Prostate cancer segmentation with multispectral MRI using cost-sensitive conditional random fields

Authors:
Y. Artan;D. L. Langer;M. A. Haider;T. H. van der Kwast;A. J. Evans;M. N. Wernick;I. S. Yetik
Affiliations:
Medical Imaging Research Center, Illinois Institute of Technology, IL;Institute of Medical Science, University of Toronto and Department of Medical Imaging, Princess Margaret Hospital, Ontario, Canada;Institute of Medical Science, University of Toronto and Department of Medical Imaging, Princess Margaret Hospital, Ontario, Canada;Department of Pathology and Laboratory Medicine, Toronto General Hospital, Ontario, Canada;Department of Pathology and Laboratory Medicine, Toronto General Hospital, Ontario, Canada;Medical Imaging Research Center, Illinois Institute of Technology, IL;Medical Imaging Research Center, Illinois Institute of Technology, IL
Venue:
ISBI'09 Proceedings of the Sixth IEEE international conference on Symposium on Biomedical Imaging: From Nano to Macro
Year:
2009

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Prostate cancer localization with multispectral MRI using cost-sensitive support vector machines and conditional random fields

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Abstract

Prostate cancer is a leading cause of cancer death for men in the United States. There is currently no widely adopted accurate noninvasive method for localizing prostate cancer using imaging. If such as technique were available it could be used to guide biopsy, radio-theraphy and surgery. However, current imaging techniques are limited due to inability to detect cancers, intensity changes related to non-malignant pathologies and interobserver variability. Recently, multispectral magnetic resonance imaging (MRI) has emerged as a promising noninvasive method for the localization of prostate cancer alternative to transrectal ultrasound (TRUS). This paper develops automated methods for prostate cancer localization with conditional random fields using multispectral MRI. We propose to combine cost-sensitive Support Vector Machines with Conditional Random Fields and show that this method results in higher accuracy of localization compared to other common methods. Our results also show that multispectral modality images helps to increase the accuracy of prostate cancer localization. Using multispectral MR images, we demonstrate the effectiveness of each algorithm by testing them on real data sets and compare them to recently proposed SVMstruct and Conditional Random Fields.