Name
Abstract | ||
|---|---|---|
Lymph node detection and measurement is a difficult and important part of cancer treatment. In this paper we present a robust and effective learning-based method for the automatic detection of solid lymph nodes from Computed Tomography data. The contributions of the paper are the following. First, it presents a learning based approach to lymph node detection based on Marginal Space Learning. Second, it presents an efficient MRF-based segmentation method for solid lymph nodes. Third, it presents two new sets of features, one set self-aligning to the local gradients and another set based on the segmentation result. An extensive evaluation on 101 volumes containing 362 lymph nodes shows that this method obtains a 82.3% detection rate at 1 false positive per volume, with an average running time of 5-20 seconds per volume. |
Year | DOI | Venue |
|---|---|---|
2010 | 10.1007/978-3-642-15705-9_4 | MICCAI |
Keywords | Field | DocType |
new set,axillary lymph node,lymph node detection,segmentation result,lymph nodes shows,solid lymph node,efficient mrf-based segmentation method,automatic detection,node detection,detection rate,effective learning-based method,computed tomography,false positive | Lymph,Computer vision,Axillary region,Pattern recognition,Segmentation,Computer science,Computed tomography,Artificial intelligence,Axillary lymph nodes,Lymph node,Marginal space learning | Conference |
Volume | Issue | ISSN |
13 | Pt 1 | 0302-9743 |
ISBN | Citations | PageRank |
3-642-15704-1 | 9 | 0.66 |
References | Authors | |
11 | 6 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Adrian Barbu | 1 | 768 | 58.59 |
| Michael Suehling | 2 | 50 | 4.52 |
| Xun Xu | 3 | 28 | 2.27 |
| David Liu | 4 | 87 | 7.58 |
| Shaohua Kevin Zhou | 5 | 1392 | 88.97 |
| Dorin Comaniciu | 6 | 8389 | 601.83 |