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Liver segmentation from registered multiphase CT data sets with EM clustering and GVF level set

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Liver segmentation from registered multiphase CT data sets with EM clustering and GVF level set. / Tao, Guozhi; Singh, Ashish; Bidaut, Luc.

Medical Imaging 2010: Image Processing (Proceedings Volume). ed. / Benoit M. Dawant; David R. Haynor. Bellingham : SPIE-International Society for Optical Engineering, 2010. p. - (Progress im biomedical optics and imaging; Vol. 11, No. 33), (Proceedings SPIE; Vol. 7623).

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

Harvard

Tao, G, Singh, A & Bidaut, L 2010, 'Liver segmentation from registered multiphase CT data sets with EM clustering and GVF level set'. in BM Dawant & DR Haynor (eds), Medical Imaging 2010: Image Processing (Proceedings Volume). Progress im biomedical optics and imaging, no. 33, vol. 11, Proceedings SPIE, vol. 7623, SPIE-International Society for Optical Engineering, Bellingham, pp. -, SPIE Medical Imaging 2010 , San Diego, United States, 13-18 February., 10.1117/12.844529

APA

Tao, G., Singh, A., & Bidaut, L. (2010). Liver segmentation from registered multiphase CT data sets with EM clustering and GVF level set. In B. M. Dawant, & D. R. Haynor (Eds.), Medical Imaging 2010: Image Processing (Proceedings Volume). (pp. -). (Progress im biomedical optics and imaging; Vol. 11, No. 33), (Proceedings SPIE; Vol. 7623). Bellingham: SPIE-International Society for Optical Engineering. 10.1117/12.844529

Vancouver

Tao G, Singh A, Bidaut L. Liver segmentation from registered multiphase CT data sets with EM clustering and GVF level set. In Dawant BM, Haynor DR, editors, Medical Imaging 2010: Image Processing (Proceedings Volume). Bellingham: SPIE-International Society for Optical Engineering. 2010. p. -. (Progress im biomedical optics and imaging; 33). (Proceedings SPIE). Available from: 10.1117/12.844529

Author

Tao, Guozhi; Singh, Ashish; Bidaut, Luc / Liver segmentation from registered multiphase CT data sets with EM clustering and GVF level set.

Medical Imaging 2010: Image Processing (Proceedings Volume). ed. / Benoit M. Dawant; David R. Haynor. Bellingham : SPIE-International Society for Optical Engineering, 2010. p. - (Progress im biomedical optics and imaging; Vol. 11, No. 33), (Proceedings SPIE; Vol. 7623).

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

Bibtex - Download

@inbook{274b88d593cc43bd948e7d55fd360941,
title = "Liver segmentation from registered multiphase CT data sets with EM clustering and GVF level set",
keywords = "Liver segmentation, Multiple phase CT, Nonlinear registration, Level set, GVF, Cancer, Images",
author = "Guozhi Tao and Ashish Singh and Luc Bidaut",
year = "2010",
doi = "10.1117/12.844529",
editor = "Dawant, {Benoit M.} and Haynor, {David R.}",
isbn = "9780819480248",
series = "Progress im biomedical optics and imaging",
pages = "-",
booktitle = "Medical Imaging 2010",

}

RIS (suitable for import to EndNote) - Download

TY - CHAP

T1 - Liver segmentation from registered multiphase CT data sets with EM clustering and GVF level set

A1 - Tao,Guozhi

A1 - Singh,Ashish

A1 - Bidaut,Luc

AU - Tao,Guozhi

AU - Singh,Ashish

AU - Bidaut,Luc

PB - SPIE-International Society for Optical Engineering

CY - Bellingham

PY - 2010

Y1 - 2010

N2 - <p>In this study, clinically produced multiphase CT volumetric data sets (pre-contrast, arterial and venous enhanced phase) are drawn upon to transcend the intrinsic limitations of single phase data sets for the robust and accurate segmentation of the liver in typically challenging cases. As an initial step, all other phase volumes are registered to either the arterial or venous phase volume by a symmetric nonlinear registration method using mutual information as similarity metric. Once registered, the multiphase CT volumes are pre-filtered to prepare for subsequent steps. Under the assumption that the intensity vectors of different organs follow the Gaussian Mixture model (GMM), expectation maximization (EM) is then used to classify the multiphase voxels into different clusters. The clusters for liver parenchyma, vessels and tumors are combined together and provide the initial liver mask that is used to generate initial zeros level set. Conversely, the voxels classified as non-liver will guide the speed image of the level sets in order to reduce leakage. Geodesic active contour level set using the gradient vector flow (GVF) derived from one of the enhanced phase volumes is then performed to further evolve the liver segmentation mask. Using EM clusters as the reference, the resulting liver mask is finally morphologically post-processed to add missing clusters and reduce leakage. The proposed method has been tested on the clinical data sets of ten patients with relatively complex and/or extensive liver cancer or metastases. A 95.8% dice similarity index when compared to expert manual segmentation demonstrates the high performance and the robustness of our proposed method - even for challenging cancer data sets - and confirms the potential of a more thorough computational exploitation of currently available clinical data sets.</p>

AB - <p>In this study, clinically produced multiphase CT volumetric data sets (pre-contrast, arterial and venous enhanced phase) are drawn upon to transcend the intrinsic limitations of single phase data sets for the robust and accurate segmentation of the liver in typically challenging cases. As an initial step, all other phase volumes are registered to either the arterial or venous phase volume by a symmetric nonlinear registration method using mutual information as similarity metric. Once registered, the multiphase CT volumes are pre-filtered to prepare for subsequent steps. Under the assumption that the intensity vectors of different organs follow the Gaussian Mixture model (GMM), expectation maximization (EM) is then used to classify the multiphase voxels into different clusters. The clusters for liver parenchyma, vessels and tumors are combined together and provide the initial liver mask that is used to generate initial zeros level set. Conversely, the voxels classified as non-liver will guide the speed image of the level sets in order to reduce leakage. Geodesic active contour level set using the gradient vector flow (GVF) derived from one of the enhanced phase volumes is then performed to further evolve the liver segmentation mask. Using EM clusters as the reference, the resulting liver mask is finally morphologically post-processed to add missing clusters and reduce leakage. The proposed method has been tested on the clinical data sets of ten patients with relatively complex and/or extensive liver cancer or metastases. A 95.8% dice similarity index when compared to expert manual segmentation demonstrates the high performance and the robustness of our proposed method - even for challenging cancer data sets - and confirms the potential of a more thorough computational exploitation of currently available clinical data sets.</p>

KW - Liver segmentation

KW - Multiple phase CT

KW - Nonlinear registration

KW - Level set

KW - GVF

KW - Cancer

KW - Images

U2 - 10.1117/12.844529

DO - 10.1117/12.844529

M1 - Conference contribution

SN - 9780819480248

BT - Medical Imaging 2010

T2 - Medical Imaging 2010

A2 - Haynor,David R.

ED - Haynor,David R.

T3 - Progress im biomedical optics and imaging

T3 - en_GB

SP - -

ER -

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