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Automatic Detection and Segmentation of Kidneys in 3D CT Images Using Random Forests

dc.contributor.authorArdon, Roberto*
dc.contributor.authorCohen, Laurent D.*
dc.contributor.authorCuingnet, Rémi*
dc.contributor.authorLesage, David*
dc.contributor.authorMory, Benoît*
dc.contributor.authorPrevost, Raphaël*
dc.date.accessioned2013-01-26T10:35:20Z
dc.date.available2013-01-26T10:35:20Z
dc.date.issued2012
dc.identifier.urihttps://basepub.dauphine.fr/handle/123456789/10889
dc.descriptionLNCS n°7512
dc.language.isoenen
dc.subject3D images segmentation
dc.subject.ddc621.3en
dc.titleAutomatic Detection and Segmentation of Kidneys in 3D CT Images Using Random Forests
dc.typeCommunication / Conférence
dc.description.abstractenKidney 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.citationpages66-74
dc.relation.ispartoftitleMedical Image Computing and Computer-Assisted Intervention – MICCAI 2012 15th International Conference, Nice, France, October 1-5, 2012, Proceedings, Part III
dc.relation.ispartoftitleMICCAI 2012
dc.relation.ispartofeditorNicholas Ayache, Hervé Delingette, Polina Golland, Kensaku Mori
dc.relation.ispartofpublnameSpringer
dc.relation.ispartofpublcityBerlin Heidelberg
dc.relation.ispartofdate2012
dc.relation.ispartofurl10.1007/978-3-642-33454-2
dc.subject.ddclabelTraitement du signalen
dc.relation.ispartofisbn978-3-642-33453-5
dc.relation.confcountryFRANCE
dc.relation.forthcomingnonen
dc.relation.forthcomingprintnonen
dc.identifier.doi10.1007/978-3-642-33454-2_9
dc.description.ssrncandidatenon
dc.description.halcandidateoui
dc.description.readershiprecherche
dc.description.audienceInternational
dc.date.updated2017-03-10T16:41:39Z
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