this also leads to much better performance on several games.

Dueling Network Architectures for Deep Reinforcement Learning

This paper presents a new neural network architecture for model-free reinforcement learning that leads to better policy evaluation in the presence of many similar-valued actions and enables the RL agent to outperform the state-of-the-art on the Atari 2600 domain.

Mastering the game of Go with deep neural networks and tree search

Using this search algorithm, the program AlphaGo achieved a 99.8% winning rate against other Go programs, and defeated the human European Go champion by 5 games to 0.5, the first time that a computer program has defeated a human professional player in the full-sized game of Go.

Reinforcement Learning: An Introduction

This book provides a clear and simple account of the key ideas and algorithms of reinforcement learning, which ranges from the history of the field's intellectual foundations to the most recent developments and applications.