Lymph node segmentation by dynamic programming and active contours

Yongqiang Tan, Lin Lu, Apurva Bonde, Deling Wang, Jing Qi, Lawrence H. Schwartz, Binsheng Zhao

Research output: Contribution to journalArticlepeer-review

8 Scopus citations


Purpose: Enlarged lymph nodes are indicators of cancer staging, and the change in their size is a reflection of treatment response. Automatic lymph node segmentation is challenging, as the boundary can be unclear and the surrounding structures complex. This work communicates a new three-dimensional algorithm for the segmentation of enlarged lymph nodes. Methods: The algorithm requires a user to draw a region of interest (ROI) enclosing the lymph node. Rays are cast from the center of the ROI, and the intersections of the rays and the boundary of the lymph node form a triangle mesh. The intersection points are determined by dynamic programming. The triangle mesh initializes an active contour which evolves to low-energy boundary. Three radiologists independently delineated the contours of 54 lesions from 48 patients. Dice coefficient was used to evaluate the algorithm's performance. Results: The mean Dice coefficient between computer and the majority vote results was 83.2%. The mean Dice coefficients between the three radiologists’ manual segmentations were 84.6%, 86.2%, and 88.3%. Conclusions: The performance of this segmentation algorithm suggests its potential clinical value for quantifying enlarged lymph nodes.

Original languageEnglish (US)
Pages (from-to)2054-2062
Number of pages9
JournalMedical physics
Issue number5
StatePublished - May 2018
Externally publishedYes


  • active contours
  • computed tomography (CT)
  • dynamic programming
  • lymph node segmentation
  • sphere subdivision

ASJC Scopus subject areas

  • Biophysics
  • Radiology Nuclear Medicine and imaging


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