Fusion of Multi-Atlas Segmentations with Spatial Distribution Modeling

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ID Serval
serval:BIB_0A1525065936
Type
Actes de conférence (partie): contribution originale à la littérature scientifique, publiée à l'occasion de conférences scientifiques, dans un ouvrage de compte-rendu (proceedings), ou dans l'édition spéciale d'un journal reconnu (conference proceedings).
Collection
Publications
Titre
Fusion of Multi-Atlas Segmentations with Spatial Distribution Modeling
Titre de la conférence
MICCAI 2011, 14th International Conference on Medical Image Computing and Computer Assisted Intervention
Auteur(s)
Gorthi S., Bach Cuadra M., Schick U., Tercier P.A., Allal A.S., Thiran J.P.
Adresse
Toronto, Canada, September 18-22, 2011
Statut éditorial
Publié
Date de publication
2011
Langue
anglais
Résumé
In recent years, multi-atlas fusion methods have gainedsignificant attention in medical image segmentation. Inthis paper, we propose a general Markov Random Field(MRF) based framework that can perform edge-preservingsmoothing of the labels at the time of fusing the labelsitself. More specifically, we formulate the label fusionproblem with MRF-based neighborhood priors, as an energyminimization problem containing a unary data term and apairwise smoothness term. We present how the existingfusion methods like majority voting, global weightedvoting and local weighted voting methods can be reframedto profit from the proposed framework, for generatingmore accurate segmentations as well as more contiguoussegmentations by getting rid of holes and islands. Theproposed framework is evaluated for segmenting lymphnodes in 3D head and neck CT images. A comparison ofvarious fusion algorithms is also presented.
Mots-clé
LTS, LTS5
Création de la notice
29/11/2011 17:40
Dernière modification de la notice
20/08/2019 13:32
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