Name
Abstract | ||
|---|---|---|
The most common primary brain tumors are gliomas, evolving from the cerebral
supportive cells. For clinical follow-up, the evaluation of the preoperative
tumor volume is essential. Volumetric assessment of tumor volume with manual
segmentation of its outlines is a time-consuming process that can be overcome
with the help of computerized segmentation methods. In this contribution, two
methods for World Health Organization (WHO) grade IV glioma segmentation in the
human brain are compared using magnetic resonance imaging (MRI) patient data
from the clinical routine. One method uses balloon inflation forces, and relies
on detection of high intensity tumor boundaries that are coupled with the use
of contrast agent gadolinium. The other method sets up a directed and weighted
graph and performs a min-cut for optimal segmentation results. The ground truth
of the tumor boundaries - for evaluating the methods on 27 cases - is manually
extracted by neurosurgeons with several years of experience in the resection of
gliomas. A comparison is performed using the Dice Similarity Coefficient (DSC),
a measure for the spatial overlap of different segmentation results. |
Year | Venue | Keywords |
|---|---|---|
2011 | Clinical Orthopaedics and Related Research | pattern recognition,ground truth,magnetic resonance image |
Field | DocType | Volume |
Graph,Pattern recognition,Segmentation,Computer science,Glioma,Resection,Human brain,Tumor segmentation,Ground truth,Artificial intelligence,Magnetic resonance imaging | Journal | abs/1102.2 |
Citations | PageRank | References |
4 | 0.43 | 0 |
Authors | ||
8 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Jan Egger | 1 | 427 | 43.52 |
| Dzenan Zukic | 2 | 43 | 5.47 |
| Miriam Helen Anna Bauer | 3 | 45 | 4.71 |
| Daniela Kuhnt | 4 | 70 | 6.58 |
| Barbara Carl | 5 | 52 | 5.33 |
| Bernd Freisleben | 6 | 1456 | 142.68 |
| Andreas Kolb | 7 | 783 | 71.76 |
| Christopher Nimsky | 8 | 466 | 42.20 |