Name
Title | ||
|---|---|---|
An iterative Bayesian approach for nearly automatic liver segmentation: algorithm and validation |
Abstract | ||
|---|---|---|
Purpose We present a new algorithm for nearly automatic liver segmentation and volume estimation from abdominal Computed Tomography
Angiography (CTA) images and its validation.
Materials and methods Our hybrid algorithm uses a multiresolution iterative scheme. It starts from a single user-defined pixel seed inside the liver,
and repeatedly applies smoothed Bayesian classification to identify the liver and other organs, followed by adaptive morphological
operations and active contours refinement. We evaluate the algorithm with two retrospective studies on 56 validated CTA images.
The first study compares it to ground-truth manual segmentation and semi-automatic and automatic commercial methods. The second
study uses the public data-set SLIVER07 and its comparison methodology.
Results We achieved for both studies, correlations of 0.98 and 0.99 for liver volume estimation, with mean volume differences of 5.36
and 2.68% with respect to manual ground-truth estimation, and mean volume variability for different initial seeds of 0.54
and 0.004%, respectively. For the second study, our algorithm scored 71.8 and 67.87 for the training and test datasets, which
compares very favorably with other semi-automatic methods.
Conclusions Our algorithm requires minimal interaction by a non-expert user, is accurate, efficient, and robust to initial seed selection.
It can be effective for hepatic volume estimation and liver modeling in a clinical setup. |
Year | DOI | Venue |
|---|---|---|
2008 | 10.1007/s11548-008-0254-1 | Int. J. Computer Assisted Radiology and Surgery |
Keywords | Field | DocType |
computed tomography · segmentation of abdominal organs · computer-assisted diagnosis,hybrid algorithm,ground truth,computed tomography,retrospective study,bayesian approach,bayesian classification,active contour | Computer vision,Computed tomography angiography,Segmentation,Computer science,Algorithm,Volume estimation,Artificial intelligence,Computed tomography,Radiology,Bayesian probability | Journal |
Volume | Issue | ISSN |
3 | 5 | 1861-6429 |
Citations | PageRank | References |
14 | 0.81 | 11 |
Authors | ||
6 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| M. Freiman | 1 | 14 | 1.15 |
| O Eliassaf | 2 | 27 | 1.64 |
| Y. Taieb | 3 | 14 | 0.81 |
| L Joskowicz | 4 | 107 | 11.24 |
| Y. Azraq | 5 | 14 | 0.81 |
| J Sosna | 6 | 59 | 3.51 |