dc.contributor.author | Ardon, Roberto | * |
dc.contributor.author | Cohen, Laurent D. | * |
dc.contributor.author | Cuingnet, Rémi | * |
dc.contributor.author | Lesage, David | * |
dc.contributor.author | Mory, Benoît | * |
dc.contributor.author | Prevost, Raphaël | * |
dc.date.accessioned | 2013-01-26T10:35:20Z | |
dc.date.available | 2013-01-26T10:35:20Z | |
dc.date.issued | 2012 | |
dc.identifier.uri | https://basepub.dauphine.fr/handle/123456789/10889 | |
dc.description | LNCS n°7512 | |
dc.language.iso | en | en |
dc.subject | 3D images segmentation | |
dc.subject.ddc | 621.3 | en |
dc.title | Automatic Detection and Segmentation of Kidneys in 3D CT Images Using Random Forests | |
dc.type | Communication / Conférence | |
dc.description.abstracten | Kidney segmentation in 3D CT images allows extracting useful information for nephrologists. For practical use in clinical routine, such an algorithm should be fast, automatic and robust to contrast-agent enhancement and elds of view. By combining and re ning state-of-the-art techniques (random forests and template deformation), we demonstrate the possibility of building an algorithm that meets these requirements. Kidneys are localized with random forests following a coarse to fi ne strategy. Their initial positions detected with global contextual information are re ned with a cascade of local regression forests. A classi cation forest is then used to obtain a probabilistic segmentation of both kidneys. The nal segmentation is performed with an implicit template deformation algorithm driven by these kidney probability maps. Our method has been validated on a highly heterogeneous database of 233 CT scans from 89 patients. 80 % of the kidneys were accurately detected and segmented (Dice coe cient > 0:90) in a few seconds per volume. | |
dc.identifier.citationpages | 66-74 | |
dc.relation.ispartoftitle | Medical Image Computing and Computer-Assisted Intervention – MICCAI 2012 15th International Conference, Nice, France, October 1-5, 2012, Proceedings, Part III | |
dc.relation.ispartoftitle | MICCAI 2012 | |
dc.relation.ispartofeditor | Nicholas Ayache, Hervé Delingette, Polina Golland, Kensaku Mori | |
dc.relation.ispartofpublname | Springer | |
dc.relation.ispartofpublcity | Berlin Heidelberg | |
dc.relation.ispartofdate | 2012 | |
dc.relation.ispartofurl | 10.1007/978-3-642-33454-2 | |
dc.subject.ddclabel | Traitement du signal | en |
dc.relation.ispartofisbn | 978-3-642-33453-5 | |
dc.relation.confcountry | FRANCE | |
dc.relation.forthcoming | non | en |
dc.relation.forthcomingprint | non | en |
dc.identifier.doi | 10.1007/978-3-642-33454-2_9 | |
dc.description.ssrncandidate | non | |
dc.description.halcandidate | oui | |
dc.description.readership | recherche | |
dc.description.audience | International | |
dc.date.updated | 2017-03-10T16:41:39Z | |
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