Influence of using manual or automatic breast density information in a mass detection CAD system.
OBJECTIVE
The goal of this article is to analyze and compare the performance of a developed mass computer-aided detection (CAD) system that takes breast density information into account when using manual or automatic breast density annotations in the training step. The advantages of considering this breast density information will be highlighted.


METHODS
The image database used in this article is 92 mediolateral oblique (MLO) and 92 craniocaudal (CC) mammograms obtained by a full-field digital mammographic unit. All mammograms contain at least one mass. The evaluation of the experiments is performed using free receiver operating characteristic analysis for evaluating the detection performance and pixel-based receiver operating characteristic analysis for evaluating the segmentation accuracy. In addition, the performance of the automatic breast density classifier is shown using confusion matrices.


RESULTS
When the breast density information is not considered and at a specificity of two false positives per image, the sensitivity obtained by the CAD system is 0.747 for the CC views and 0.853 for the MLO views. Considering the breast density information, the sensitivity for CC and MLO mammograms increases to 0.800 and 0.893, respectively, using manual classification, and 0.827 and 0.907, respectively, using automatic estimation. The same trend is observed when evaluating the CAD segmentation accuracy for detected masses in terms of area under the curve values: without considering breast density, these are 0.920 +/- 0.057 and 0.917 +/- 0.072; using manual classification, 0.934 +/- 0.039 and 0.932 +/- 0.046; and using automatic estimation, 0.947 +/- 0.038 and 0.946 +/- 0.045 for CC and MLO views, respectively.


CONCLUSIONS
The experiments showed improved results when breast density information was taken into account. Moreover, the results obtained when using automatic breast density estimation outperformed those based on the manual annotations provided by expert radiologists. In this sense, the experiments showed that breast density information can be beneficial for CAD systems, and this information can be estimated robustly by an automatic procedure, which reduces the inter- and intra-class variability of the radiologists.
Oliver
Arnau
A
Department of Computer Architecture and Technology, University of Girona, Spain. aoliver@eia.udg.edu <aoliver@eia.udg.edu>
Lladó
Xavier
X
Freixenet
Jordi
J
Martí
Robert
R
Pérez
Elsa
E
Pont
Josep
J
Zwiggelaar
Reyer
R
D000328:Adult; D000368:Aged; D000465:Algorithms; D001185:Artificial Intelligence; D001943:Breast Neoplasms / Q000000981:diagnostic imaging*; D003720:Densitometry / Q000379:methods*; D005260:Female; D006801:Humans; D008327:Mammography / Q000379:methods*; D008875:Middle Aged; D010363:Pattern Recognition, Automated / Q000379:methods*; D011856:Radiographic Image Enhancement / Q000379:methods; D011857:Radiographic Image Interpretation, Computer-Assisted / Q000379:methods*; D015203:Reproducibility of Results; D012680:Sensitivity and Specificity; D055815:Young Adult