Segmentation algorithms of subcortical brain structures on MRI : a review - Archive ouverte HAL Accéder directement au contenu
Article Dans Une Revue Journal of Neuroimage Année : 2014

Segmentation algorithms of subcortical brain structures on MRI : a review

Résumé

Abstract This work covers the current state of the art with regard to approaches to segment subcortical brain structures. A huge range of diverse methods have been presented in the literature during the last decade to segment not only one or a constrained number of structures, but also a complete set of these subcortical regions. Special attention has been paid to atlas based segmentation methods, statistical models and deformable models for this purpose. More recently, the introduction of machine learning techniques, such as artificial neural networks or support vector machines, has helped the researchers to optimize the classification problem. These methods are presented in this work, and their advantages and drawbacks are further discussed. Although these methods have proved to perform well, their use is often limited to those situations where either there are no lesions in the brain or the presence of lesions does not highly vary the brain anatomy. Consequently, the development of segmentation algorithms that can deal with such lesions in the brain and still provide a good performance when segmenting subcortical structures is highly required in practice by some clinical applications, such as radiotherapy or radiosurgery.

Domaines

Cancer
Fichier principal
Vignette du fichier
2014_BrainReview.pdf (338.52 Ko) Télécharger le fichier
Origine : Fichiers produits par l'(les) auteur(s)
Loading...

Dates et versions

hal-01182305 , version 1 (30-11-2015)

Identifiants

  • HAL Id : hal-01182305 , version 1

Citer

J. Dolz, L Massoptier, Maximilien Vermandel. Segmentation algorithms of subcortical brain structures on MRI : a review. Journal of Neuroimage, 2014, pp.200/212. ⟨hal-01182305⟩
111 Consultations
316 Téléchargements

Partager

Gmail Facebook X LinkedIn More