title: Computer-aided detection of metastatic brain tumors using automated three-dimensional template matching.
abstract: OBJECTIVE
  To demonstrate the efficacy of an automated three-dimensional (3D) template matching-based algorithm in detecting brain metastases on conventional MR scans and the potential of our algorithm to be developed into a computer-aided detection tool that will allow radiologists to maintain a high level of detection sensitivity while reducing image reading time.
  
  
  METHODS
  Spherical tumor appearance models were created to match the expected geometry of brain metastases while accounting for partial volume effects and offsets due to the cut of MRI sampling planes. A 3D normalized cross-correlation coefficient was calculated between the brain volume and spherical templates of varying radii using a fast frequency domain algorithm to identify likely positions of brain metastases.
  
  
  RESULTS
  Algorithm parameters were optimized on training datasets, and then data were collected on 22 patient datasets containing 79 total brain metastases producing a sensitivity of 89.9% with a false positive rate of 0.22 per image slice when restricted to the brain mass.
  
  
  CONCLUSIONS
  Study results demonstrate that the 3D template matching-based method can be an effective, fast, and accurate approach that could serve as a useful tool for assisting radiologists in providing earlier and more definitive diagnoses of metastases within the brain.
authors.affiliation: Department of Biomedical Engineering, University of Rochester, Rochester, New York, USA.
authors.forename: Robert D
authors.initials: RD
authors.lastname: Ambrosini
authors.forename: Peng
authors.initials: P
authors.lastname: Wang
authors.forename: Walter G
authors.initials: WG
authors.lastname: O'Dell
mesh_terms: D000465:Algorithms; D001185:Artificial Intelligence; D001932:Brain Neoplasms / Q000175:diagnosis* / Q000556:secondary*; D006801:Humans; D007089:Image Enhancement / Q000379:methods; D007090:Image Interpretation, Computer-Assisted / Q000379:methods*; D021621:Imaging, Three-Dimensional / Q000379:methods*; D010363:Pattern Recognition, Automated / Q000379:methods*; D015203:Reproducibility of Results; D012680:Sensitivity and Specificity; D013382:Subtraction Technique