Supervised machine learning based classification scheme to segment the brainstem on MRI in multicenter brain tumor treatment context

Abstract : Purpose To constrain the risk of severe toxicity in radiotherapy and radiosurgery, precise volume delineation of organs at risk (OARs) is required. This task is still manually performed, which is time-consuming and prone to observer variability. To address these issues, and as alternative to atlas-based segmentation methods, machine learning techniques, such as support vector machines (SVM), have been recently presented to segment subcortical structures on magnetic resonance images (MRI).
Document type :
Journal articles
Liste complète des métadonnées

https://hal.archives-ouvertes.fr/hal-01181348
Contributor : Serge Mordon <>
Submitted on : Thursday, July 30, 2015 - 8:35:22 AM
Last modification on : Friday, April 12, 2019 - 4:22:56 PM
Document(s) archivé(s) le : Saturday, October 31, 2015 - 10:25:07 AM

File

Dolz-Laprie-Ken-Leroy-Reyns-Ma...
Files produced by the author(s)

Identifiers

  • HAL Id : hal-01181348, version 1

Collections

Citation

J. Dolz, Anne Laprie, Soléakhéna Ken, Henri-Arthur Leroy, Nicolas Reyns, et al.. Supervised machine learning based classification scheme to segment the brainstem on MRI in multicenter brain tumor treatment context. Int J Comput Assisted Radiol Surg, 2015, pp.1/16. ⟨hal-01181348⟩

Share

Metrics

Record views

122

Files downloads

142