Interactive exploration of a 3D intracranial aneurysm wall model extracted from histologic slices.


Journal

International journal of computer assisted radiology and surgery
ISSN: 1861-6429
Titre abrégé: Int J Comput Assist Radiol Surg
Pays: Germany
ID NLM: 101499225

Informations de publication

Date de publication:
Jan 2020
Historique:
received: 30 07 2019
accepted: 18 10 2019
pubmed: 11 11 2019
medline: 2 6 2020
entrez: 10 11 2019
Statut: ppublish

Résumé

Currently no detailed in vivo imaging of the intracranial vessel wall exists. Ex vivo histologic images can provide information about the intracranial aneurysm (IA) wall composition that is useful for the understanding of IA development and rupture risk. For a 3D analysis, the 2D histologic slices must be incorporated in a 3D model which can be used for a spatial evaluation of the IA's morphology, including analysis of the IA neck. In 2D images of histologic slices, different wall layers were manually segmented and a 3D model was generated. The nuclei were automatically detected and classified as round or elongated, and a neural network-based wall type classification was performed. The information was combined in a software prototype visualization providing a unique view of the wall characteristics of an IA and allowing interactive exploration. Furthermore, the heterogeneity (as variance of the wall thickness) of the wall was evaluated. A 3D model correctly representing the histologic data was reconstructed. The visualization integrating wall information was perceived as useful by a medical expert. The classification produces a plausible result. The usage of histologic images allows to create a 3D model with new information about the aneurysm wall. The model provides information about the wall thickness, its heterogeneity and, when performed on cadaveric samples, includes information about the transition between IA neck and sac.

Identifiants

pubmed: 31705419
doi: 10.1007/s11548-019-02083-0
pii: 10.1007/s11548-019-02083-0
doi:

Types de publication

Journal Article

Langues

eng

Sous-ensembles de citation

IM

Pagination

99-107

Subventions

Organisme : Bundesministerium für Bildung und Forschung
ID : 13GW0095A
Organisme : German Research Foundation
ID : SA 3461/2-1

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Auteurs

Annika Niemann (A)

Faculty of Computer Science, Otto-von-Guericke University Magdeburg, Universitätsplatz 2, 39106, Magdeburg, Germany. annika.niemann@ovgu.de.

Simon Weigand (S)

Ludwig-Maximilians-Universität Klinikum, Munich, Germany.

Thomas Hoffmann (T)

Research Campus STIMULATE, Magdeburg, Germany.

Martin Skalej (M)

University Hospital Magdeburg, Magdeburg, Germany.

Riikka Tulamo (R)

Helsinki University Hospital, University of Helsinki, Helsinki, Finland.

Bernhard Preim (B)

Faculty of Computer Science, Otto-von-Guericke University Magdeburg, Universitätsplatz 2, 39106, Magdeburg, Germany.

Sylvia Saalfeld (S)

Faculty of Computer Science, Otto-von-Guericke University Magdeburg, Universitätsplatz 2, 39106, Magdeburg, Germany.
Research Campus STIMULATE, Magdeburg, Germany.

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Classifications MeSH