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Cited by 992

The DOI matches its Crossref record; 49 of the 49 references it cites were found in a registry or an index.

Geometrically Accurate Topology-Correction of Cortical Surfaces Using Nonseparating Loops

Segonne, Florent, Pacheco, Jenni, Fischl, Bruce·2007·IEEE Transactions on Medical Imaging

Abstract

In this paper, we focus on the retrospective topology correction of surfaces. We propose a technique to accurately correct the spherical topology of cortical surfaces. Specifically, we construct a mapping from the original surface onto the sphere to detect topological defects as minimal nonhomeomorphic regions. The topology of each defect is then corrected by opening and sealing the surface along a set of nonseparating loops that are selected in a Bayesian framework. The proposed method is a wholly self-contained topology correction algorithm, which determines geometrically accurate, topologically correct solutions based on the magnetic resonance imaging (MRI) intensity profile and the expected local curvature. Applied to real data, our method provides topological corrections similar to those made by a trained operator.

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Metadata

Authors
Segonne, F., Pacheco, J., & Fischl, B.
Venue
IEEE Transactions on Medical Imaging
Published
2007-04
Volume
26
Issue
4
Pages
518-529
ISSN
0278-0062 (ISSN-L)
OpenAlex
W2105086806
Cited by
992
References
49
FWCI
19.60
Type
journal article
Language
en
Open access
closed

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CoChat. (2026). Citation check: Geometrically Accurate Topology-Correction of Cortical Surfaces Using Nonseparating Loops. https://app.cochat.ai/papers/10.1109/tmi.2006.887364
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Segonne, F., Pacheco, J., & Fischl, B. (2007). Geometrically Accurate Topology-Correction of Cortical Surfaces Using Nonseparating Loops. IEEE Transactions on Medical Imaging. https://doi.org/10.1109/tmi.2006.887364

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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.