ArticleMedical image analysis2019
Evaluating reinforcement learning agents for anatomical landmark detection.
Article in Medical image analysis, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 39 papers.
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39 citing papers in PubMed, 185 citations in OpenAlex.
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- Promptable segmentation with region exploration enables minimal-effort expert-level prostate cancer delineation.International journal of computer assisted radiology and surgery · 2026Article
- Deep reinforcement learning for automatic anatomic CT landmark localization in Stanford Type B aortic dissection.Radiology advances · 2026Article
- Autonomous path planning for intercostal robotic ultrasound imaging using reinforcement learning.Scientific reports · 2026Article
- Automatic assessment of lower limb deformities using high-resolution X-ray images.BMC musculoskeletal disorders · 2025Article
- Review of the Current State of Artificial Intelligence in Pediatric Cardiovascular Magnetic Resonance Imaging.Children (Basel, Switzerland) · 2025Review
- Deep learning for opportunistic, end-to-end automated assessment of epicardial adipose tissue in pre-interventional, ECG-gated spiral computed tomography.Insights into imaging · 2024Article
- Centerline-guided reinforcement learning model for pancreatic duct identifications.Journal of medical imaging (Bellingham, Wash.) · 2024Article
- Advancements in Artificial Intelligence for Fetal Neurosonography: A Comprehensive Review.Journal of clinical medicine · 2024Review
- Automatic detection, segmentation, and classification of primary bone tumors and bone infections using an ensemble multi-task deep learning framework on multi-parametric MRIs: a multi-center study.European radiology · 2024Article
- PELE scores: pelvic X-ray landmark detection with pelvis extraction and enhancement.International journal of computer assisted radiology and surgery · 2024Article
- Transfer Learning in Deep Reinforcement Learning: A Survey.IEEE transactions on pattern analysis and machine intelligence · 2023Article
- Evolving the Era of 5D Ultrasound? A Systematic Literature Review on the Applications for Artificial Intelligence Ultrasound Imaging in Obstetrics and Gynecology.Journal of clinical medicine · 2023Review
- Effects of Path-Finding Algorithms on the Labeling of the Centerlines of Circle of Willis Arteries.Tomography (Ann Arbor, Mich.) · 2023Article
- An Ensemble of Deep Learning Object Detection Models for Anatomical and Pathological Regions in Brain MRI.Diagnostics (Basel, Switzerland) · 2023Article
- Reinforcement learning in medical image analysis: Concepts, applications, challenges, and future directions.Journal of applied clinical medical physics · 2023Review
- A deep learning approach to predict collateral flow in stroke patients using radiomic features from perfusion images.Frontiers in neurology · 2023Article
- An Automatic Image Processing Method Based on Artificial Intelligence for Locating the Key Boundary Points in the Central Serous Chorioretinopathy Lesion Area.Computational intelligence and neuroscience · 2023Article
- Reinforcement learning using DeepBMC medical imaging · 2022Article
Corrections and comments
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Authors and funding
11 authors at 2 institutions in 1 country.
Funding
Abstract
Automatic detection of anatomical landmarks is an important step for a wide range of applications in medical image analysis. Manual annotation of landmarks is a tedious task and prone to observer errors. In this paper, we evaluate novel deep reinforcement learning (RL) strategies to train agents that can precisely and robustly localize target landmarks in medical scans. An artificial RL agent learns to identify the optimal path to the landmark by interacting with an environment, in our case 3D images. Furthermore, we investigate the use of fixed- and multi-scale search strategies with novel hierarchical action steps in a coarse-to-fine manner. Several deep Q-network (DQN) architectures are evaluated for detecting multiple landmarks using three different medical imaging datasets: fetal head ultrasound (US), adult brain and cardiac magnetic resonance imaging (MRI). The performance of our agents surpasses state-of-the-art supervised and RL methods. Our experiments also show that multi-scale search strategies perform significantly better than fixed-scale agents in images with large field of view and noisy background such as in cardiac MRI. Moreover, the novel hierarchical steps can significantly speed up the searching process by a factor of 4-5 times.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.