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Proceedings Paper

Thoracic lymph node station recognition on CT images based on automatic anatomy recognition with an optimal parent strategy
Author(s): Guoping Xu; Jayaram K. Udupa; Yubing Tong; Hanqiang Cao; Dewey Odhner; Drew A. Torigian; Xingyu Wu
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Paper Abstract

Currently, there are many papers that have been published on the detection and segmentation of lymph nodes from medical images. However, it is still a challenging problem owing to low contrast with surrounding soft tissues and the variations of lymph node size and shape on computed tomography (CT) images. This is particularly very difficult on low-dose CT of PET/CT acquisitions. In this study, we utilize our previous automatic anatomy recognition (AAR) framework to recognize the thoracic-lymph node stations defined by the International Association for the Study of Lung Cancer (IASLC) lymph node map. The lymph node stations themselves are viewed as anatomic objects and are localized by using a one-shot method in the AAR framework. Two strategies have been taken in this paper for integration into AAR framework. The first is to combine some lymph node stations into composite lymph node stations according to their geometrical nearness. The other is to find the optimal parent (organ or union of organs) as an anchor for each lymph node station based on the recognition error and thereby find an overall optimal hierarchy to arrange anchor organs and lymph node stations. Based on 28 contrast-enhanced thoracic CT image data sets for model building, 12 independent data sets for testing, our results show that thoracic lymph node stations can be localized within 2-3 voxels compared to the ground truth.

Paper Details

Date Published: 2 March 2018
PDF: 7 pages
Proc. SPIE 10574, Medical Imaging 2018: Image Processing, 105742F (2 March 2018); doi: 10.1117/12.2293258
Show Author Affiliations
Guoping Xu, Univ. of Pennsylvania (United States)
Huazhong Univ. of Science and Technology (China)
Jayaram K. Udupa, Univ. of Pennsylvania (United States)
Yubing Tong, Univ. of Pennsylvania (United States)
Hanqiang Cao, Huazhong Univ. of Science and Technology (China)
Dewey Odhner, Univ. of Pennsylvania (United States)
Drew A. Torigian, Univ. of Pennsylvania (United States)
Xingyu Wu, Univ. of Pennsylvania (United States)


Published in SPIE Proceedings Vol. 10574:
Medical Imaging 2018: Image Processing
Elsa D. Angelini; Bennett A. Landman, Editor(s)

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