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Automatically Parcellating the Human Cerebral Cortex
Abstract
We present a technique for automatically assigning a neuroanatomical label to each location on a cortical surface model based on probabilistic information estimated from a manually labeled training set. This procedure incorporates both geometric information derived from the cortical model, and neuroanatomical convention, as found in the training set. The result is a complete labeling of cortical sulci and gyri. Examples are given from two different training sets generated using different neuroanatomical conventions, illustrating the flexibility of the algorithm. The technique is shown to be comparable in accuracy to manual labeling.
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Self-citationAutomatic 3D intersubject registration of MR volumetric data in standardized Talairach space
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“The alternative is to compile a probabilistic atlas based on the anatomy of a large number of subjects (e.g. Collins et al. , 1994; Fox et al. , 1994; Mazziotta et al. , 1995; Thompson et al. , 1997, 2000).”
BackgroundThree-dimensional statistical analysis of sulcal variability in the human brain
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“Such parcellations include predefined anatomical segregations (e.g., Brodmann areas, Desikan-Killiany atlas [118], and anatomical automatic labeling map [119]), atlas-independent random cortical segmentation [24], and data-driven clusters, which are often derived from functional correspondence…”
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“1 23–25 .”
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Metadata
- Authors
- Fischl, B.
- Venue
- Cerebral Cortex
- Publisher
- Oxford University Press (OUP)
- Published
- 2004-01-01
- Volume
- 14
- Issue
- 1
- Pages
- 11-22
- ISSN
- 1047-3211 (ISSN-L)
- 1460-2199
- OpenAlex
- W2151130155
- Cited by
- 4,253
- References
- 3
- FWCI
- 14.57
- 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 ›
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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.
