MRI confirmed prostate tissue classification with laplacian eigenmaps of ultrasound RF spectra

Mehdi Moradi; Christian Wachinger; Andriy Fedorov; William M. Wells; Tina Kapur; Luciant D. Wolfsberger; Paul Nguyen; Clare M. Tempany

doi:10.1007/978-3-642-35428-1_3

MRI confirmed prostate tissue classification with laplacian eigenmaps of ultrasound RF spectra

Mehdi Moradi
, Christian Wachinger
, Andriy Fedorov
, William M. Wells
, Tina Kapur
, Luciant D. Wolfsberger
, Paul Nguyen
, Clare M. Tempany

University of British Columbia
Brigham and Women's Hospital
Massachusetts Institute of Technology

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

2 Scopus citations

Abstract

The delivery of therapeutic prostate interventions can be improved by intraprocedural visualization of the tumor during ultrasound-guided procedures. To this end, ultrasound-based tissue classification and registration of the clinical target volume from preoperative multiparametric MR images to intraoperative ultrasound are suggested as two potential solutions. In this paper we report techniques to implement both of these solutions. In ultrasound-based tissue typing, we employ Laplacian eigenmaps for reducing the dimensionality of the spectral feature space formed by ultrasound RF power spectra. This is followed by support vector machine classification for separating cancer from normal prostate tissue. A classification accuracy of 78.3±4.8% is reported. We also present a deformable MR-US registration method which relies on transforming the binary label maps acquired by delineating the prostate gland in both MRI and ultrasound. This method is developed to transfer the diagnostic references from MRI to US for training and validation of the proposed ultrasound-based prostate tissue classification technique. It yields a target registration error of 3.5±2.1 mm. We also report its use for MR-based dose boosting during ultrasound-guided brachytherapy.

Original language	English
Title of host publication	Machine Learning in Medical Imaging - Third International Workshop, MLMI 2012, Held in Conjunction with MICCAI 2012, Revised Selected Papers
Pages	19-26
Number of pages	8
DOIs	https://doi.org/10.1007/978-3-642-35428-1_3
State	Published - 2012
Externally published	Yes
Event	3rd International Workshop on Machine Learning in Medical Imaging, MLMI 2012, Held in conjunction with the 15th International Conference on Medical Image Computing and Computer-Assisted Intervention, MICCAI 2012 - Nice, France Duration: 1 Oct 2012 → 1 Oct 2012

Publication series

Name	Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume	7588 LNCS
ISSN (Print)	0302-9743
ISSN (Electronic)	1611-3349

Conference

Conference	3rd International Workshop on Machine Learning in Medical Imaging, MLMI 2012, Held in conjunction with the 15th International Conference on Medical Image Computing and Computer-Assisted Intervention, MICCAI 2012
Country/Territory	France
City	Nice
Period	1/10/12 → 1/10/12

UN SDGs

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

SDG 3 Good Health and Well-being

Access to Document

10.1007/978-3-642-35428-1_3

Cite this

Moradi, M., Wachinger, C., Fedorov, A., Wells, W. M., Kapur, T., Wolfsberger, L. D., Nguyen, P., & Tempany, C. M. (2012). MRI confirmed prostate tissue classification with laplacian eigenmaps of ultrasound RF spectra. In Machine Learning in Medical Imaging - Third International Workshop, MLMI 2012, Held in Conjunction with MICCAI 2012, Revised Selected Papers (pp. 19-26). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 7588 LNCS). https://doi.org/10.1007/978-3-642-35428-1_3

Moradi, Mehdi ; Wachinger, Christian ; Fedorov, Andriy et al. / MRI confirmed prostate tissue classification with laplacian eigenmaps of ultrasound RF spectra. Machine Learning in Medical Imaging - Third International Workshop, MLMI 2012, Held in Conjunction with MICCAI 2012, Revised Selected Papers. 2012. pp. 19-26 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)).

@inproceedings{ebf07dde6d9d4f9d8d34cc5883b698bb,

title = "MRI confirmed prostate tissue classification with laplacian eigenmaps of ultrasound RF spectra",

abstract = "The delivery of therapeutic prostate interventions can be improved by intraprocedural visualization of the tumor during ultrasound-guided procedures. To this end, ultrasound-based tissue classification and registration of the clinical target volume from preoperative multiparametric MR images to intraoperative ultrasound are suggested as two potential solutions. In this paper we report techniques to implement both of these solutions. In ultrasound-based tissue typing, we employ Laplacian eigenmaps for reducing the dimensionality of the spectral feature space formed by ultrasound RF power spectra. This is followed by support vector machine classification for separating cancer from normal prostate tissue. A classification accuracy of 78.3±4.8\% is reported. We also present a deformable MR-US registration method which relies on transforming the binary label maps acquired by delineating the prostate gland in both MRI and ultrasound. This method is developed to transfer the diagnostic references from MRI to US for training and validation of the proposed ultrasound-based prostate tissue classification technique. It yields a target registration error of 3.5±2.1 mm. We also report its use for MR-based dose boosting during ultrasound-guided brachytherapy.",

author = "Mehdi Moradi and Christian Wachinger and Andriy Fedorov and Wells, \{William M.\} and Tina Kapur and Wolfsberger, \{Luciant D.\} and Paul Nguyen and Tempany, \{Clare M.\}",

year = "2012",

doi = "10.1007/978-3-642-35428-1\_3",

language = "English",

isbn = "9783642354274",

series = "Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)",

pages = "19--26",

booktitle = "Machine Learning in Medical Imaging - Third International Workshop, MLMI 2012, Held in Conjunction with MICCAI 2012, Revised Selected Papers",

note = "3rd International Workshop on Machine Learning in Medical Imaging, MLMI 2012, Held in conjunction with the 15th International Conference on Medical Image Computing and Computer-Assisted Intervention, MICCAI 2012 ; Conference date: 01-10-2012 Through 01-10-2012",

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Moradi, M, Wachinger, C, Fedorov, A, Wells, WM, Kapur, T, Wolfsberger, LD, Nguyen, P & Tempany, CM 2012, MRI confirmed prostate tissue classification with laplacian eigenmaps of ultrasound RF spectra. in Machine Learning in Medical Imaging - Third International Workshop, MLMI 2012, Held in Conjunction with MICCAI 2012, Revised Selected Papers. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), vol. 7588 LNCS, pp. 19-26, 3rd International Workshop on Machine Learning in Medical Imaging, MLMI 2012, Held in conjunction with the 15th International Conference on Medical Image Computing and Computer-Assisted Intervention, MICCAI 2012, Nice, France, 1/10/12. https://doi.org/10.1007/978-3-642-35428-1_3

MRI confirmed prostate tissue classification with laplacian eigenmaps of ultrasound RF spectra. / Moradi, Mehdi; Wachinger, Christian; Fedorov, Andriy et al.
Machine Learning in Medical Imaging - Third International Workshop, MLMI 2012, Held in Conjunction with MICCAI 2012, Revised Selected Papers. 2012. p. 19-26 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 7588 LNCS).

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

TY - GEN

T1 - MRI confirmed prostate tissue classification with laplacian eigenmaps of ultrasound RF spectra

AU - Moradi, Mehdi

AU - Wachinger, Christian

AU - Fedorov, Andriy

AU - Wells, William M.

AU - Kapur, Tina

AU - Wolfsberger, Luciant D.

AU - Nguyen, Paul

AU - Tempany, Clare M.

PY - 2012

Y1 - 2012

N2 - The delivery of therapeutic prostate interventions can be improved by intraprocedural visualization of the tumor during ultrasound-guided procedures. To this end, ultrasound-based tissue classification and registration of the clinical target volume from preoperative multiparametric MR images to intraoperative ultrasound are suggested as two potential solutions. In this paper we report techniques to implement both of these solutions. In ultrasound-based tissue typing, we employ Laplacian eigenmaps for reducing the dimensionality of the spectral feature space formed by ultrasound RF power spectra. This is followed by support vector machine classification for separating cancer from normal prostate tissue. A classification accuracy of 78.3±4.8% is reported. We also present a deformable MR-US registration method which relies on transforming the binary label maps acquired by delineating the prostate gland in both MRI and ultrasound. This method is developed to transfer the diagnostic references from MRI to US for training and validation of the proposed ultrasound-based prostate tissue classification technique. It yields a target registration error of 3.5±2.1 mm. We also report its use for MR-based dose boosting during ultrasound-guided brachytherapy.

AB - The delivery of therapeutic prostate interventions can be improved by intraprocedural visualization of the tumor during ultrasound-guided procedures. To this end, ultrasound-based tissue classification and registration of the clinical target volume from preoperative multiparametric MR images to intraoperative ultrasound are suggested as two potential solutions. In this paper we report techniques to implement both of these solutions. In ultrasound-based tissue typing, we employ Laplacian eigenmaps for reducing the dimensionality of the spectral feature space formed by ultrasound RF power spectra. This is followed by support vector machine classification for separating cancer from normal prostate tissue. A classification accuracy of 78.3±4.8% is reported. We also present a deformable MR-US registration method which relies on transforming the binary label maps acquired by delineating the prostate gland in both MRI and ultrasound. This method is developed to transfer the diagnostic references from MRI to US for training and validation of the proposed ultrasound-based prostate tissue classification technique. It yields a target registration error of 3.5±2.1 mm. We also report its use for MR-based dose boosting during ultrasound-guided brachytherapy.

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U2 - 10.1007/978-3-642-35428-1_3

DO - 10.1007/978-3-642-35428-1_3

M3 - Conference contribution

AN - SCOPUS:84869997279

SN - 9783642354274

T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

SP - 19

EP - 26

BT - Machine Learning in Medical Imaging - Third International Workshop, MLMI 2012, Held in Conjunction with MICCAI 2012, Revised Selected Papers

T2 - 3rd International Workshop on Machine Learning in Medical Imaging, MLMI 2012, Held in conjunction with the 15th International Conference on Medical Image Computing and Computer-Assisted Intervention, MICCAI 2012

Y2 - 1 October 2012 through 1 October 2012

ER -

Moradi M, Wachinger C, Fedorov A, Wells WM, Kapur T, Wolfsberger LD et al. MRI confirmed prostate tissue classification with laplacian eigenmaps of ultrasound RF spectra. In Machine Learning in Medical Imaging - Third International Workshop, MLMI 2012, Held in Conjunction with MICCAI 2012, Revised Selected Papers. 2012. p. 19-26. (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)). doi: 10.1007/978-3-642-35428-1_3

MRI confirmed prostate tissue classification with laplacian eigenmaps of ultrasound RF spectra

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