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

Satish Viswanath, B. Nicolas Bloch, Mark Rosen, Jonathan Chappelow, Robert Toth, Neil Rofsky, Robert Lenkinski, Elisabeth Genega, Arjun Kalyanpur, Anant Madabhushi

Research output: Chapter in Book/Report/Conference proceedingConference contribution

33 Scopus citations


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.

Original languageEnglish (US)
Title of host publicationMedical Imaging 2009
Subtitle of host publicationComputer-Aided Diagnosis
StatePublished - 2009
EventMedical Imaging 2009: Computer-Aided Diagnosis - Lake Buena Vista, FL, United States
Duration: Feb 10 2009Feb 12 2009

Publication series

NameProgress in Biomedical Optics and Imaging - Proceedings of SPIE
ISSN (Print)1605-7422


OtherMedical Imaging 2009: Computer-Aided Diagnosis
Country/TerritoryUnited States
CityLake Buena Vista, FL


  • 3 Tesla
  • Bagging
  • CAD
  • Data fusion
  • Decision fusion
  • Decision trees
  • Multimodal integration
  • Non-rigid registration
  • Prostate cancer
  • Random forests
  • Segmentation
  • Supervised learning
  • T2-w MRI

ASJC Scopus subject areas

  • Electronic, Optical and Magnetic Materials
  • Biomaterials
  • Atomic and Molecular Physics, and Optics
  • Radiology Nuclear Medicine and imaging


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