Semi-supervised segmentation of ultrasound images based on patch representation and continuous min cut.

Détails

Ressource 1Télécharger: BIB_1D06022B9DAB.P001.pdf (3231.96 [Ko])
Etat: Serval
Version: de l'auteur
ID Serval
serval:BIB_1D06022B9DAB
Type
Article: article d'un périodique ou d'un magazine.
Collection
Publications
Titre
Semi-supervised segmentation of ultrasound images based on patch representation and continuous min cut.
Périodique
Plos One
Auteur(s)
Ciurte A., Bresson X., Cuisenaire O., Houhou N., Nedevschi S., Thiran J.P., Cuadra M.B.
ISSN
1932-6203 (Electronic)
ISSN-L
1932-6203
Statut éditorial
Publié
Date de publication
2014
Peer-reviewed
Oui
Volume
9
Numéro
7
Pages
e100972
Langue
anglais
Notes
Publication types: Journal ArticlePublication Status: epublish
Résumé
Ultrasound segmentation is a challenging problem due to the inherent speckle and some artifacts like shadows, attenuation and signal dropout. Existing methods need to include strong priors like shape priors or analytical intensity models to succeed in the segmentation. However, such priors tend to limit these methods to a specific target or imaging settings, and they are not always applicable to pathological cases. This work introduces a semi-supervised segmentation framework for ultrasound imaging that alleviates the limitation of fully automatic segmentation, that is, it is applicable to any kind of target and imaging settings. Our methodology uses a graph of image patches to represent the ultrasound image and user-assisted initialization with labels, which acts as soft priors. The segmentation problem is formulated as a continuous minimum cut problem and solved with an efficient optimization algorithm. We validate our segmentation framework on clinical ultrasound imaging (prostate, fetus, and tumors of the liver and eye). We obtain high similarity agreement with the ground truth provided by medical expert delineations in all applications (94% DICE values in average) and the proposed algorithm performs favorably with the literature.
Pubmed
Web of science
Open Access
Oui
Création de la notice
04/07/2014 16:38
Dernière modification de la notice
08/05/2019 15:22
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