ArticleJournal of digital imaging2010
The caBIG annotation and image Markup project.
Article in Journal of digital imaging, 2010. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 54 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
54 citing papers in PubMed.
- Cross-Institutional Evaluation of Large Language Models for Radiology Diagnosis Extraction: A Prompt-Engineering Perspective.Journal of imaging informatics in medicine · 2026Article
- Standardizing DICOM annotation: deep learning enhances body part description in X-ray image retrieval for clinical research.BMC medical imaging · 2025Article
- Image annotation and curation in radiology: an overview for machine learning practitioners.European radiology experimental · 2024Review
- MI-Common Data Model: Extending Observational Medical Outcomes Partnership-Common Data Model (OMOP-CDM) for Registering Medical Imaging Metadata and Subsequent Curation Processes.JCO clinical cancer informatics · 2023Article
- Toward Practical Integration of Omic and Imaging Data in Co-Clinical Trials.Tomography (Ann Arbor, Mich.) · 2023Article
- BlueLight: An Open Source DICOM Viewer Using Low-Cost Computation Algorithm Implemented with JavaScript Using Advanced Medical Imaging Visualization.Journal of digital imaging · 2023Article
- Artificial Intelligence for Radiation Oncology Applications Using Public Datasets.Seminars in radiation oncology · 2022Review
- Quantitative imaging feature pipeline: a web-based tool for utilizing, sharing, and building image-processing pipelines.Journal of medical imaging (Bellingham, Wash.) · 2020Article
- Automatic Staging of Cancer Tumors Using AIM Image Annotations and Ontologies.Journal of digital imaging · 2020Article
- Advancing Semantic Interoperability of Image Annotations: Automated Conversion of Non-standard Image Annotations in a Commercial PACS to the Annotation and Image Markup.Journal of digital imaging · 2020Article
- ePAD: An Image Annotation and Analysis Platform for Quantitative Imaging.Tomography (Ann Arbor, Mich.) · 2019Article
- Quantitative imaging of cancer in the postgenomic era: Radio(geno)mics, deep learning, and habitats.Cancer · 2018Review
- Development and Reliability of a User-Friendly Multicenter Phenotyping Application for Hemorrhagic and Ischemic Stroke.Journal of stroke and cerebrovascular diseases : the official journal of National Stroke Association · 2017Article
- A Containerized Software System for Generation, Management, and Exploration of Features from Whole Slide Tissue Images.Cancer research · 2017Article
- Building and Querying RDF/OWL Database of Semantically Annotated Nuclear Medicine Images.Journal of digital imaging · 2017Article
- Towards Generation, Management, and Exploration of Combined Radiomics and Pathomics Datasets for Cancer Research.AMIA Joint Summits on Translational Science proceedings. AMIA Joint Summits on Translational Science · 2017Article
- A Comparison of Lung Nodule Segmentation Algorithms: Methods and Results from a Multi-institutional Study.Journal of digital imaging · 2016Article
- Using Object Storage Technology vs Vendor Neutral Archives for an Image Data Repository Infrastructure.Journal of digital imaging · 2016Article
- Imaging Informatics: 25 Years of Progress.Yearbook of medical informatics · 2016Article
- Semantic representation of reported measurements in radiology.BMC medical informatics and decision making · 2016Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
5 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Image annotation and markup are at the core of medical interpretation in both the clinical and the research setting. Digital medical images are managed with the DICOM standard format. While DICOM contains a large amount of meta-data about whom, where, and how the image was acquired, DICOM says little about the content or meaning of the pixel data. An image annotation is the explanatory or descriptive information about the pixel data of an image that is generated by a human or machine observer. An image markup is the graphical symbols placed over the image to depict an annotation. While DICOM is the standard for medical image acquisition, manipulation, transmission, storage, and display, there are no standards for image annotation and markup. Many systems expect annotation to be reported verbally, while markups are stored in graphical overlays or proprietary formats. This makes it difficult to extract and compute with both of them. The goal of the Annotation and Image Markup (AIM) project is to develop a mechanism, for modeling, capturing, and serializing image annotation and markup data that can be adopted as a standard by the medical imaging community. The AIM project produces both human- and machine-readable artifacts. This paper describes the AIM information model, schemas, software libraries, and tools so as to prepare researchers and developers for their use of AIM.
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What OpenQuestion holds
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.