Integrating structural and functional imaging for computer assisted detection of prostate cancer on multi-protocol in vivo 3 Tesla MRI

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.

A fully automatic computer aided diagnosis system for peripheral zone prostate cancer detection using multi-parametric magnetic resonance imaging

“…Most of the recent works describing the development of a CAD system did not apply a registration algorithm before feature extraction [5,10]. Previously, only Vos et al [32] and Viswanath et al [39] embedded a fully automatic registration algorithm into a CAD system for PCa on mp-MRI. Vos et al [32] aligned T2w and Proton Density (PD)-T1-w images, from which they extracted pharmacokinetics maps, while Viswanath et al [39] performed an affine registration between the T2w image and the 5th contrast-enhanced frame of the DCE acquisition.…”

“…Previously, only Vos et al [32] and Viswanath et al [39] embedded a fully automatic registration algorithm into a CAD system for PCa on mp-MRI. Vos et al [32] aligned T2w and Proton Density (PD)-T1-w images, from which they extracted pharmacokinetics maps, while Viswanath et al [39] performed an affine registration between the T2w image and the 5th contrast-enhanced frame of the DCE acquisition. The latter approach does not consider that registration errors could occur due to patients movements [5].…”

“…Conversely, our method is able to align each contrast-enhanced frame to the T2w image. Moreover, neither Vos et al [32] nor Viswanath [39] assess a specific framework to correct for image distortion and patients movements during EPI-DW imaging acquisitions.…”

A novel NMF-based DWI CAD framework for prostate cancer.

“…4. In several CAD systems, a combination of these MRI techniques has been used for diagnosing prostate cancer [68][69][70][71][72][73][74]. This is often called multi-parametric imaging.…”

“…Another semi-automated system that utilized T2-MRI and DCE-MRI as input was developed by Viswanath et al [68]. An ASM model was initialized by a manually placed bounding-box and then guided by image intensity, image texture and mutual information to segment the prostate region.…”