Semi-automatic lymph node segmentation in LN-MRI

G. Unal; G. Slabaugh; A. Ess; A. Yezzi; T. Fang; J. Tyan; M. Requardt; R. Krieg; R. Seethamraju; M. Harisinghani; R. Weissleder

doi:10.1109/ICIP.2006.312366

Semi-automatic lymph node segmentation in LN-MRI

G. Unal^*
, G. Slabaugh
, A. Ess
, A. Yezzi
, T. Fang
, J. Tyan
, M. Requardt
, R. Krieg
, R. Seethamraju
, M. Harisinghani
, R. Weissleder

^*Corresponding author for this work

Siemens
Swiss Federal Institute of Technology Zurich
Georgia Institute of Technology
Harvard University

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

20 Citations (Scopus)

Abstract

Accurate staging of nodal cancer still relies on surgical exploration because many primary malignancies spread via lymphatic dissemination. The purpose of this study was to utilize nanoparticle-enhanced lymphotropic magnetic resonance imaging (LN-MRI) to explore semi-automated noninvasive nodal cancer staging. We present a joint image segmentation and registration approach, which makes use of the problem specific information to increase the robustness of the algorithm to noise and weak contrast often observed in medical imaging applications. The effectiveness of the approach is demonstrated with a given lymph node segmentation problem in post-contrast pelvic MRI sequences.

Original language	English
Title of host publication	2006 IEEE International Conference on Image Processing, ICIP 2006 - Proceedings
Pages	77-80
Number of pages	4
DOIs	https://doi.org/10.1109/ICIP.2006.312366
Publication status	Published - 2006
Externally published	Yes
Event	2006 IEEE International Conference on Image Processing, ICIP 2006 - Atlanta, GA, United States Duration: 8 Oct 2006 → 11 Oct 2006

Publication series

Name	Proceedings - International Conference on Image Processing, ICIP
ISSN (Print)	1522-4880

Conference

Conference	2006 IEEE International Conference on Image Processing, ICIP 2006
Country/Territory	United States
City	Atlanta, GA
Period	8/10/06 → 11/10/06

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

SDG 3 Good Health and Well-being

Keywords

Biomedical image processing
Biomedical magnetic resonance imaging
Image segmentation
Medical diagnosis

Access to Document

10.1109/ICIP.2006.312366

Cite this

Unal, G., Slabaugh, G., Ess, A., Yezzi, A., Fang, T., Tyan, J., Requardt, M., Krieg, R., Seethamraju, R., Harisinghani, M., & Weissleder, R. (2006). Semi-automatic lymph node segmentation in LN-MRI. In 2006 IEEE International Conference on Image Processing, ICIP 2006 - Proceedings (pp. 77-80). Article 4106470 (Proceedings - International Conference on Image Processing, ICIP). https://doi.org/10.1109/ICIP.2006.312366

@inproceedings{0591f2131a22427b86f3b8fcb17f80e0,

title = "Semi-automatic lymph node segmentation in LN-MRI",

abstract = "Accurate staging of nodal cancer still relies on surgical exploration because many primary malignancies spread via lymphatic dissemination. The purpose of this study was to utilize nanoparticle-enhanced lymphotropic magnetic resonance imaging (LN-MRI) to explore semi-automated noninvasive nodal cancer staging. We present a joint image segmentation and registration approach, which makes use of the problem specific information to increase the robustness of the algorithm to noise and weak contrast often observed in medical imaging applications. The effectiveness of the approach is demonstrated with a given lymph node segmentation problem in post-contrast pelvic MRI sequences.",

keywords = "Biomedical image processing, Biomedical magnetic resonance imaging, Image segmentation, Medical diagnosis",

author = "G. Unal and G. Slabaugh and A. Ess and A. Yezzi and T. Fang and J. Tyan and M. Requardt and R. Krieg and R. Seethamraju and M. Harisinghani and R. Weissleder",

year = "2006",

doi = "10.1109/ICIP.2006.312366",

language = "English",

isbn = "1424404819",

series = "Proceedings - International Conference on Image Processing, ICIP",

pages = "77--80",

booktitle = "2006 IEEE International Conference on Image Processing, ICIP 2006 - Proceedings",

note = "2006 IEEE International Conference on Image Processing, ICIP 2006 ; Conference date: 08-10-2006 Through 11-10-2006",

}

Unal, G, Slabaugh, G, Ess, A, Yezzi, A, Fang, T, Tyan, J, Requardt, M, Krieg, R, Seethamraju, R, Harisinghani, M & Weissleder, R 2006, Semi-automatic lymph node segmentation in LN-MRI. in 2006 IEEE International Conference on Image Processing, ICIP 2006 - Proceedings., 4106470, Proceedings - International Conference on Image Processing, ICIP, pp. 77-80, 2006 IEEE International Conference on Image Processing, ICIP 2006, Atlanta, GA, United States, 8/10/06. https://doi.org/10.1109/ICIP.2006.312366

TY - GEN

T1 - Semi-automatic lymph node segmentation in LN-MRI

AU - Unal, G.

AU - Slabaugh, G.

AU - Ess, A.

AU - Yezzi, A.

AU - Fang, T.

AU - Tyan, J.

AU - Requardt, M.

AU - Krieg, R.

AU - Seethamraju, R.

AU - Harisinghani, M.

AU - Weissleder, R.

PY - 2006

Y1 - 2006

N2 - Accurate staging of nodal cancer still relies on surgical exploration because many primary malignancies spread via lymphatic dissemination. The purpose of this study was to utilize nanoparticle-enhanced lymphotropic magnetic resonance imaging (LN-MRI) to explore semi-automated noninvasive nodal cancer staging. We present a joint image segmentation and registration approach, which makes use of the problem specific information to increase the robustness of the algorithm to noise and weak contrast often observed in medical imaging applications. The effectiveness of the approach is demonstrated with a given lymph node segmentation problem in post-contrast pelvic MRI sequences.

AB - Accurate staging of nodal cancer still relies on surgical exploration because many primary malignancies spread via lymphatic dissemination. The purpose of this study was to utilize nanoparticle-enhanced lymphotropic magnetic resonance imaging (LN-MRI) to explore semi-automated noninvasive nodal cancer staging. We present a joint image segmentation and registration approach, which makes use of the problem specific information to increase the robustness of the algorithm to noise and weak contrast often observed in medical imaging applications. The effectiveness of the approach is demonstrated with a given lymph node segmentation problem in post-contrast pelvic MRI sequences.

KW - Biomedical image processing

KW - Biomedical magnetic resonance imaging

KW - Image segmentation

KW - Medical diagnosis

UR - https://www.scopus.com/pages/publications/63149099059

U2 - 10.1109/ICIP.2006.312366

DO - 10.1109/ICIP.2006.312366

M3 - Conference contribution

AN - SCOPUS:63149099059

SN - 1424404819

SN - 9781424404810

T3 - Proceedings - International Conference on Image Processing, ICIP

SP - 77

EP - 80

BT - 2006 IEEE International Conference on Image Processing, ICIP 2006 - Proceedings

T2 - 2006 IEEE International Conference on Image Processing, ICIP 2006

Y2 - 8 October 2006 through 11 October 2006

ER -

Semi-automatic lymph node segmentation in LN-MRI

Abstract

Publication series

Conference

UN SDGs

Keywords

Access to Document

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