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Gland and zonal segmentation of prostate ...
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Document type :
Article dans une revue scientifique: Article original
DOI :
10.1007/s10278-016-9890-0
PMID :
27363993
Permalink :
http://hdl.handle.net/20.500.12210/16024
Title :
Gland and zonal segmentation of prostate on t2w mr images
Author(s) :
Chilali, O. [Auteur]
Puech, Philippe [Auteur] refId
Thérapies Lasers Assistées par l'Image pour l'Oncologie (ONCO-THAI) - U1189
Lakroum, Said [Auteur]
Diaf, M. [Auteur]
Mordon, Serge [Auteur] refId
Thérapies Lasers Assistées par l'Image pour l'Oncologie (ONCO-THAI) - U1189
Betrouni, Nacim [Auteur] refId
Troubles cognitifs dégénératifs et vasculaires - U1171
Journal title :
Journal of digital imaging
Abbreviated title :
J. Digit. Imaging
Volume number :
29
Pages :
730-736
Publication date :
2016-12-01
ISSN :
0897-1889
English keyword(s) :
Zones
Prostate
Atlas
Segmentation
MRT2W images
HAL domain(s) :
Sciences du Vivant [q-bio]
English abstract : [en]
For many years, prostate segmentation on MR images concerned only the extraction of the entire gland. Currently, in the focal treatment era, there is a continuously increasing need for the separation of the different parts ...
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For many years, prostate segmentation on MR images concerned only the extraction of the entire gland. Currently, in the focal treatment era, there is a continuously increasing need for the separation of the different parts of the organ. In this paper, we propose an automatic segmentation method based on the use of T2W images and atlas images to segment the prostate and to isolate the peripheral and transition zones. The algorithm consists of two stages. First, the target image is registered with each zonal atlas image then the segmentation is obtained by the application of an evidential C-Means clustering. The method was evaluated on a representative and multi-centric image base and yielded mean Dice accuracy values of 0.81, 0.70, and 0.62 for the prostate, the transition zone, and peripheral zone, respectively.Show less >
Language :
Anglais
Audience :
Internationale
Popular science :
Non
Administrative institution(s) :
CHU Lille
CNRS
Inserm
Université de Lille
Collections :
  • Thérapies Lasers Assistées par l'Image pour l'Oncologie (ONCO-THAI) - U1189
  • Troubles cognitifs dégénératifs et vasculaires - U1171
Submission date :
2019-11-27T13:04:04Z
Université de Lille

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