Name
Title | ||
|---|---|---|
Fast kidney detection and segmentation with learned kernel convolution and model deformation in 3D ultrasound images |
Abstract | ||
|---|---|---|
We present a method to segment kidneys in 3D ultrasound images. The main challenges are the high variability in kidney appearance, the frequent presence of artifacts (shadows, speckle noise, etc.) and a strong constraint on computation time for clinical acceptance (less than 10 seconds). Our algorithm leverages a database of 480 3D images through a support vector machine(SVM)-based detection algorithm followed by a model-based deformation technique. Since severe pathologies induce strong deformations of kidneys, the proposed method encompasses intuitive interaction functions allowing the user to refine the result with a few clicks. Validation has been performed by learning on 120 cases and testing on 360; a perfect segmentation was reached automatically in 50% of the cases, and in 90% of the cases in less than 3 clicks. |
Year | DOI | Venue |
|---|---|---|
2015 | 10.1109/ISBI.2015.7163865 | IEEE International Symposium on Biomedical Imaging |
Keywords | Field | DocType |
Kidney, Detection, Segmentation, 3D Ultrasound, Support Vector Machine, Template Matching, Template Deformation | Kernel (linear algebra),Computer vision,Scale-space segmentation,Pattern recognition,Convolution,Computer science,Segmentation,Support vector machine,Image segmentation,Artificial intelligence,Speckle noise,3D ultrasound | Conference |
ISSN | Citations | PageRank |
1945-7928 | 4 | 0.39 |
References | Authors | |
3 | 4 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Roberto Ardon | 1 | 162 | 11.06 |
| Rémi Cuingnet | 2 | 415 | 19.36 |
| ketan bacchuwar | 3 | 9 | 1.84 |
| vincent auvray | 4 | 5 | 0.74 |