title: Automatic detection and segmentation of lymph nodes from CT data.
abstract: Lymph nodes are assessed routinely in clinical practice and their size is followed throughout radiation or chemotherapy to monitor the effectiveness of cancer treatment. This paper presents a robust learning-based method for automatic detection and segmentation of solid lymph nodes from CT data, with the following contributions. First, it presents a learning based approach to solid lymph node detection that relies on marginal space learning to achieve great speedup with virtually no loss in accuracy. Second, it presents a computationally efficient segmentation method for solid lymph nodes (LN). Third, it introduces two new sets of features that are effective for LN detection, one that self-aligns to high gradients and another set obtained from the segmentation result. The method is evaluated for axillary LN detection on 131 volumes containing 371 LN, yielding a 83.0% detection rate with 1.0 false positive per volume. It is further evaluated for pelvic and abdominal LN detection on 54 volumes containing 569 LN, yielding a 80.0% detection rate with 3.2 false positives per volume. The running time is 5-20 s per volume for axillary areas and 15-40 s for pelvic. An added benefit of the method is the capability to detect and segment conglomerated lymph nodes.
authors.affiliation: Department of Statistics, Florida State University, Tallahassee, FL 32306 USA. abarbu@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; D006801:Humans; D008198:Lymph Nodes / Q000000981:diagnostic imaging*; D008223:Lymphoma / 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*