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dc.contributor.authorLucidarme, Olivier
HAL ID: 176330
ORCID: 0000-0003-0893-3764
dc.contributor.authorRouet, Laurence
dc.contributor.authorMory, Benoît
dc.contributor.authorCuingnet, Rémi
dc.contributor.authorRomain, Blandine
dc.contributor.authorPrevost, Raphaël
dc.subjectHealth Informaticsen
dc.subjectImaging / Radiologyen
dc.subjectArtificial Intelligence (incl. Robotics)en
dc.subjectComputer Graphicsen
dc.subjectPattern Recognitionen
dc.subjectImage Processing and Computer Visionen
dc.titleRegistration of Free-Breathing 3D+t Abdominal Perfusion CT Images via Co-segmentationen
dc.typeCommunication / Conférence
dc.contributor.editoruniversityotherService de radiologie Assistance publique - Hôpitaux de Paris (AP-HP) – Hôpital Pitié-Salpêtrière – Université Pierre et Marie Curie (UPMC) - Paris VI;France
dc.contributor.editoruniversityotherMathématiques Appliquées aux Systèmes - EA 4037 (MAS) Ecole Centrale Paris;France
dc.contributor.editoruniversityotherInformatique, Biologie Intégrative et Systèmes Complexes (IBISC) Université d'Evry-Val d'Essonne : EA4526;France
dc.description.abstractenDynamic contrast-enhanced computed tomography (DCE-CT) is a valuable imaging modality to assess tissues properties, particularly in tumours, by estimating pharmacokinetic parameters from the evolution of pixels intensities in 3D+t acquisitions. However, this requires a registration of the whole sequence of volumes, which is challenging especially when the patient breathes freely. In this paper, we propose a generic, fast and automatic method to address this problem. As standard iconic registration methods are not robust to contrast intake, we rather rely on the segmentation of the organ of interest. This segmentation is performed jointly with the registration of the sequence within a novel co-segmentation framework. Our approach is based on implicit template deformation, that we extend to a co-segmentation algorithm which provides as outputs both a segmentation of the organ of interest in every image and stabilising transformations for the whole sequence. The proposed method is validated on 15 datasets acquired from patients with renal lesions and shows improvement in terms of registration and estimation of pharmacokinetic parameters over the state-of-the-art method.en
dc.relation.ispartofseriestitleLecture Notes in Computer Scienceen
dc.relation.ispartoftitleMedical Image Computing and Computer-Assisted Intervention – MICCAI 2013en
dc.relation.ispartofeditorNavab, Nassir
dc.relation.ispartofeditorBarillot, Christian
dc.relation.ispartofeditorSato, Yoshinobu
dc.relation.ispartofeditorSakuma, Ichiro
dc.relation.ispartofeditorMori, Kensaku
dc.subject.ddclabelIntelligence artificielleen
dc.relation.ispartofisbnPrint ISBN 978-3-642-40762-8 Online ISBN 978-3-642-40763-5en
dc.relation.conftitleMICCAI 2013en

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