Exploitation of local adjacencies for parallel construction of a Reeb graph variant: cerebral vascular tree case

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dc.contributor.authorLepaire, Charles
dc.contributor.authorBelhaouari, Hakim
dc.contributor.authorPascual, Romain
dc.contributor.authorMeseure, Philippe
dc.contributor.editorSkala, Václav
dc.date.accessioned2024-07-21T09:11:46Z
dc.date.available2024-07-21T09:11:46Z
dc.date.issued2024
dc.description.abstract-translatedStrokes concerned more than 795,000 individuals annually in the United States as of 20211 . Detecting thrombus (blood clot) is crucial for aiding surgeons in diagnosis, a process heavily reliant on 3D models reconstructed from medical imaging. While these models are very dense with information (many vertices, edges, faces in the mesh, and noise), extracting the critical data is essential to produce an accurate analysis to support the work of practitioners. Our research, conducted in collaboration with a consortium of surgeons, leverages generalized maps (g-maps) to compute quality criteria on the cerebral vascular tree. According to medical professionals, artifacts due to noise and thin topological changes are significant parameters among these criteria. These parameters can be determined via the Reeb graph, a topological descriptor commonly used in topological data analysis (TDA). In this article, we introduce a novel classification of saddle points, and a Reeb graph variant called the Local to Global Reeb graph (LGRG). We present parallel computation methods for critical points and LGRG, relying only on local information thanks to the homogeneity of the g-map formalism. We show that LGRG preserves the most subtle topological changes while simplifying the input into a graph formalism that respects the global structure of the mesh, allowing its use in future analyses.en
dc.description.sponsorshipThis research work is funded by Region Nouvelle Aquitaine, France (AAP ESR 2021 Program - ARTERIA Project, AAPR2021A-2021-12189710) and SIEMENS Healhineerscs_CZ
dc.description.sponsorshipThis research work is funded by Region Nouvelle Aquitaine, France (AAP ESR 2021 Program - ARTERIA Project, AAPR2021A-2021-12189710) and SIEMENS Healhineersen
dc.format12 s.cs_CZ
dc.format12 s.cs
dc.format.mimetypeapplication/pdf
dc.identifier.citationJournal of WSCG. 2024, vol. 32, no. 1-2, p. 79-90.en
dc.identifier.doihttps://www.doi.org/10.24132/JWSCG.2024.9
dc.identifier.issn1213 – 6972
dc.identifier.issn1213 – 6980 (CD-ROM)
dc.identifier.issn1213 – 6964 (on-line)
dc.identifier.urihttp://hdl.handle.net/11025/57347
dc.language.isoenen
dc.publisherVáclav Skala - UNION Agencycs
dc.rights© Václav Skala - UNION Agencycs_CZ
dc.rights© Václav Skala - UNION Agencyen
dc.rights.accessopenAccessen
dc.subjecttopologická analýza datcs
dc.subjectzobecněné mapycs
dc.subjectparallel computingcs
dc.subjecttopology-based geometric modelingcs
dc.subjectarianta Reebova grafucs
dc.subjectkritické bodycs
dc.subjectcerebrální cévní stromcs
dc.subject.translatedtopological data analysisen
dc.subject.translatedgeneralized mapsen
dc.subject.translatedparalelní počítáníen
dc.subject.translatedgeometrické modelování založené na topologiien
dc.subject.translatedReeb graph varianten
dc.subject.translatedcritical pointsen
dc.subject.translatedcerebral vascular treeen
dc.titleExploitation of local adjacencies for parallel construction of a Reeb graph variant: cerebral vascular tree caseen
dc.typečlánekcs
dc.typearticleen
dc.type.statusPeer-revieweden
dc.type.versionpublishedVersion

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Volume 32, number 1-2 (2024)