Name
Abstract | ||
|---|---|---|
Zonal segmentation of the prostate into the central gland and peripheral zone is a useful tool in computer-aided detection of prostate cancer, because occurrence and characteristics of cancer in both zones differ substantially. In this paper we present a pattern recognition approach to segment the prostate zones. It incorporates three types of features that can differentiate between the two zones: anatomical, intensity and texture. It is evaluated against a multi-parametric multi-atlas based method using 48 multi-parametric MRI studies. Three observers are used to assess inter-observer variability and we compare our results against the state of the art from literature. Results show a mean Dice coefficient of 0.89 +/- 0.03 for the central gland and 0.75 +/- 0.07 for the peripheral zone, compared to 0.87 +/- 0.04 and 0.76 +/- 0.06 in literature. Summarizing, a pattern recognition approach incorporating anatomy, intensity and texture has been shown to give good results in zonal segmentation of the prostate. |
Year | DOI | Venue |
|---|---|---|
2012 | 10.1007/978-3-642-33418-4_51 | MICCAI (2) |
Keywords | Field | DocType |
computer-aided detection,zonal segmentation,inter-observer variability,central gland,prostate cancer,good result,peripheral zone,prostate zone,multi-parametric mri study,pattern recognition approach | Computer vision,Pattern recognition,Computer science,Sørensen–Dice coefficient,Segmentation,Artificial intelligence,Prostate,Prostate cancer,Cancer,Peripheral zone | Conference |
Volume | Issue | ISSN |
15 | Pt 2 | 0302-9743 |
Citations | PageRank | References |
9 | 1.01 | 6 |
Authors | ||
5 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Geert Litjens | 1 | 996 | 50.79 |
| Oscar Debats | 2 | 10 | 1.71 |
| Wendy van de Ven | 3 | 9 | 1.01 |
| Nico Karssemeijer | 4 | 992 | 122.49 |
| Henkjan Huisman | 5 | 36 | 6.15 |