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Proceedings Paper

Novel method and applications for labeling and identifying lymph nodes
Author(s): Atilla P. Kiraly; David P. Naidich; Lutz Guendel; Li Zhang; Carol L. Novak
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Paper Abstract

The lymphatic system comprises a series of interconnected lymph nodes that are commonly distributed along branching or linearly oriented anatomic structures. Physicians must evaluate lymph nodes when staging cancer and planning optimal paths for nodal biopsy. This process requires accurately determining the lymph node's position with respect to major anatomical landmarks. In an effort to standardize lung cancer staging, The American Joint Committee on Cancer (AJCC) has classified lymph nodes within the chest into 4 groups and 14 sub groups. We present a method for automatically labeling lymph nodes according to this classification scheme, in order to improve the speed and accuracy of staging and biopsy planning. Lymph nodes within the chest are clustered around the major blood vessels and the airways. Our fully automatic labeling method determines the nodal group and sub-group in chest CT data by use of computed airway and aorta centerlines to produce features relative to a given node location. A classifier then determines the label based upon these features. We evaluate the efficacy of the method on 10 chest CT datasets containing 86 labeled lymph nodes. The results are promising with 100% of the nodes assigned to the correct group and 76% to the correct sub-group. We anticipate that additional features and training data will further improve the results. In addition to labeling, other applications include automated lymph node localization and visualization. Although we focus on chest CT data, the method can be generalized to other regions of the body as well as to different imaging modalities.

Paper Details

Date Published: 10 May 2007
PDF: 9 pages
Proc. SPIE 6511, Medical Imaging 2007: Physiology, Function, and Structure from Medical Images, 651111 (10 May 2007);
Show Author Affiliations
Atilla P. Kiraly, Siemens Corporate Research (United States)
David P. Naidich, New York Univ. Medical Ctr. (United States)
Lutz Guendel, Siemens Medical Solutions (Germany)
Li Zhang, Siemens Corporate Research (United States)
Carol L. Novak, Siemens Corporate Research (United States)

Published in SPIE Proceedings Vol. 6511:
Medical Imaging 2007: Physiology, Function, and Structure from Medical Images
Armando Manduca; Xiaoping P. Hu, Editor(s)

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