Segmentation of neck lymph nodes in CT datasets with stable 3D mass-spring models.
The quantitative assessment of neck lymph nodes in the context of malign tumors requires an efficient segmentation technique for lymph nodes in tomographic 3D datasets. We present a Stable 3D Mass-Spring Model for lymph node segmentation in CT datasets. Our model for the first time represents concurrently the characteristic gray value range, directed contour information as well as shape knowledge, which leads to a much more robust and efficient segmentation process. Our model design and segmentation accuracy are both evaluated with lymph nodes from clinical CT neck datasets.
Dornheim
Jana
J
Otto-von-Guericke-Universität Magdeburg, Germany.
Seim
Heiko
H
Preim
Bernhard
B
Hertel
Ilka
I
Strauss
Gero
G
D000465:Algorithms; D001185:Artificial Intelligence; D003198:Computer Simulation; D004548:Elasticity; D006801:Humans; D021621:Imaging, Three-Dimensional / Q000379:methods*; D008198:Lymph Nodes / Q000000981:diagnostic imaging* / Q000502:physiology*; D008954:Models, Biological; D009333:Neck; 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*; D014783:Viscosity