The DOI matches its Crossref record.
The Visible Human Project
Abstract
The Visible Human Project data sets are designed to serve as a common reference point for the study of human anatomy, as a set of common public-domain data for testing medical imaging algorithms, and as a testbed and model for the construction of image libraries that can be accessed through networks. The data sets are being applied to a wide range of educational, diagnostic, treatment planning, virtual reality, artistic, mathematical, and industrial uses by more than 800 licensees in 27 countries. But key issues remain in the development of methods to link such image data to text-based data. Standards do not currently exist for such linkages. Basic research is needed in the description and representation of image-based structures and in the connection of image-based structural-anatomical data to text-based functional-physiological data. This is the larger, long-term goal of the Visible Human Project: to link the print library of functional-physiological knowledge with the image library of structural-anatomical knowledge transparently into one unified resource of health information.
Overview
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Integrity signals
Registry
Cites
Statistics
Verification
Claims the paper makes
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
The self-check needs the paper’s full text.
Citations
Cited by
Kainz, W., Neufeld, E., Bolch, W. E., Graff, C. G., Kim, C. H., Kuster, N., Lloyd, B., Morrison, T., Segars, P., Yeom, Y. S., et al. (2019) · IEEE Transactions on Radiation and Plasma Medical Sciences · doi:10.1109/trpms.2018.2883437 · Cited by 109
Sani, A., Alomainy, A., & Hao, Y. (2009) · IEEE Transactions on Microwave Theory and Techniques · doi:10.1109/tmtt.2009.2029770 · Cited by 91
Multi-disciplinary challenges in tissue modeling for wireless electromagnetic powering: a review
Bocan, K. N., Mickle, M. H., & Sejdic, E. (2017) · IEEE Sensors Journal · doi:10.1109/jsen.2017.2748338 · Cited by 41
Metadata
- Authors
- Ackerman, M. J.
- Venue
- Proceedings of the IEEE
- Published
- 1998-03
- Volume
- 86
- Issue
- 3
- Pages
- 504-511
- ISSN
- 0018-9219 (ISSN-L)
- OpenAlex
- W2166657119
- Cited by
- 725
- References
- 0
- FWCI
- 12.30
- Type
- journal article
- Language
- en
- Open access
- closed
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
Are you an author, or do you see a problem with this page? Request a review
How to cite
Cite CoChat for the checks on this page, and the paper for its own findings.
- This page
- CoChat. (2026). Citation check: The Visible Human Project. https://app.cochat.ai/papers/10.1109/5.662875
- The paper
- Ackerman, M. J. (1998). The Visible Human Project. Proceedings of the IEEE. https://doi.org/10.1109/5.662875
Data sources
Bibliographic data: Semantic Scholar, OpenAlex, Crossref.
Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.