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Proceedings Paper

Enhanced multi-protocol analysis via intelligent supervised embedding (EMPrAvISE): detecting prostate cancer on multi-parametric MRI
Author(s): Satish Viswanath; B. Nicholas Bloch; Jonathan Chappelow; Pratik Patel; Neil Rofsky; Robert Lenkinski; Elizabeth Genega; Anant Madabhushi
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Paper Abstract

Currently, there is significant interest in developing methods for quantitative integration of multi-parametric (structural, functional) imaging data with the objective of building automated meta-classifiers to improve disease detection, diagnosis, and prognosis. Such techniques are required to address the differences in dimensionalities and scales of individual protocols, while deriving an integrated multi-parametric data representation which best captures all disease-pertinent information available. In this paper, we present a scheme called Enhanced Multi-Protocol Analysis via Intelligent Supervised Embedding (EMPrAvISE); a powerful, generalizable framework applicable to a variety of domains for multi-parametric data representation and fusion. Our scheme utilizes an ensemble of embeddings (via dimensionality reduction, DR); thereby exploiting the variance amongst multiple uncorrelated embeddings in a manner similar to ensemble classifier schemes (e.g. Bagging, Boosting). We apply this framework to the problem of prostate cancer (CaP) detection on 12 3 Tesla pre-operative in vivo multi-parametric (T2-weighted, Dynamic Contrast Enhanced, and Diffusion-weighted) magnetic resonance imaging (MRI) studies, in turn comprising a total of 39 2D planar MR images. We first align the different imaging protocols via automated image registration, followed by quantification of image attributes from individual protocols. Multiple embeddings are generated from the resultant high-dimensional feature space which are then combined intelligently to yield a single stable solution. Our scheme is employed in conjunction with graph embedding (for DR) and probabilistic boosting trees (PBTs) to detect CaP on multi-parametric MRI. Finally, a probabilistic pairwise Markov Random Field algorithm is used to apply spatial constraints to the result of the PBT classifier, yielding a per-voxel classification of CaP presence. Per-voxel evaluation of detection results against ground truth for CaP extent on MRI (obtained by spatially registering pre-operative MRI with available whole-mount histological specimens) reveals that EMPrAvISE yields a statistically significant improvement (AUC=0.77) over classifiers constructed from individual protocols (AUC=0.62, 0.62, 0.65, for T2w, DCE, DWI respectively) as well as one trained using multi-parametric feature concatenation (AUC=0.67).

Paper Details

Date Published: 4 March 2011
PDF: 15 pages
Proc. SPIE 7963, Medical Imaging 2011: Computer-Aided Diagnosis, 79630U (4 March 2011); doi: 10.1117/12.878312
Show Author Affiliations
Satish Viswanath, Rutgers, The State Univ. of New Jersey (United States)
B. Nicholas Bloch, Boston Medical Ctr. (United States)
Jonathan Chappelow, Rutgers, The State Univ. of New Jersey (United States)
Pratik Patel, Rutgers, The State Univ. of New Jersey (United States)
Neil Rofsky, The Univ. of Texas Southwestern Medical Ctr. at Dallas (United States)
Robert Lenkinski, Beth Israel Deaconess Medical Ctr. (United States)
Elizabeth Genega, Beth Israel Deaconess Medical Ctr. (United States)
Anant Madabhushi, Rutgers, The State Univ. of New Jersey (United States)


Published in SPIE Proceedings Vol. 7963:
Medical Imaging 2011: Computer-Aided Diagnosis
Ronald M. Summers; Bram van Ginneken, Editor(s)

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