Name
Abstract | ||
|---|---|---|
Thyroid nodule segmentation is a hard task due to different echo structures, textures and echogenicities in ultrasound (US)
images as well as speckle noise. Currently, a typical clinical evaluation involves the manual, approximate measurement in
two section planes in order to obtain an estimate of the nodule’s size. The aforementioned nodule attributes are recorded
on paper. We propose instead the semi-automatic segmentation of 2D slices of acquired 3D US volumes with power watersheds
(PW) independent of the nodule type. We tested different input seeds to evaluate the potential of the applied algorithm. On
average we achieved a 76.81 % sensitivity, 88.95 % precision and 0.81 Dice coefficient. The runtime on a standard PC is about
0.02 s which indicates that the extension to 3D volume data should be feasible.
|
Year | DOI | Venue |
|---|---|---|
2011 | 10.1007/978-3-642-19335-4_27 | Bildverarbeitung für die Medizin |
Field | DocType | Citations |
Computer vision,Sørensen–Dice coefficient,Computer science,Segmentation,Artificial intelligence,Speckle noise,Thyroid nodules,Ultrasound image,Ultrasound | Conference | 0 |
PageRank | References | Authors |
0.34 | 3 | 5 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Eva N. K. Kollorz | 1 | 3 | 0.77 |
| Elli Angelopoulou | 2 | 375 | 34.46 |
| Michael Beck | 3 | 0 | 0.34 |
| D Schmidt | 4 | 10 | 2.77 |
| Torsten Kuwert | 5 | 27 | 6.41 |