dc.contributor.author | Fernández-Pena, Alberto | |
dc.contributor.author | Martín de Blas, Daniel | |
dc.contributor.author | Navas-Sánchez, Francisco J | |
dc.contributor.author | Marcos-Vidal, Luis | |
dc.contributor.author | M Gordaliza, Pedro | |
dc.contributor.author | Santonja, Javier | |
dc.contributor.author | Janssen, Joost | |
dc.contributor.author | Carmona, Susanna | |
dc.contributor.author | Desco, Manuel | |
dc.contributor.author | Alemán-Gómez, Yasser | |
dc.date.accessioned | 2023-03-22T11:27:22Z | |
dc.date.available | 2023-03-22T11:27:22Z | |
dc.date.issued | 2023-01 | |
dc.identifier.citation | Neuroinformatics. 2023 Jan;21(1):145-162 | es_ES |
dc.identifier.uri | http://hdl.handle.net/20.500.12105/15682 | |
dc.description.abstract | The archetypical folded shape of the human cortex has been a long-standing topic for neuroscientific research. Nevertheless, the accurate neuroanatomical segmentation of sulci remains a challenge. Part of the problem is the uncertainty of where a sulcus transitions into a gyrus and vice versa. This problem can be avoided by focusing on sulcal fundi and gyral crowns, which represent the topological opposites of cortical folding. We present Automated Brain Lines Extraction (ABLE), a method based on Laplacian surface collapse to reliably segment sulcal fundi and gyral crown lines. ABLE is built to work on standard FreeSurfer outputs and eludes the delineation of anastomotic sulci while maintaining sulcal fundi lines that traverse the regions with the highest depth and curvature. First, it segments the cortex into gyral and sulcal surfaces; then, each surface is spatially filtered. A Laplacian-collapse-based algorithm is applied to obtain a thinned representation of the surfaces. This surface is then used for careful detection of the endpoints of the lines. Finally, sulcal fundi and gyral crown lines are obtained by eroding the surfaces while preserving the connectivity between the endpoints. The method is validated by comparing ABLE with three other sulcal extraction methods using the Human Connectome Project (HCP) test-retest database to assess the reproducibility of the different tools. The results confirm ABLE as a reliable method for obtaining sulcal lines with an accurate representation of the sulcal topology while ignoring anastomotic branches and the overestimation of the sulcal fundi lines. ABLE is publicly available via https://github.com/HGGM-LIM/ABLE . | es_ES |
dc.description.sponsorship | This work was supported by the project exAScale ProgramIng
models for extreme Data procEssing (ASPIDE), that has received funding
from the European Union’s Horizon 2020 research and innovation program
under grant agreement No 801091. This work has received funding from
“la Caixa” Foundation under the project code LCF/PR/HR19/52160001.
Susanna Carmona funded by Instituto de Salud Carlos III, co-funded by
European Social Fund “Investing in your future” (Miguel Servet Type
I research contract CP16/00096). The CNIC is supported by the Instituto de Salud Carlos III (ISCIII), the Ministerio de Ciencia e Innovación
(MCIN) and the Pro CNIC Foundation, and is a Severo Ochoa Center of
Excellence (SEV-2015-0505). Yasser Alemán-Gómez is supported by the
Swiss National Science Foundation (185897) and the National Center of
Competence in Research (NCCR) SYNAPSY - The Synaptic Bases of
Mental Diseases, funded as well by the Swiss National Science Foundation (51AU40-1257). | es_ES |
dc.language.iso | eng | es_ES |
dc.publisher | Humana Press | es_ES |
dc.type.hasVersion | VoR | es_ES |
dc.rights.uri | http://creativecommons.org/licenses/by/4.0/ | * |
dc.subject.mesh | Magnetic Resonance Imaging | es_ES |
dc.subject.mesh | Connectome | es_ES |
dc.subject.mesh | Humans | es_ES |
dc.subject.mesh | Reproducibility of Results | es_ES |
dc.subject.mesh | Cerebral Cortex | es_ES |
dc.subject.mesh | Brain | es_ES |
dc.title | ABLE: Automated Brain Lines Extraction Based on Laplacian Surface Collapse. | es_ES |
dc.type | journal article | es_ES |
dc.rights.license | Atribución 4.0 Internacional | * |
dc.identifier.pubmedID | 36008650 | es_ES |
dc.format.volume | 21 | es_ES |
dc.format.number | 1 | es_ES |
dc.format.page | 145 | es_ES |
dc.identifier.doi | 10.1007/s12021-022-09601-7 | es_ES |
dc.contributor.funder | Unión Europea. Comisión Europea. H2020 | es_ES |
dc.contributor.funder | Fundación La Caixa | es_ES |
dc.contributor.funder | Instituto de Salud Carlos III | es_ES |
dc.contributor.funder | Unión Europea. Fondo Social Europeo (ESF/FSE) | es_ES |
dc.contributor.funder | Fundación ProCNIC | es_ES |
dc.contributor.funder | Ministerio de Ciencia e Innovación. Centro de Excelencia Severo Ochoa (España) | es_ES |
dc.contributor.funder | Swiss National Science Foundation | es_ES |
dc.description.peerreviewed | Sí | es_ES |
dc.identifier.e-issn | 1559-0089 | es_ES |
dc.relation.publisherversion | 10.1007/s12021-022-09601-7 | es_ES |
dc.identifier.journal | Neuroinformatics | es_ES |
dc.repisalud.orgCNIC | CNIC::Unidades técnicas::Imagen Avanzada | es_ES |
dc.repisalud.institucion | CNIC | es_ES |
dc.relation.projectID | info:eu-repo/grantAgreement/EC/H2020/801091 | es_ES |
dc.rights.accessRights | open access | es_ES |
dc.relation.projectFECYT | info:eu-repo/grantAgreement/ES/LCF/PR/HR19/52160001 | es_ES |
dc.relation.projectFECYT | info:eu-repo/grantAgreement/ES/CP16/00096 | es_ES |
dc.relation.projectFECYT | info:eu-repo/grantAgreement/ES/SEV-2015-0505 | es_ES |