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

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Date
2012
Notes
LNCS n°7512
Dewey
Traitement du signal
Sujet
3D images segmentation
DOI
http://dx.doi.org/10.1007/978-3-642-33454-2_9
Conference country
FRANCE
Book title
Medical Image Computing and Computer-Assisted Intervention – MICCAI 2012 15th International Conference, Nice, France, October 1-5, 2012, Proceedings, Part IIIMICCAI 2012
Author
Nicholas Ayache, Hervé Delingette, Polina Golland, Kensaku Mori
Publisher
Springer
Publisher city
Berlin Heidelberg
Year
2012
ISBN
978-3-642-33453-5
Book URL
10.1007/978-3-642-33454-2
URI
https://basepub.dauphine.fr/handle/123456789/10889
Collections
  • CEREMADE : Publications
Metadata
Show full item record
Author
Ardon, Roberto
Cohen, Laurent D.
Cuingnet, Rémi
Lesage, David
Mory, Benoît
Prevost, Raphaël
Type
Communication / Conférence
Item number of pages
66-74
Abstract (EN)
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.

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