Interactive exploration of a 3D intracranial aneurysm wall model extracted from histologic slices.
Aneurysm wall
Histologic images
Intracranial aneurysms
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
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
99-107Subventions
Organisme : Bundesministerium für Bildung und Forschung
ID : 13GW0095A
Organisme : German Research Foundation
ID : SA 3461/2-1
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