Cover Image

Automated detection and segmentation of intracranial hemorrhage suspect hyperdensities in non-contrast-enhanced CT scans of acute stroke patients

Artificial intelligence (AI)-based image analysis is increasingly applied in the acute stroke field. Its implementation for the detection and quantification of hemorrhage suspect hyperdensities in non-contrast-enhanced head CT (NCCT) scans may facilitate clinical decision-making and accelerate strok...

Full description

Saved in:
Bibliographic Details
Main Authors: Schmitt, Niclas (Author) , Mokli, Yahia (Author) , Weyland, Charlotte S. (Author) , Gerry, S. (Author) , Herweh, Christian (Author) , Ringleb, Peter A. (Author) , Nagel, Simon (Author)
Format: Article (Journal)
Language:English
Published: 2022
In: European radiology
Year: 2022, Volume: 32, Issue: 4, Pages: 2246-2254
ISSN:1432-1084
DOI:10.1007/s00330-021-08352-4
Online Access:Verlag, kostenfrei, Volltext: https://doi.org/10.1007/s00330-021-08352-4
Verlag, kostenfrei, Volltext: https://link.springer.com/article/10.1007/s00330-021-08352-4
Get full text
Author Notes:N. Schmitt, Y. Mokli, C.S. Weyland, S. Gerry, C. Herweh, P.A. Ringleb, S. Nagel
Description
Summary:Artificial intelligence (AI)-based image analysis is increasingly applied in the acute stroke field. Its implementation for the detection and quantification of hemorrhage suspect hyperdensities in non-contrast-enhanced head CT (NCCT) scans may facilitate clinical decision-making and accelerate stroke management.
Item Description:Online veröffentlicht am 13. November 2021
Gesehen am 14.12.2022
Physical Description:Online Resource
ISSN:1432-1084
DOI:10.1007/s00330-021-08352-4

Similar Items