A novel nested graph cuts method for segmenting human lymph nodes in 3D high frequency ultrasound images

Jen Wei Kuo, Jonathan Mamou, Yao Wang, Emi Saegusa-Beecroft, Junji Machi, Ernest J. Feleppa

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

Three-dimensional (3D) quantitative-ultrasound (QUS) methods were recently developed and successfully applied to detect cancerous regions in freshly-dissected lymph nodes (LNs). The 3D high frequency ultrasound (HFU) images obtained from these LNs contain three different parts: LN-parenchyma (LNP), fat, and phosphate-buffered saline (PBS). To apply QUS estimates inside the LNP region, an automatic and accurate algorithm for LNP segmentation is needed. In this paper, we describe a novel, nested-graph-cut (NGC) method that effectively exploits the nested structure of the LN images. To overcome the large variability of the intensity distribution of LNP pixels due to acoustic attenuation and focusing, we further describe an iterative self-updating framework combining NGC and spline-based robust intensity fitting. Dice similarity coefficients of 89.56±8.44% were achieved when the proposed automatic segmentation algorithm was compared to expert manual segmentation on a dataset consisting of 115 LNs.

Original languageEnglish (US)
Title of host publication2015 IEEE 12th International Symposium on Biomedical Imaging, ISBI 2015
PublisherIEEE Computer Society
Pages372-375
Number of pages4
ISBN (Electronic)9781479923748
DOIs
StatePublished - Jul 21 2015
Event12th IEEE International Symposium on Biomedical Imaging, ISBI 2015 - Brooklyn, United States
Duration: Apr 16 2015Apr 19 2015

Publication series

NameProceedings - International Symposium on Biomedical Imaging
Volume2015-July
ISSN (Print)1945-7928
ISSN (Electronic)1945-8452

Other

Other12th IEEE International Symposium on Biomedical Imaging, ISBI 2015
CountryUnited States
CityBrooklyn
Period4/16/154/19/15

Keywords

  • Lymph node
  • graph cuts
  • segmentation
  • ultrasound

ASJC Scopus subject areas

  • Biomedical Engineering
  • Radiology Nuclear Medicine and imaging

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