title: Enhanced Multi-Protocol Analysis via Intelligent Supervised Embedding (EMPrAvISE): Detecting Prostate Cancer on Multi-Parametric MRI.
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-operativein vivomulti-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).
authors.affiliation: Rutgers, the State University of New Jersey, USA.
authors.forename: Satish
authors.initials: S
authors.lastname: Viswanath
authors.affiliation: Boston Medical Center, Boston, MA, USA.
authors.forename: B Nicolas
authors.initials: BN
authors.lastname: Bloch
authors.affiliation: Rutgers, the State University of New Jersey, USA.
authors.forename: Jonathan
authors.initials: J
authors.lastname: Chappelow
authors.affiliation: Rutgers, the State University of New Jersey, USA.
authors.forename: Pratik
authors.initials: P
authors.lastname: Patel
authors.affiliation: UT Southwestern Medical School, Dallas, Texas, USA.
authors.forename: Neil
authors.initials: N
authors.lastname: Rofsky
authors.affiliation: Beth Israel Deaconess Medical Center, Boston, MA USA.
authors.forename: Robert
authors.initials: R
authors.lastname: Lenkinski
authors.affiliation: Beth Israel Deaconess Medical Center, Boston, MA USA.
authors.forename: Elisabeth
authors.initials: E
authors.lastname: Genega
authors.affiliation: Rutgers, the State University of New Jersey, USA.
authors.forename: Anant
authors.initials: A
authors.lastname: Madabhushi
keywords: 3 Tesla; CAD; DCE-MRI; DWI-MRI; T2w MRI; ensemble embedding; multi-modal integration; multi-parametric; multi-protocol; non-rigid registration; probabilistic boosting trees; prostate cancer; supervised learning