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Proceedings Paper

Integrating structural and functional imaging for computer assisted detection of prostate cancer on multi-protocol in vivo 3 Tesla MRI
Author(s): Satish Viswanath; B. Nicolas Bloch; Mark Rosen; Jonathan Chappelow; Robert Toth; Neil Rofsky; Robert Lenkinski; Elizabeth Genega; Arjun Kalyanpur; Anant Madabhushi
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Paper Abstract

Screening and detection of prostate cancer (CaP) currently lacks an image-based protocol which is reflected in the high false negative rates currently associated with blinded sextant biopsies. Multi-protocol magnetic resonance imaging (MRI) offers high resolution functional and structural data about internal body structures (such as the prostate). In this paper we present a novel comprehensive computer-aided scheme for CaP detection from high resolution in vivo multi-protocol MRI by integrating functional and structural information obtained via dynamic-contrast enhanced (DCE) and T2-weighted (T2-w) MRI, respectively. Our scheme is fully-automated and comprises (a) prostate segmentation, (b) multimodal image registration, and (c) data representation and multi-classifier modules for information fusion. Following prostate boundary segmentation via an improved active shape model, the DCE/T2-w protocols and the T2-w/ex vivo histological prostatectomy specimens are brought into alignment via a deformable, multi-attribute registration scheme. T2-w/histology alignment allows for the mapping of true CaP extent onto the in vivo MRI, which is used for training and evaluation of a multi-protocol MRI CaP classifier. The meta-classifier used is a random forest constructed by bagging multiple decision tree classifiers, each trained individually on T2-w structural, textural and DCE functional attributes. 3-fold classifier cross validation was performed using a set of 18 images derived from 6 patient datasets on a per-pixel basis. Our results show that the results of CaP detection obtained from integration of T2-w structural textural data and DCE functional data (area under the ROC curve of 0.815) significantly outperforms detection based on either of the individual modalities (0.704 (T2-w) and 0.682 (DCE)). It was also found that a meta-classifier trained directly on integrated T2-w and DCE data (data-level integration) significantly outperformed a decision-level meta-classifier, constructed by combining the classifier outputs from the individual T2-w and DCE channels.

Paper Details

Date Published: 28 February 2009
PDF: 12 pages
Proc. SPIE 7260, Medical Imaging 2009: Computer-Aided Diagnosis, 72603I (28 February 2009); doi: 10.1117/12.811899
Show Author Affiliations
Satish Viswanath, Rutgers Univ. (United States)
B. Nicolas Bloch, Beth Israel Deaconess Medical Ctr. (United States)
Mark Rosen, Univ. of Pennsylvania (United States)
Jonathan Chappelow, Rutgers Univ. (United States)
Robert Toth, Rutgers Univ. (United States)
Neil Rofsky, Beth Israel Deaconess Medical Ctr. (United States)
Robert Lenkinski, Beth Israel Deaconess Medical Ctr. (United States)
Elizabeth Genega, Beth Israel Deaconess Medical Ctr. (United States)
Arjun Kalyanpur, Teleradiology Solutions Pvt. Ltd. (India)
Anant Madabhushi, Rutgers Univ. (United States)


Published in SPIE Proceedings Vol. 7260:
Medical Imaging 2009: Computer-Aided Diagnosis
Nico Karssemeijer; Maryellen L. Giger, Editor(s)

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