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 MassSpring 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. © Springer-Verlag Berlin Heidelberg 2006.
CITATION STYLE
Dornheim, J., Seim, H., Preim, B., Hertel, I., & Strauss, G. (2006). Segmentation of neck lymph nodes in CT datasets with stable 3D mass-spring models. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 4191 LNCS-II, pp. 904–911). Springer Verlag. https://doi.org/10.1007/11866763_111
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