Development of a quantitative method for analysis of breast density based on three-dimensional breast MRI

Abstract

Breast density has been established as an independent risk factor associated with the development of breast cancer. It is known that an increase of mammographic density is associated with an increased cancer risk. Since a mammogram is a projection image, different body position, level of compression, and the x-ray intensity may lead to a large variability in the density measurement. Breast MRI provides strong soft tissue contrast between fibroglandular and fatty tissues, and three-dimensional coverage of the entire breast, thus making it suitable for density analysis. To develop the MRI-based method, the first task is to achieve consistency in segmentation of the breast region from the body. The method included an initial segmentation based on body landmarks of each individual woman, followed by fuzzy C-mean (FCM) classification to exclude air and lung tissue, B-spline curve fitting to exclude chest wall muscle, and dynamic searching to exclude skin. Then, within the segmented breast, the adaptive FCM was used for simultaneous bias field correction and fibroglandular tissue segmentation. The intraoperator and interoperator reproducibility was evaluated using 11 selected cases covering a broad spectrum of breast densities with different parenchymal patterns. The average standard deviation for breast volume and percent density measurements was in the range of 3%-4% among three trials of one operator or among three different operators. The body position dependence was also investigated by performing scans of two healthy volunteers, each at five different positions, and found the variation in the range of 3%-4%. These initial results suggest that the technique based on three-dimensional MRI can achieve reasonable consistency to be applied in longitudinal follow-up studies to detect small changes. It may also provide a reliable method for evaluating the change of breast density for risk management of women, or for evaluating the benefits/risks when considering hormonal replacement therapy or chemoprevention.

Figure 1

Overall analysis flow chart for breast segmentation and fibroglandular tissue segmentation.

Figure 2

The process for initial V-shape cut. (a) The first slice showing the level of the aortic arch which identifies the thoracic spine is selected. (b) The V-tip of spinous process of the thoracic spine, and the lateral margin of the bilateral pectoralis muscles are defined as landmarks. Two lines along AC and BC are extended for V-shape cut. (c) The segmentation defines the posterior lateral margin at both sides; also, the arm-folding artifacts seen in (a) and (b) are excluded.

Figure 3

Breast segmentation using fuzzy C-means clustering. (a) original precontrast images after V-shape cut. (b) four-clusters FCM segmentation result. The cluster #1 has the lowest signal intensity, representing air and the lung tissue. (c) cluster #1 is excluded to obtain the breast area that still contains chest wall muscle. The dividing point P between the two clusters representing breast fat and chest wall muscle at the lateral posterior boundary is automatically identified for B-spline fitting. (d) segmented breast after exclusion of chest wall muscle based on the boundary obtained from b-spline fitting.

Figure 4

Illustration of cases with incomplete breast segmentation that requires manual correction. The left column shows a dense breast with fibroglandular tissue connected to the chest wall muscle, and the right column shows one inferior slice where the liver tissue appears right underneath the chest wall muscle. In both cases the contrast for chest wall muscle is not sufficient to allow boundary detection by B-spline fitting. (a) original MR image; (b) computerized segmentation result; (c) manual correction result after the operator tracing the boundary between the breast and the chest wall muscle.

Figure 5

Skin exclusion result. (a) The segmented breast, same as Fig. 3d. (b) The initial FCM clustering segmentation results performed for breast segmentation, same as Fig. 3b. The skin and some fibroglandular tissues are categorized into the same cluster. The tangential line at each location along the breast-air boundary curve was found (white arrows), and the dynamic searching was performed along the perpendicular direction (black arrows). The upper border is determined when the negative gradient from skin to air is found, and the lower border is determined when the positive gradient from skin to the breast fat is found. (c) The obtained segmentation results of the skin.

Figure 6

Fibroglandular tissue segmentation result. (a) The segmented breast after skin exclusion, by subtracting the skin shown in Fig. 5c from the original image shown in Fig. 5a. (b) The adaptive FCM clustering segmentation results performed for fibroglandular tissue segmentation. The algorithm also performs homogeneity correction. The cluster #1 is selected as the fibroglandular tissue, and cluster #2 and #3 both represent fatty tissues. (c) The breast territory and the outlined fibroglandular tissue, separately for the right and left breasts.

Figure 7

Segmentation results of one subject at five different positions (from top down, neutral, twisting to the right, twisting to the left, moving upper body forward, and moving upper body backward. In each study one image selected from the midsection of the breast at a comparable level is shown.