Computer-aided detection of metastatic brain tumors using automated three-dimensional template matching

• Published in: Journal of magnetic resonance imaging Vol. 31; no. 1; pp. 85 - 93
• Main Authors: Ambrosini, Robert D., Wang, Peng, O'Dell, Walter G.
• Format: Journal Article
• Language: English
• Published: Hoboken Wiley Subscription Services, Inc., A Wiley Company 01.01.2010; Wiley Subscription Services, Inc
• Subjects: Algorithms; Artificial Intelligence; brain; Brain Neoplasms - diagnosis; Brain Neoplasms - secondary; CAD; Humans; Image Enhancement - methods; Image Interpretation, Computer-Assisted - methods; Imaging, Three-Dimensional - methods; Magnetic resonance imaging; object detection; Pattern Recognition, Automated - methods; Reproducibility of Results; Sensitivity and Specificity; Subtraction Technique; tumors
• ISSN: 1053-1807; 1522-2586; 1522-2586
• DOI: 10.1002/jmri.22009

Purpose: 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. Materials and 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. Conclusion: 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. J. Magn. Reson. Imaging 2010;31:85–93. © 2009 Wiley‐Liss, Inc.