Breast density quantification using magnetic resonance imaging (MRI) with bias field correction: a postmortem study

Abstract

Purpose: Quantification of breast density based on three-dimensional breast MRI may provide useful information for the early detection of breast cancer. However, the field inhomogeneity can severely challenge the computerized image segmentation process. In this work, the effect of the bias field in breast density quantification has been investigated with a postmortem study.

Methods: T1-weighted images of 20 pairs of postmortem breasts were acquired on a 1.5 T breast MRI scanner. Two computer-assisted algorithms were used to quantify the volumetric breast density. First, standard fuzzy c-means (FCM) clustering was used on raw images with the bias field present. Then, the coherent local intensity clustering (CLIC) method estimated and corrected the bias field during the iterative tissue segmentation process. Finally, FCM clustering was performed on the bias-field-corrected images produced by CLIC method. The left-right correlation for breasts in the same pair was studied for both segmentation algorithms to evaluate the precision of the tissue classification. Finally, the breast densities measured with the three methods were compared to the gold standard tissue compositions obtained from chemical analysis. The linear correlation coefficient, Pearson's r, was used to evaluate the two image segmentation algorithms and the effect of bias field.

Results: The CLIC method successfully corrected the intensity inhomogeneity induced by the bias field. In left-right comparisons, the CLIC method significantly improved the slope and the correlation coefficient of the linear fitting for the glandular volume estimation. The left-right breast density correlation was also increased from 0.93 to 0.98. When compared with the percent fibroglandular volume (%FGV) from chemical analysis, results after bias field correction from both the CLIC the FCM algorithms showed improved linear correlation. As a result, the Pearson's r increased from 0.86 to 0.92 with the bias field correction.

Conclusions: The investigated CLIC method significantly increased the precision and accuracy of breast density quantification using breast MRI images by effectively correcting the bias field. It is expected that a fully automated computerized algorithm for breast density quantification may have great potential in clinical MRI applications.

Figure 1

Left–right comparison between breasts from the same pair for the glandular volume (a) and breast density (b) measured with the standard FCM method, where the effect of bias field has not been corrected in the raw images. The linear fittings are shown as the straight lines in the plots. The fitting parameters and the correlation coefficients are shown for both plots.

Figure 2

Left–right comparison between breasts from the same pair for the glandular volume (a) and breast density (b) measured with the CLIC method, which estimated and corrected the bias field during tissue segmentation. The linear fittings are shown as straight lines in the plots. The fitting parameters and the correlation coefficients are shown for both plots.

Figure 3

Total breast volumes measured with the FCM (a) and CLIC (b) methods as a function of that obtained from the chemical analysis. Both methods can successfully predict the breast volume with correlation coefficients over 0.99.

Figure 4

The correlation between breast densities measured with the FCM method and the %FGV from chemical analysis. The linear fitting is shown as the straight line. The Pearson's r is estimated to be 0.86.

Figure 5

An example of the effects of the bias field on segmentation: (a) raw image with the bias field present; (b) bias-field-corrected image produced by the CLIC method; (c) tissue classification using the FCM method on the raw image; (d) tissue classification using the CLIC method.

Figure 6

The correlation between breast densities measured with the CLIC method and the %FGV from chemical analysis. The linear fitting is shown as the straight line. The Pearson's r is estimated to be 0.92.

Figure 7

(a) The correlation between breast densities measured with the FCM method on the bias-field-corrected images generated from the CLIC method and the %FGV from chemical analysis from one of the two readers. The linear fitting is shown as the straight line. (b) The Bland-Altman plot for the comparison between the results from the two readers. Only a small inter-reader variation was found in the FCM segmentation.