Lymph node segmentation on CT images by a shape model guided deformable surface method

Daniel Maleike; Michael Fabel; Ralf Tetzlaff; Hendrik von Tengg-Kobligk; Tobias Heimann; Hans-Peter Meinzer; Ivo Wolf

doi:10.1117/12.770352

11 March 2008 Lymph node segmentation on CT images by a shape model guided deformable surface methodh

Daniel Maleike, Michael Fabel, Ralf Tetzlaff, Hendrik von Tengg-Kobligk, Tobias Heimann, Hans-Peter Meinzer, Ivo Wolf

Proceedings Volume 6914, Medical Imaging 2008: Image Processing; 69141S (2008) https://doi.org/10.1117/12.770352
Event: Medical Imaging, 2008, San Diego, California, United States

Abstract

With many tumor entities, quantitative assessment of lymph node growth over time is important to make therapy choices or to evaluate new therapies. The clinical standard is to document diameters on transversal slices, which is not the best measure for a volume. We present a new algorithm to segment (metastatic) lymph nodes and evaluate the algorithm with 29 lymph nodes in clinical CT images. The algorithm is based on a deformable surface search, which uses statistical shape models to restrict free deformation. To model lymph nodes, we construct an ellipsoid shape model, which strives for a surface with strong gradients and user-defined gray values. The algorithm is integrated into an application, which also allows interactive correction of the segmentation results. The evaluation shows that the algorithm gives good results in the majority of cases and is comparable to time-consuming manual segmentation. The median volume error was 10.1% of the reference volume before and 6.1% after manual correction. Integrated into an application, it is possible to perform lymph node volumetry for a whole patient within the 10 to 15 minutes time limit imposed by clinical routine.

Citation Download Citation

Daniel Maleike, Michael Fabel, Ralf Tetzlaff, Hendrik von Tengg-Kobligk, Tobias Heimann, Hans-Peter Meinzer, and Ivo Wolf "Lymph node segmentation on CT images by a shape model guided deformable surface methodh", Proc. SPIE 6914, Medical Imaging 2008: Image Processing, 69141S (11 March 2008); https://doi.org/10.1117/12.770352

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