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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).
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