title: Automatic detection and segmentation of axillary lymph nodes.
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.
authors.affiliation: Statistics Department, Florida State Univ., Tallahassee, FL 32306, USA. abarbu@stat.fsu.edu
authors.forename: Adrian
authors.initials: A
authors.lastname: Barbu
authors.forename: Michael
authors.initials: M
authors.lastname: Suehling
authors.forename: Xun
authors.initials: X
authors.lastname: Xu
authors.forename: David
authors.initials: D
authors.lastname: Liu
authors.forename: S Kevin
authors.initials: SK
authors.lastname: Zhou
authors.forename: Dorin
authors.initials: D
authors.lastname: Comaniciu
mesh_terms: D000465:Algorithms; D001185:Artificial Intelligence; D001365:Axilla; D006801:Humans; D021621:Imaging, Three-Dimensional / Q000379:methods*; D008198:Lymph Nodes / Q000000981:diagnostic imaging*; D010363:Pattern Recognition, Automated / Q000379:methods*; D011856:Radiographic Image Enhancement / Q000379:methods; D011857:Radiographic Image Interpretation, Computer-Assisted / Q000379:methods*; D015203:Reproducibility of Results; D012680:Sensitivity and Specificity; D014057:Tomography, X-Ray Computed / Q000379:methods*