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Stacking denoising auto-encoders in a deep ...
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Document type :
Article dans une revue scientifique
DOI :
10.1016/j.compmedimag.2016.03.003
Title :
Stacking denoising auto-encoders in a deep network to segment the brainstem on MRI in brain cancer patients: a clinical study
Author(s) :
Dolz, Jose [Auteur]
Thérapies Laser Assistées par l'Image pour l'Oncologie - U 1189 [ONCO-THAI]
Betrouni, Nacim [Auteur] refId
Thérapies Laser Assistées par l'Image pour l'Oncologie - U 1189 [ONCO-THAI]
Quidet, Mathilde [Auteur]
Thérapies Laser Assistées par l'Image pour l'Oncologie - U 1189 [ONCO-THAI]
Kharroubi, Dris [Auteur]
Thérapies Laser Assistées par l'Image pour l'Oncologie - U 1189 [ONCO-THAI]
Leroy, Henri A. [Auteur]
Centre Hospitalier Régional Universitaire [Lille] [CHRU Lille]
Thérapies Laser Assistées par l'Image pour l'Oncologie - U 1189 [ONCO-THAI]
Reyns, Nicolas [Auteur]
Centre Hospitalier Régional Universitaire [Lille] [CHRU Lille]
Thérapies Laser Assistées par l'Image pour l'Oncologie - U 1189 [ONCO-THAI]
Massoptier, Laurent [Auteur]
Vermandel, Maximilien [Auteur]
Centre Hospitalier Régional Universitaire [Lille] [CHRU Lille]
Thérapies Laser Assistées par l'Image pour l'Oncologie - U 1189 [ONCO-THAI]
Journal title :
Computerized Medical Imaging and Graphics
Publisher :
Elsevier
Publication date :
2016
ISSN :
0895-6111
English keyword(s) :
deep learning
MRI segmentation
brain cancer
machine learning
HAL domain(s) :
Sciences du Vivant [q-bio]/Ingénierie biomédicale/Imagerie
English abstract : [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 ...
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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.Show less >
Language :
Anglais
Peer reviewed article :
Oui
Audience :
Internationale
Popular science :
Non
Collections :
  • Thérapies Lasers Assistées par l'Image pour l'Oncologie (ONCO-THAI) - U1189
Source :
Harvested from HAL
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