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.
UR - http://www.scopus.com/inward/record.url?scp=84869997279&partnerID=8YFLogxK
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 -
