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
Volume2015-July
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

Other

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

Fingerprint

Lymph Nodes
Ultrasonics
Oils and fats
Splines
Phosphates
Acoustics
Pixels
Fats

Keywords

  • graph cuts
  • Lymph node
  • segmentation
  • ultrasound

ASJC Scopus subject areas

  • Biomedical Engineering
  • Radiology Nuclear Medicine and imaging

Cite this

Kuo, J. W., Mamou, J., Wang, Y., Saegusa-Beecroft, E., Machi, J., & Feleppa, E. J. (2015). A novel nested graph cuts method for segmenting human lymph nodes in 3D high frequency ultrasound images. In 2015 IEEE 12th International Symposium on Biomedical Imaging, ISBI 2015 (Vol. 2015-July, pp. 372-375). [7163890] IEEE Computer Society. https://doi.org/10.1109/ISBI.2015.7163890

A novel nested graph cuts method for segmenting human lymph nodes in 3D high frequency ultrasound images. / Kuo, Jen Wei; Mamou, Jonathan; Wang, Yao; Saegusa-Beecroft, Emi; Machi, Junji; Feleppa, Ernest J.

2015 IEEE 12th International Symposium on Biomedical Imaging, ISBI 2015. Vol. 2015-July IEEE Computer Society, 2015. p. 372-375 7163890.

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

Kuo, JW, Mamou, J, Wang, Y, Saegusa-Beecroft, E, Machi, J & Feleppa, EJ 2015, A novel nested graph cuts method for segmenting human lymph nodes in 3D high frequency ultrasound images. in 2015 IEEE 12th International Symposium on Biomedical Imaging, ISBI 2015. vol. 2015-July, 7163890, IEEE Computer Society, pp. 372-375, 12th IEEE International Symposium on Biomedical Imaging, ISBI 2015, Brooklyn, United States, 4/16/15. https://doi.org/10.1109/ISBI.2015.7163890
Kuo JW, Mamou J, Wang Y, Saegusa-Beecroft E, Machi J, Feleppa EJ. A novel nested graph cuts method for segmenting human lymph nodes in 3D high frequency ultrasound images. In 2015 IEEE 12th International Symposium on Biomedical Imaging, ISBI 2015. Vol. 2015-July. IEEE Computer Society. 2015. p. 372-375. 7163890 https://doi.org/10.1109/ISBI.2015.7163890
Kuo, Jen Wei ; Mamou, Jonathan ; Wang, Yao ; Saegusa-Beecroft, Emi ; Machi, Junji ; Feleppa, Ernest J. / A novel nested graph cuts method for segmenting human lymph nodes in 3D high frequency ultrasound images. 2015 IEEE 12th International Symposium on Biomedical Imaging, ISBI 2015. Vol. 2015-July IEEE Computer Society, 2015. pp. 372-375
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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.",
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