title: Segmentation of neck lymph nodes in CT datasets with stable 3D mass-spring models segmentation of neck lymph nodes.
abstract: OBJECTIVE
  The quantitative assessment of neck lymph nodes in the context of malignant tumors requires an efficient segmentation technique for lymph nodes in tomographic three-dimensional (3D) datasets. We present a stable 3D mass-spring model for lymph node segmentation in computed tomography (CT) datasets.
  
  
  METHODS
  For the first time our model concurrently represents the characteristic gray value range, directed contour information, and shape knowledge, which leads to a robust and efficient segmentation process.
  
  
  RESULTS
  Our model design and the segmentation accuracy were both evaluated with 40 lymph nodes from five clinical CT datasets containing malignant tumors of the neck.
  
  
  CONCLUSIONS
  The segmentation accuracy proved to be comparable to that of manual segmentations by experienced users and significantly reduced the time and interaction needed for the lymph node segmentation.
authors.affiliation: Otto-von-Guericke-Universität Magdeburg, Universitätsplatz 2, 39106 Magdeburg, Germany. jana.dornheim@isg.cs.uni-magdeburg.de
authors.forename: Jana
authors.initials: J
authors.lastname: Dornheim
authors.forename: Heiko
authors.initials: H
authors.lastname: Seim
authors.forename: Bernhard
authors.initials: B
authors.lastname: Preim
authors.forename: Ilka
authors.initials: I
authors.lastname: Hertel
authors.forename: Gero
authors.initials: G
authors.lastname: Strauss
mesh_terms: D000465:Algorithms; D001185:Artificial Intelligence; D003198:Computer Simulation; D016208:Databases, Factual; D004548:Elasticity; D006258:Head and Neck Neoplasms / Q000000981:diagnostic imaging*; D006801:Humans; D021621:Imaging, Three-Dimensional / Q000379:methods*; D008198:Lymph Nodes / Q000000981:diagnostic imaging*; D008207:Lymphatic Metastasis; D008954:Models, Biological; 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; D013314:Stress, Mechanical; D014057:Tomography, X-Ray Computed / Q000379:methods*