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

@article{Dou2019AgentWW,
  title={Agent with Warm Start and Active Termination for Plane Localization in 3D Ultrasound},
  author={Haoran Dou and Xin Yang and Jikuan Qian and Wufeng Xue and Hao Qin and Xu Wang and Lequan Yu and Shujun Wang and Yi Xiong and Pheng-Ann Heng and Dong Ni},
  journal={ArXiv},
  year={2019},
  volume={abs/1910.04331}
}
Standard plane localization is crucial for ultrasound (US) diagnosis. In prenatal US, dozens of standard planes are manually acquired with a 2D probe. It is time-consuming and operator-dependent. In comparison, 3D US containing multiple standard planes in one shot has the inherent advantages of less user-dependency and more efficiency. However, manual plane localization in US volume is challenging due to the huge search space and large fetal posture variation. In this study, we propose a novel… 
Agent With Warm Start and Adaptive Dynamic Termination for Plane Localization in 3D Ultrasound
TLDR
This study enhances the previous RL framework with a newly designed adaptive dynamic termination to enable an early stop for the agent searching, saving at most 67% inference time, thus boosting the accuracy and efficiency of the RL framework at the same time.
Searching Collaborative Agents for Multi-plane Localization in 3D Ultrasound
TLDR
A novel Multi-Agent Reinforcement Learning (MARL) framework to localize multiple uterine SPs in 3D US simultaneously and proposes a novel collaborative strategy to strengthen agents' communication.
Standard Plane Extraction From 3D Ultrasound With 6-DOF Deep Reinforcement Learning Agent
TLDR
A deep Q-network agent is used to model the process of iteratively searching for the target 2D view from a 3D volume and is able to produce a 6-Degree-of-Freedom (DOF) discrete action that moves the plane one step closer to the target.
Autonomous Navigation of an Ultrasound Probe Towards Standard Scan Planes with Deep Reinforcement Learning
  • Keyu Li, Jian Wang, M. Meng
  • Computer Science
    2021 IEEE International Conference on Robotics and Automation (ICRA)
  • 2021
TLDR
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.
Image-Guided Navigation of a Robotic Ultrasound Probe for Autonomous Spinal Sonography Using a Shadow-Aware Dual-Agent Framework
TLDR
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.
Learn Fine-Grained Adaptive Loss for Multiple Anatomical Landmark Detection in Medical Images
TLDR
This work proposes a novel learning-to-learn framework for landmark detection to optimize the neural network and the target precision simultaneously and introduces an early-stop strategy for active termination of the RL agent's interaction that adapts the optimal precision for separate targets considering exploration-exploitation tradeoffs.
Label Efficient Localization of Fetal Brain Biometry Planes in Ultrasound Through Metric Learning
TLDR
A framework for automatic quality assessment of freehand fetal ultrasound video that has been designed and built subject to constraints such as those encountered in low-income settings is described: ultrasound data acquired by minimally trained users, using a low-cost ultrasound probe and android tablet.
A Review on Deep-Learning Algorithms for Fetal Ultrasound-Image Analysis
TLDR
A critical summary of the current state of the art on DL algorithms for fetal US image analysis and a discussion on current challenges that have to be tackled by researchers working in the field to translate the research methodology into the actual clinical practice are discussed.
...
1
2
...

References

SHOWING 1-10 OF 11 REFERENCES
Automatic View Planning with Multi-scale Deep Reinforcement Learning Agents
TLDR
A fully automatic method to find standardized view planes in 3D image acquisitions by employing a multi-scale reinforcement learning (RL) agent framework and extensively evaluate several Deep Q-Network (DQN) based strategies is proposed.
Standard Plane Detection in 3D Fetal Ultrasound Using an Iterative Transformation Network
TLDR
This work proposes a new Iterative Transformation Network (ITN) for the automatic detection of standard planes in 3D volumes and introduces additional classification probability outputs to the network to act as confidence measures for the regressed transformation parameters in order to further improve the localisation accuracy.
Multi-Scale Deep Reinforcement Learning for Real-Time 3D-Landmark Detection in CT Scans
TLDR
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.
Offset regression networks for view plane estimation in 3D fetal ultrasound
TLDR
A novel framework for deep hyperplane learning is proposed and applied for view plane estimation in fetal US examinations and a high accuracy is obtained, outperforming or comparable to recent publications on the same application.
Automated abdominal plane and circumference estimation in 3D US for fetal screening
TLDR
A fully automated pipeline has been designed starting with a random forest based anatomical landmark detection to address a part of the canonical fetal screening program, namely the localization of the abdominal cross-sectional plane with the corresponding measurement of the abdomen circumference in this plane.
Automated 3D Ultrasound Biometry Planes Extraction for First Trimester Fetal Assessment
TLDR
A fully automated machine-learning based solution to localize the fetus and extract the best fetal biometry planes for the head and abdomen from 11–13+6days week 3D fetal ultrasound (US) images is presented.
U-Net: Convolutional Networks for Biomedical Image Segmentation
TLDR
It is shown that such a network can be trained end-to-end from very few images and outperforms the prior best method (a sliding-window convolutional network) on the ISBI challenge for segmentation of neuronal structures in electron microscopic stacks.
Diagnostic Plane Extraction from 3D Parametric Surface of the Fetal Cranium
In this paper, we evaluate the viability of using a 3D parametric surface model of the fetal cranium to extract diagnostic 2D ultrasound (US) image planes and biometric measurements useful in fetal
Class-Specific Regression Random Forest for Accurate Extraction of Standard Planes from 3D Echocardiography
TLDR
A class-specific regression random forest is proposed as a fully automatic algorithm for extraction of the standard view planes from 3D echocardiography by integrating the voxel class label information into the training of the regression forest to exclude irrelevant classes from voting.
Deep Reinforcement Learning with Double Q-Learning
TLDR
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.
...
1
2
...