Snake model-based lymphoma segmentation for sequential CT images

Comput Methods Programs Biomed. 2013 Aug;111(2):366-75. doi: 10.1016/j.cmpb.2013.05.019. Epub 2013 Jun 17.


The measurement of the size of lesions in follow-up CT examinations of cancer patients is important to evaluate the success of treatment. This paper presents an automatic algorithm for identifying and segmenting lymph nodes in CT images across longitudinal time points. Firstly, a two-step image registration method is proposed to locate the lymph nodes including coarse registration based on body region detection and fine registration based on a double-template matching algorithm. Then, to make the initial segmentation approximate the boundaries of lymph nodes, the initial image registration result is refined with intensity and edge information. Finally, a snake model is used to evolve the refined initial curve and obtain segmentation results. Our algorithm was tested on 26 lymph nodes at multiple time points from 14 patients. The image at the earlier time point was used as the baseline image to be used in evaluating the follow-up image, resulting in 76 total test cases. Of the 76 test cases, we made a 76 (100%) successful detection and 38/40 (95%) correct clinical assessment according to Response Evaluation Criteria in Solid Tumors (RECIST). The quantitative evaluation based on several metrics, such as average Hausdorff distance, indicates that our algorithm is produces good results. In addition, the proposed algorithm is fast with an average computing time 2.58s. The proposed segmentation algorithm for lymph nodes is fast and can achieve high segmentation accuracy, which may be useful to automate the tracking and evaluation of cancer therapy.

Keywords: Image registration; Image segmentation; Lymphoma; Snake model; Template matching.

Publication types

  • Research Support, N.I.H., Extramural
  • Research Support, Non-U.S. Gov't

MeSH terms

  • Algorithms
  • Diagnosis, Computer-Assisted
  • Humans
  • Image Processing, Computer-Assisted / methods
  • Lymph Nodes / diagnostic imaging
  • Lymph Nodes / pathology
  • Lymphoma / diagnosis*
  • Lymphoma / diagnostic imaging*
  • Models, Statistical
  • Pattern Recognition, Automated / methods
  • Radiographic Image Interpretation, Computer-Assisted / methods*
  • Subtraction Technique
  • Time Factors
  • Tomography, X-Ray Computed / methods*