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Proceedings Paper

Improving CAD performance in detecting masses depicted on prior images
Author(s): Bin Zheng; Xiao Hui Wang; Luisa Wallace; Cathy Cohen; Lara A. Hardesty; Christiane M. Hakim; Gordon Abran; Jules H. Sumkin; David Gur
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Paper Abstract

We investigated a new approach to improve the performance of a computer-aided detection (CAD) scheme in identifying masses depicted on images acquired earlier ("prior"). The scheme was trained using a dataset with simulated mass features. From a database with images acquired during two consecutive examinations, 100 locations matched pairs of malignant mass regions were selected in both the “current” and the most recent “prior” images. While reviewing the current images, mass regions were identified and as a result biopsies were ultimately performed. Prior images were not identified as suspicious by radiologists during the original interpretation. The same number of false-positive regions was also selected in both current and prior images. The selected regions were then randomly divided into training and testing datasets with 50 true-positive and 50 false-positive regions in each. For each selected region, five features; area, contrast, circularity, normalized standard deviation of radial length, and conspicuity; were computed. The ratios of the average difference of five feature values between current and prior mass regions in the training datasets were also computed. Multiplying these ratios by the computed values in current mass regions, we generated a new dataset of simulated features of “prior” mass regions. Three artificial neural networks (ANN) were trained. ANN-1 and ANN-2 were trained using training datasets of current and prior regions, respectively. ANN-3 was trained using simulated “prior” dataset. The performance of three ANNs was then evaluated using the testing dataset of prior images. Areas under ROC curves (Az) were 0.613 ± 0.026 for ANN-1, 0.678 ± 0.029 for ANN-2, and 0.667 ± 0.029 for ANN-3, respectively. This preliminary study demonstrated that one could estimate an average change of feature values over time and "adjust" CAD performance for better detection of masses at an earlier stage.

Paper Details

Date Published: 15 May 2003
PDF: 7 pages
Proc. SPIE 5032, Medical Imaging 2003: Image Processing, (15 May 2003); doi: 10.1117/12.480291
Show Author Affiliations
Bin Zheng, Univ. of Pittsburgh (United States)
Xiao Hui Wang, Univ. of Pittsburgh (United States)
Luisa Wallace, Magee Womens Hospital (United States)
Cathy Cohen, Magee Womens Hospital (United States)
Lara A. Hardesty, Magee Womens Hospital (United States)
Christiane M. Hakim, Magee Womens Hospital (United States)
Gordon Abran, Magee Womens Hospital (United States)
Jules H. Sumkin, Magee Womens Hospital (United States)
David Gur, Univ. of Pittsburgh (United States)


Published in SPIE Proceedings Vol. 5032:
Medical Imaging 2003: Image Processing
Milan Sonka; J. Michael Fitzpatrick, Editor(s)

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