The DOI matches its Crossref record.
Mathematical textbook of deformable neuroanatomies.
Open accessAbstract
Mathematical techniques are presented for the transformation of digital anatomical textbooks from the ideal to the individual, allowing for the representation of the variabilities manifest in normal human anatomies. The ideal textbook is constructed on a fixed coordinate system to contain all of the information currently available about the physical properties of neuroanatomies. This information is obtained via sensor probes such as magnetic resonance, as well as computed axial and emission tomography, along with symbolic information such as white- and gray-matter tracts, nuclei, etc. Human variability associated with individuals is accommodated by defining probabilistic transformations on the textbook coordinate system, the transformations forming mathematical translation groups of high dimension. The ideal is applied to the individual patient by finding the transformation which is consistent with physical properties of deformable elastic solids and which brings the coordinate system of the textbook to that of the patient. Registration, segmentation, and fusion all result automatically because the textbook carries symbolic values as well as multisensor features.
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
Fischl, B., Salat, D. H., Busa, E., Albert, M., Dieterich, M., Haselgrove, C., van der Kouwe, A., Killiany, R., Kennedy, D., Klaveness, S., et al. (2002) · Neuron · doi:10.1016/s0896-6273(02)00569-x · Cited by 8,610
Ashburner, J., & Friston, K. J. (2005) · NeuroImage · doi:10.1016/j.neuroimage.2005.02.018 · Cited by 7,507
Brock, K. K., Mutic, S., McNutt, T. R., Li, H., & Kessler, M. L. (2017) · Medical Physics · doi:10.1002/mp.12256 · Cited by 881
One-Year Brain Atrophy Evident in Healthy Aging
Fjell, A. M., Walhovd, K. B., Fennema-Notestine, C., McEvoy, L. K., Hagler, D. J., Holland, D., Brewer, J. B., & Dale, A. M. (2009) · The Journal of Neuroscience · doi:10.1523/jneurosci.3252-09.2009 · Cited by 692
Variational problems on flows of diffeomorphisms for image matching
Dupuis, P., Grenander, U., & Miller, M. I. (1998) · Quarterly of Applied Mathematics · doi:10.1090/qam/1632326 · Cited by 447
Self-citationComputer-assisted imaging to assess brain structure in healthy and diseased brains
Ashburner, J., Csernansk, J. G., Davatzikos, C., Fox, N. C., Frisoni, G. B., & Thompson, P. M. (2003) · The Lancet Neurology · doi:10.1016/s1474-4422(03)00304-1 · Cited by 371
Csernansky, J. G., Wang, L., Swank, J., Miller, J. P., Gado, M., McKeel, D., Miller, M. I., & Morris, J. C. (2005) · NeuroImage · doi:10.1016/j.neuroimage.2004.12.036 · Cited by 328
Self-citationGhosh, S. S., Kakunoori, S., Augustinack, J., Nieto-Castanon, A., Kovelman, I., Gaab, N., Christodoulou, J. A., Triantafyllou, C., Gabrieli, J. D. E., & Fischl, B. (2010) · NeuroImage · doi:10.1016/j.neuroimage.2010.05.075 · Cited by 281
Wang, L., Swank, J. S., Glick, I. E., Gado, M. H., Miller, M. I., Morris, J. C., & Csernansky, J. G. (2003) · NeuroImage · doi:10.1016/s1053-8119(03)00361-6 · Cited by 263
Self-citationAutomated graph-based analysis and correction of cortical volume topology
Shattuck, D. W., & Leahy, R. M. (2001) · IEEE Transactions on Medical Imaging · doi:10.1109/42.963819 · Cited by 215
Random forest regression for magnetic resonance image synthesis
Jog, A., Carass, A., Roy, S., Pham, D. L., & Prince, J. L. (2017) · Medical Image Analysis · doi:10.1016/j.media.2016.08.009 · Cited by 212
Subregional neuroanatomical change as a biomarker for Alzheimer's disease
Holland, D., Brewer, J. B., Hagler, D. J., Fennema-Notestine, C., Dale, A. M., Initiative, T. A. D. N., Weiner, M., Thal, L., Petersen, R., Jack, C. R., et al. (2009) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.0906053106 · Cited by 210
Metadata
- Authors
- Miller, M. I., Christensen, G. E., Amit, Y., & Grenander, U.
- Venue
- Proceedings of the National Academy of Sciences
- Published
- 1993-12-15
- Volume
- 90
- Issue
- 24
- Pages
- 11944-11948
- ISSN
- 0027-8424 (ISSN-L)
- 1091-6490
- OpenAlex
- W2102849263
- Cited by
- 462
- References
- 0
- FWCI
- 19.65
- Type
- journal article
- Language
- en
- Open access
- bronze
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
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.