Stacking denoising auto-encoders in a deep ...
Type de document :
Article dans une revue scientifique: Article original
PMID :
URL permanente :
Titre :
Stacking denoising auto-encoders in a deep network to segment the brainstem on mri in brain cancer patients: a clinical study
Auteur(s) :
Dolz, Jose [Auteur]
Betrouni, Nacim [Auteur]
Troubles cognitifs dégénératifs et vasculaires - U 1171 - EA 1046 [TCDV]
Troubles cognitifs dégénératifs et vasculaires - U1171
Quidet, Mathilde [Auteur]
Kharroubi, Dris [Auteur]
Leroy, Henri-Arthur [Auteur]
Thérapies Assistées par Lasers et Immunothérapies pour l'Oncologie - U 1189 [OncoThAI]
Reyns, Nicolas [Auteur]
Thérapies Assistées par Lasers et Immunothérapies pour l'Oncologie - U 1189 [OncoThAI]
Thérapies Lasers Assistées par l'Image pour l'Oncologie (ONCO-THAI) - U1189
Massoptier, Laurent [Auteur]
Vermandel, Maximilien [Auteur]
Thérapies Assistées par Lasers et Immunothérapies pour l'Oncologie - U 1189 [OncoThAI]
Thérapies Assistées par Lasers et Immunothérapies pour l'Oncologie - U 1189 [OncoThAI]
Thérapies Lasers Assistées par l'Image pour l'Oncologie (ONCO-THAI) - U1189
Betrouni, Nacim [Auteur]
Troubles cognitifs dégénératifs et vasculaires - U 1171 - EA 1046 [TCDV]
Troubles cognitifs dégénératifs et vasculaires - U1171
Quidet, Mathilde [Auteur]
Kharroubi, Dris [Auteur]
Leroy, Henri-Arthur [Auteur]
Thérapies Assistées par Lasers et Immunothérapies pour l'Oncologie - U 1189 [OncoThAI]
Reyns, Nicolas [Auteur]
Thérapies Assistées par Lasers et Immunothérapies pour l'Oncologie - U 1189 [OncoThAI]
Thérapies Lasers Assistées par l'Image pour l'Oncologie (ONCO-THAI) - U1189
Massoptier, Laurent [Auteur]
Vermandel, Maximilien [Auteur]
Thérapies Assistées par Lasers et Immunothérapies pour l'Oncologie - U 1189 [OncoThAI]
Thérapies Assistées par Lasers et Immunothérapies pour l'Oncologie - U 1189 [OncoThAI]
Thérapies Lasers Assistées par l'Image pour l'Oncologie (ONCO-THAI) - U1189
Titre de la revue :
Computerized medical imaging and graphics . the official journal of the Computerized Medical Imaging Society
Nom court de la revue :
Comput. Med. Imaging Graph.
Numéro :
52
Pagination :
8-18
Date de publication :
2016-09-01
ISSN :
0895-6111
Mot(s)-clé(s) en anglais :
Brain cancer
Machine learning
Deep learning
MRI segmentation
Machine learning
Deep learning
MRI segmentation
Discipline(s) HAL :
Sciences du Vivant [q-bio]
Résumé en anglais : [en]
Delineation of organs at risk (OARs) is a crucial step in surgical and treatment planning in brain cancer, where precise OARs volume delineation is required. However, this task is still often manually performed, which is ...
Lire la suite >Delineation of organs at risk (OARs) is a crucial step in surgical and treatment planning in brain cancer, where precise OARs volume delineation is required. However, this task is still often manually performed, which is time-consuming and prone to observer variability. To tackle these issues a deep learning approach based on stacking denoising auto-encoders has been proposed to segment the brainstem on magnetic resonance images in brain cancer context. Additionally to classical features used in machine learning to segment brain structures, two new features are suggested. Four experts participated in this study by segmenting the brainstem on 9 patients who underwent radiosurgery. Analysis of variance on shape and volume similarity metrics indicated that there were significant differences (p<0.05) between the groups of manual annotations and automatic segmentations. Experimental evaluation also showed an overlapping higher than 90% with respect to the ground truth. These results are comparable, and often higher, to those of the state of the art segmentation methods but with a considerably reduction of the segmentation time.Lire moins >
Lire la suite >Delineation of organs at risk (OARs) is a crucial step in surgical and treatment planning in brain cancer, where precise OARs volume delineation is required. However, this task is still often manually performed, which is time-consuming and prone to observer variability. To tackle these issues a deep learning approach based on stacking denoising auto-encoders has been proposed to segment the brainstem on magnetic resonance images in brain cancer context. Additionally to classical features used in machine learning to segment brain structures, two new features are suggested. Four experts participated in this study by segmenting the brainstem on 9 patients who underwent radiosurgery. Analysis of variance on shape and volume similarity metrics indicated that there were significant differences (p<0.05) between the groups of manual annotations and automatic segmentations. Experimental evaluation also showed an overlapping higher than 90% with respect to the ground truth. These results are comparable, and often higher, to those of the state of the art segmentation methods but with a considerably reduction of the segmentation time.Lire moins >
Langue :
Anglais
Audience :
Internationale
Vulgarisation :
Non
Établissement(s) :
CHU Lille
CNRS
Inserm
Université de Lille
CNRS
Inserm
Université de Lille
Date de dépôt :
2019-11-27T13:33:54Z
2021-05-18T13:16:22Z
2021-05-18T13:16:22Z