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

Ardon, Roberto; Cohen, Laurent D.; Cuingnet, Rémi; Lesage, David; Mory, Benoît; Prevost, Raphaël (2012), Automatic Detection and Segmentation of Kidneys in 3D CT Images Using Random Forests, in Nicholas Ayache, Hervé Delingette, Polina Golland, Kensaku Mori, Medical Image Computing and Computer-Assisted Intervention – MICCAI 2012 15th International Conference, Nice, France, October 1-5, 2012, Proceedings, Part III, Springer : Berlin Heidelberg, p. 66-74. 10.1007/978-3-642-33454-2_9

Type
Communication / Conférence
Date
2012
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 III; MICCAI 2012
Book author
Nicholas Ayache, Hervé Delingette, Polina Golland, Kensaku Mori
Publisher
Springer
Published in
Berlin Heidelberg
ISBN
978-3-642-33453-5
Pages
66-74
Publication identifier
10.1007/978-3-642-33454-2_9
Metadata
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Author(s)
Ardon, Roberto

Cohen, Laurent D.

Cuingnet, Rémi

Lesage, David

Mory, Benoît

Prevost, Raphaël
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.
Subjects / Keywords
3D images segmentation

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