ReviewJournal of digital imaging2019
RIL-Contour: a Medical Imaging Dataset Annotation Tool for and with Deep Learning.
Review in Journal of digital imaging, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 44 papers.
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Who cites it
44 citing papers in PubMed.
- Optimizing MRI annotation workflows for high-accuracy deep learning thigh muscle segmentation in athletes.Radiology advances · 2026Article
- Automated deep learning pipeline for callosal angle quantification.Fluids and barriers of the CNS · 2025Article
- YOLOv10n-CF-Lite: A Method for Individual Face Recognition of Hu Sheep Based on Automated Annotation and Transfer Learning.Animals : an open access journal from MDPI · 2025Article
- The Evolution of Radiology Image Annotation in the Era of Large Language Models.Radiology. Artificial intelligence · 2025Review
- Domain knowledge-infused pre-trained deep learning models for efficient white blood cell classification.Scientific reports · 2025Article
- Automatic Diagnosis of Hepatocellular Carcinoma and Metastases Based on Computed Tomography Images.Journal of imaging informatics in medicine · 2025Article
- Improving radiologist detection of meniscal abnormality on undersampled, deep learning reconstructed knee MRI.Radiology advances · 2025Article
- MedYOLO: A Medical Image Object Detection Framework.Journal of imaging informatics in medicine · 2024Article
- The ABCs of Subarachnoid Hemorrhage Blood Volume Measurement: A Simplified Quantitative Method Predicts Outcomes and Delayed Cerebral Ischemia.Journal of the American Heart Association · 2024Observational
- BraNet: a mobil application for breast image classification based on deep learning algorithms.Medical & biological engineering & computing · 2024Article
- Mesenchymal Stem Cell Therapy in Acute Intracerebral Hemorrhage: A Dose-Escalation Safety and Tolerability Trial.Neurocritical care · 2024Article
- Artificial Intelligence in Pancreatic Image Analysis: A Review.Sensors (Basel, Switzerland) · 2024Review
- Integrating Clinical Data and Medical Imaging in Lung Cancer: Feasibility Study Using the Observational Medical Outcomes Partnership Common Data Model Extension.JMIR medical informatics · 2024Article
- Efficient musculoskeletal annotation using free-form deformation.Scientific reports · 2024Article
- A Semi-Automatic Magnetic Resonance Imaging Annotation Algorithm Based on Semi-Weakly Supervised Learning.Sensors (Basel, Switzerland) · 2024Article
- Radiomics-Based Classification of Tumor and Healthy Liver on Computed Tomography Images.Cancers · 2024Article
- Image annotation and curation in radiology: an overview for machine learning practitioners.European radiology experimental · 2024Review
- Article
- Artificial intelligence assisted whole organ pancreatic fat estimation on magnetic resonance imaging and correlation with pancreas attenuation on computed tomography.Pancreatology : official journal of the International Association of Pancreatology (IAP) ... [et al.] · 2023Article
- A hybrid method of correcting CBCT for proton range estimation with deep learning and deformable image registration.Physics in medicine and biology · 2023Article
Corrections and comments
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Authors and funding
11 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Deep-learning algorithms typically fall within the domain of supervised artificial intelligence and are designed to "learn" from annotated data. Deep-learning models require large, diverse training datasets for optimal model convergence. The effort to curate these datasets is widely regarded as a barrier to the development of deep-learning systems. We developed RIL-Contour to accelerate medical image annotation for and with deep-learning. A major goal driving the development of the software was to create an environment which enables clinically oriented users to utilize deep-learning models to rapidly annotate medical imaging. RIL-Contour supports using fully automated deep-learning methods, semi-automated methods, and manual methods to annotate medical imaging with voxel and/or text annotations. To reduce annotation error, RIL-Contour promotes the standardization of image annotations across a dataset. RIL-Contour accelerates medical imaging annotation through the process of annotation by iterative deep learning (AID). The underlying concept of AID is to iteratively annotate, train, and utilize deep-learning models during the process of dataset annotation and model development. To enable this, RIL-Contour supports workflows in which multiple-image analysts annotate medical images, radiologists approve the annotations, and data scientists utilize these annotations to train deep-learning models. To automate the feedback loop between data scientists and image analysts, RIL-Contour provides mechanisms to enable data scientists to push deep newly trained deep-learning models to other users of the software. RIL-Contour and the AID methodology accelerate dataset annotation and model development by facilitating rapid collaboration between analysts, radiologists, and engineers.
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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.