Name
Abstract | ||
|---|---|---|
We propose a fast, automatic and versatile framework for the segmentation of multiple anatomical structures from 2D and 3D images. We extend the work of [1] on implicit template deformation to multiple targets. Our variational formulation optimizes the non-rigid transformation of a set of templates according to image-driven forces. It embeds non-overlapping constraints ensuring a consistent segmentation result. We demonstrate the potential of our approach on the segmentation of abdominal organs (liver, kidneys, spleen and gallbladder) with an evaluation on CT volumes (50 for training and 50 for testing). Our method reaches state-of-the-art accuracy, ranging from 2mm (liver and kidneys) to 8mm (gallbladder). |
Year | DOI | Venue |
|---|---|---|
2015 | 10.1109/ISBI.2015.7163887 | 2015 IEEE 12th International Symposium on Biomedical Imaging (ISBI) |
Keywords | Field | DocType |
Multi-organ segmentation,template deformation,automatic segmentation | Computer vision,Scale-space segmentation,Pattern recognition,Segmentation,Computer science,Image segmentation,Artificial intelligence,Computed tomography,Template,Anatomical structures,Deformation (mechanics) | Conference |
ISSN | Citations | PageRank |
1945-7928 | 3 | 0.39 |
References | Authors | |
9 | 4 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Romane Gauriau | 1 | 31 | 2.47 |
| Roberto Ardori | 2 | 3 | 0.39 |
| David Lesage | 3 | 441 | 18.16 |
| Isabelle Bloch | 4 | 2123 | 170.75 |