title: Stacking denoising auto-encoders in a deep network to segment the brainstem on MRI in brain cancer patients: A clinical study.
abstract: 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.
authors.affiliation: AQUILAB, Loos-les-Lille, France; Univ. Lille, Inserm, CHU Lille, U1189 - ONCO-THAI - Image Assisted Laser Therapy for Oncology, F-59000 Lille, France. Electronic address: jose.dolz.upv@gmail.com.
authors.forename: Jose
authors.initials: J
authors.lastname: Dolz
authors.affiliation: Univ. Lille, Inserm, CHU Lille, U1189 - ONCO-THAI - Image Assisted Laser Therapy for Oncology, F-59000 Lille, France.
authors.forename: Nacim
authors.initials: N
authors.lastname: Betrouni
authors.affiliation: Univ. Lille, Inserm, CHU Lille, U1189 - ONCO-THAI - Image Assisted Laser Therapy for Oncology, F-59000 Lille, France.
authors.forename: Mathilde
authors.initials: M
authors.lastname: Quidet
authors.affiliation: Univ. Lille, Inserm, CHU Lille, U1189 - ONCO-THAI - Image Assisted Laser Therapy for Oncology, F-59000 Lille, France.
authors.forename: Dris
authors.initials: D
authors.lastname: Kharroubi
authors.affiliation: Univ. Lille, Inserm, CHU Lille, U1189 - ONCO-THAI - Image Assisted Laser Therapy for Oncology, F-59000 Lille, France; Neurosurgery Department, University Hospital Lille, Lille, France.
authors.forename: Henri A
authors.initials: HA
authors.lastname: Leroy
authors.affiliation: Univ. Lille, Inserm, CHU Lille, U1189 - ONCO-THAI - Image Assisted Laser Therapy for Oncology, F-59000 Lille, France; Neurosurgery Department, University Hospital Lille, Lille, France.
authors.forename: Nicolas
authors.initials: N
authors.lastname: Reyns
authors.affiliation: AQUILAB, Loos-les-Lille, France.
authors.forename: Laurent
authors.initials: L
authors.lastname: Massoptier
authors.affiliation: Univ. Lille, Inserm, CHU Lille, U1189 - ONCO-THAI - Image Assisted Laser Therapy for Oncology, F-59000 Lille, France; Neurosurgery Department, University Hospital Lille, Lille, France.
authors.forename: Maximilien
authors.initials: M
authors.lastname: Vermandel
mesh_terms: D001932:Brain Neoplasms / Q000000981:diagnostic imaging*; D001933:Brain Stem / Q000000981:diagnostic imaging*; D006801:Humans; D000069550:Machine Learning; D008279:Magnetic Resonance Imaging / Q000379:methods*
keywords: Brain cancer; Deep learning; MRI segmentation; Machine learning