Accuracy of segmentation of a commercial computer-aided detection system for mammography.
OBJECTIVE
To assess the accuracy of segmentation in a commercially available computer-aided detection (CAD) system.


METHODS
Approval for this study was obtained from the authors' institutional review board. Informed consent was not required by the board for this review, as data were stripped of patient identifiers. Two thousand twenty mammograms from 507 women were analyzed with the hardware and software of a commercial CAD system. The accuracy of the segmentation process was determined semiquantitatively and categorized as near perfect if the skin line of the breast was accurately detected, acceptable if only subcutaneous fat was excluded, or unacceptable if any breast parenchyma was excluded from consideration. The accuracy of segmentation was compared for different breast densities and film sizes by using logistic regression (P < .05).


RESULTS
Overall, segmentation was near perfect or acceptable in almost 96.8% of images. However, segmentation defects were significantly more common in mammograms with heterogeneously dense breast tissue (8% unacceptable) than in those with fatty replaced (0% unacceptable), scattered (1.2% unacceptable), or extremely dense (1.8% unacceptable) breast parenchyma (P < .05). For images with unacceptable segmentation, the average percentage of breast parenchyma excluded was almost 25% (range, 5%-100%), with no significant differences among breast densities.


CONCLUSIONS
For one commercial CAD system, segmentation was usually near perfect or acceptable but was unacceptable more than five times more frequently for mammograms of breasts with heterogeneously dense parenchyma than for those with all other breast densities. On average, one-quarter of the breast parenchyma was excluded from CAD analysis for images with unacceptable segmentation.
Baker
Jay A
JA
Department of Radiology, Duke University Medical Center, Box 3800, Erwin Road, Durham, NC 27710, USA. jay.baker@duke.edu
Rosen
Eric L
EL
Crockett
Michele M
MM
Lo
Joseph Y
JY
D000328:Adult; D000368:Aged; D047688:Breast Cyst / Q000000981:diagnostic imaging*; D001943:Breast Neoplasms / Q000000981:diagnostic imaging*; D003936:Diagnosis, Computer-Assisted / Q000706:statistics & numerical data*; D005260:Female; D006801:Humans; D007091:Image Processing, Computer-Assisted / Q000706:statistics & numerical data*; D016015:Logistic Models; D008327:Mammography / Q000706:statistics & numerical data*; D008403:Mass Screening / Q000706:statistics & numerical data; D008875:Middle Aged; D016609:Neoplasms, Second Primary / Q000000981:diagnostic imaging; D015588:Observer Variation; D011446:Prospective Studies; D012680:Sensitivity and Specificity; D013673:Technology Assessment, Biomedical / Q000706:statistics & numerical data