Abstract
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.
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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: Larsen, R., Nielsen, M., Sporring, J. (eds) Medical Image Computing and Computer-Assisted Intervention – MICCAI 2006. MICCAI 2006. Lecture Notes in Computer Science, vol 4191. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11866763_111
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DOI: https://doi.org/10.1007/11866763_111
Publisher Name: Springer, Berlin, Heidelberg
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