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L. A. Meinel, A. H. Stolpen, K. S. Berbaum, L. L. Fajardo, and J. M. Reinhardt. Breast MRI Lesion Classification: Improved Performance of Human Readers with a Backpropagation Neural Network Computer-Aided Diagnosis (CAD) System. J. Magn. Res. Imaging, vol. 25, no. 1, pp. 85-95, 2007.

Abstract: Purpose: To develop and test a computer-aided diagnosis (CAD) system to improve the performance of radiologists in classifying lesions on breast MRI (BMRI). Materials and Methods: A CAD system was developed that uses a semiautomated segmentation method. After segmentation, 42 features based on lesion shape, texture, and enhancement kinetics were computed, and the 13 best features were selected and used as inputs to a backpropagation neural network (BNN). The BNN was trained and tested using the leave-one-out method on 80 BMRI lesions (37 benign, 43 malignant). Lesion histopathology was used as the reference standard. Five human readers classified the 80 lesions first without and then with CAD assistance. The performance of the computer classifier and the human readers was assessed using receiver operating characteristic curves; the performance of the human readers was also evaluated using multireader multicase (MRMC) analysis. Results: The performance of the human readers significantly improved when aided by the CAD system (P < 0.05). MRMC analysis showed that human reader performance with and without CAD system assistance can be generalized to the population of cases (P < 0.001). Conclusion: A CAD system based on lesion morphology and enhancement kinetics can improve the performance of human readers in classifying lesions on breast MRI. Key Words: breast MRI; neural networks; pattern recognition; computer-aided diagnosis; shape and texture features; kinetics enhancement

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Keywords: breast cad

Other publications by: L. A. Meinel, A. H. Stolpen, K. S. Berbaum, L. L. Fajardo, J. M. Reinhardt

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L. Arbach, G. Fallouh, J. M. Reinhardt, and L. Bennett. Distinguishing between malignant and nonmalignant breast masses from mammograms using artificial intelligence techniques. Bassel Al-Assad J. for Engineering Science, vol. 16, pp. 103-121, 2002. (details)

Related conference papers:

L. Arbach, L. Bennett, J. M. Reinhardt, and G. Fallouh. Breast mass classification: Comparison between human readers and a back-propagation neural network. In M. Sonka and J. M. Fitzpatrick, eds., Proc. SPIE Conf. Medical Imaging, vol. 5032, pp. 810-818, San Diego, CA, 2003. (details)
L. Arbach, L. Bennett, J. M. Reinhardt, and G. Fallouh. Mammogram Breast Mass Classification with Backpropagation Neural Network. In IEEE Canadian Conference on Electrical and Computer Engineering, vol. 3, pp. 1441-1444, 2003. (details)
L. Arbach, A. H. Stolpen, and J. M. Reinhardt. Classification of breast MRI lesions using a backpropagation neural network (BNN). In 2004 International Symposium on Biomedical Imaging, pp. 253-256, 2004. (details)
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Related theses:

L. Arbach. Development of a computer-aided diagnostic system for breast MRI lesion classification. PhD thesis, The University of Iowa, Iowa City, IA, 2005. (details)