Graph-based segmentation of lymph nodes in CT data

Authors:
Yao Wang;Reinhard Beichel
Affiliations:
Dept. of Electrical and Computer Enginering and The Iowa Institute for Biomedical Imaging, The University of Iowa, Iowa City, IA;Dept. of Electrical and Computer Enginering and Dept. of Internal Medicine and The Iowa Institute for Biomedical Imaging, The University of Iowa, Iowa City, IA
Venue:
ISVC'10 Proceedings of the 6th international conference on Advances in visual computing - Volume Part II
Year:
2010

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Abstract

The quantitative assessment of lymph node size plays an important role in treatment of diseases like cancer. In current clinical practice, lymph nodes are analyzed manually based on very rough measures of long and/or short axis length, which is error prone. In this paper we present a graph-based lymph node segmentation method to enable the computer-aided three-dimensional (3D) assessment of lymph node size. Our method has been validated on 22 cases of enlarged lymph nodes imaged with X-ray computed tomography (CT). For the signed and unsigned surface positioning error, the mean and standard deviation was 0.09±0.17 mm and 0.47±0.08 mm, respectively. On average, 5.3 seconds were required by our algorithm for the segmentation of a lymph node.