Multiple Landmark Detection using Multi-Agent Reinforcement Learning

2019-06-30 · Athanasios Vlontzos, Amir Alansary, Konstantinos Kamnitsas, Daniel Rueckert, Bernhard Kainz

The detection of anatomical landmarks is a vital step for medical image analysis and applications for diagnosis, interpretation and guidance. Manual annotation of landmarks is a tedious process that requires domain-specific expertise and introduces inter-observer variability. This paper proposes a new detection approach for multiple landmarks based on multi-agent reinforcement learning. Our hypothesis is 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. Using a Deep Q-Network (DQN) architecture we construct an environment and agent with implicit inter-communication such that we can accommodate K agents acting and learning simultaneously, while they attempt to detect K different landmarks. During training the agents collaborate by sharing their accumulated knowledge for a collective gain. We compare our approach with state-of-the-art architectures and achieve significantly better accuracy by reducing the detection error by 50%, while requiring fewer computational resources and time to train compared to the naive approach of training K agents separately.

📄 PDF Abstract BibTeX arXiv:1907.00318

Code (1)

thanosvlo/MARL-for-Anatomical-Landmark-Detection 공식 구현 tf

Tasks

AnatomyMedical Image AnalysisMulti-agent Reinforcement Learning reinforcement-learning Reinforcement Learning Reinforcement Learning (RL)

Communicative Reinforcement Learning Agents for Landmark Detection in Brain Images

2020-08-18 · Guy Leroy, Daniel Rueckert, Amir Alansary

Accurate detection of anatomical landmarks is an essential step in several medical imaging tasks. We propose a novel communicative multi-agent reinforcement learning (C-MARL) system to automatically detect landmarks in 3…

Multi-agent Reinforcement Learning reinforcement-learning Reinforcement Learning Reinforcement Learning (RL)

Learn Fine-grained Adaptive Loss for Multiple Anatomical Landmark Detection in Medical Images

2021-05-19 · Guang-Quan Zhou, Juzheng Miao, Xin Yang, Rui Li 외

Automatic and accurate detection of anatomical landmarks is an essential operation in medical image analysis with a multitude of applications. Recent deep learning methods have improved results by directly encoding the a…

Anatomical Landmark Detection Anatomy Medical Image Analysis Reinforcement Learning (RL)

Dual-Agent Deep Reinforcement Learning for Deformable Face Tracking

2018-09-01 · ECCV 2018 9 · Minghao Guo, Jiwen Lu, Jie zhou

In this paper, we propose a dual-agent deep reinforcement learning (DADRL) method for deformable face tracking, which generates bounding boxes and detects facial landmarks interactively from face videos. Most existing de…

Deep Reinforcement Learning Facial Landmark Detection reinforcement-learning Reinforcement Learning+1

Enhanced detection of fetal pose in 3D MRI by Deep Reinforcement Learning with physical structure priors on anatomy

2020-07-16 · Molin Zhang, Junshen Xu, Esra Abaci Turk, P. Ellen Grant 외

Fetal MRI is heavily constrained by unpredictable and substantial fetal motion that causes image artifacts and limits the set of viable diagnostic image contrasts. Current mitigation of motion artifacts is predominantly …

Anatomy Decision Making Deep Reinforcement Learning Diagnostic

Multi-Target Landmark Detection with Incomplete Images via Reinforcement Learning and Shape Prior

2023-01-13 · Kaiwen Wan, Lei LI, Dengqiang Jia, Shangqi Gao 외

Medical images are generally acquired with limited field-of-view (FOV), which could lead to incomplete regions of interest (ROI), and thus impose a great challenge on medical image analysis. This is particularly evident …

Medical Image Analysis Reinforcement Learning (RL)