title: Integrating Structural and Functional Imaging for Computer Assisted Detection of Prostate Cancer on Multi-ProtocolIn Vivo3 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 resolutionin vivomulti-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 vivohistological 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 thein vivoMRI, 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.
authors.affiliation: Department of Biomedical Engineering, Rutgers University, NJ, USA 08854.
authors.forename: Satish
authors.initials: S
authors.lastname: Viswanath
authors.affiliation: Beth Israel Deaconess Medical Center, MA, USA 02215.
authors.forename: B Nicolas
authors.initials: BN
authors.lastname: Bloch
authors.affiliation: Department of Radiology, University of Pennsylvania, PA, USA 19104.
authors.forename: Mark
authors.initials: M
authors.lastname: Rosen
authors.affiliation: Department of Biomedical Engineering, Rutgers University, NJ, USA 08854.
authors.forename: Jonathan
authors.initials: J
authors.lastname: Chappelow
authors.affiliation: Department of Biomedical Engineering, Rutgers University, NJ, USA 08854.
authors.forename: Robert
authors.initials: R
authors.lastname: Toth
authors.affiliation: Beth Israel Deaconess Medical Center, MA, USA 02215.
authors.forename: Neil
authors.initials: N
authors.lastname: Rofsky
authors.affiliation: Beth Israel Deaconess Medical Center, MA, USA 02215.
authors.forename: Robert
authors.initials: R
authors.lastname: Lenkinski
authors.affiliation: Beth Israel Deaconess Medical Center, MA, USA 02215.
authors.forename: Elisabeth
authors.initials: E
authors.lastname: Genega
authors.affiliation: Teleradiology Solutions Pvt. Ltd., Bangalore, India 560048.
authors.forename: Arjun
authors.initials: A
authors.lastname: Kalyanpur
authors.affiliation: Department of Biomedical Engineering, Rutgers University, NJ, USA 08854.
authors.forename: Anant
authors.initials: A
authors.lastname: Madabhushi
keywords: 3 Tesla; CAD; DCE-MRI; T2-w MRI; bagging; data fusion; decision fusion; decision trees; multimodal integration; non-rigid registration; prostate cancer; random forests; segmentation; supervised learning