Name
Title | ||
|---|---|---|
Automatic detection and segmentation of kidneys in 3D CT images using random forests. |
Abstract | ||
|---|---|---|
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 fields of view. By combining and refining 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-fine strategy. Their initial positions detected with global contextual information are refined with a cascade of local regression forests. A classification forest is then used to obtain a probabilistic segmentation of both kidneys. The final 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 coefficient > 0.90) in a few seconds per volume. |
Year | DOI | Venue |
|---|---|---|
2012 | 10.1007/978-3-642-33454-2_9 | Lecture Notes in Computer Science |
Field | DocType | Volume |
Computer vision,Contextual information,Scale-space segmentation,Pattern recognition,Segmentation,Computer science,Sørensen–Dice coefficient,Local regression,Both kidneys,Artificial intelligence,Random forest,Probabilistic segmentation | Conference | 7512 |
Issue | ISSN | Citations |
Pt 3 | 0302-9743 | 45 |
PageRank | References | Authors |
1.61 | 15 | 6 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Rémi Cuingnet | 1 | 415 | 19.36 |
| Raphael Prevost | 2 | 92 | 7.01 |
| David Lesage | 3 | 441 | 18.16 |
| Laurent D. Cohen | 4 | 1162 | 149.39 |
| Benoit Mory | 5 | 150 | 11.08 |
| Roberto Ardon | 6 | 162 | 11.06 |