Limits...
Quantification of Regional Breast Density in Four Quadrants Using 3D MRI-A Pilot Study.

Fwu PT, Chen JH, Li Y, Chan S, Su MY - Transl Oncol (2015)

Bottom Line: The symmetry of the quadrant BV in the left and right breasts separated by using the nipple alone, or the nipple-centroid line, was compared.Among the four quadrants, PD was the highest in the lower outer and the lowest in the upper outer (significant than the other three) quadrants (P < .05).The reorientation based on the nipple-centroid line improved the left to right quadrant symmetry, and this may provide a better standardized method to measure quantitative quadrant density.

View Article: PubMed Central - PubMed

Affiliation: Center for Functional Onco-Imaging, Department of Radiological Sciences, University of California, Irvine, CA, USA.

No MeSH data available.


Related in: MedlinePlus

A diagram illustrating the volume-preserving affine transformation to reorient a 3D breast to align with the nipple-centroid line in the L/R direction. An axial projection view is shown, with the x-axis coordinates representing the L/R direction, and the z-axis coordinates representing the A/P direction. The posterior boundary along the sternum of the woman used in the initial cut for breast segmentation is defined as the z = 0 plane. The centroid of the breast is marked by an asterisk. After the transformation, the nipple-centroid line becomes perpendicular to the z = 0 axis. Similar transformation is performed in the y-axis shown in Figure 2.
© Copyright Policy - CC BY-NC-ND
Related In: Results  -  Collection

License
getmorefigures.php?uid=PMC4562976&req=5

f0005: A diagram illustrating the volume-preserving affine transformation to reorient a 3D breast to align with the nipple-centroid line in the L/R direction. An axial projection view is shown, with the x-axis coordinates representing the L/R direction, and the z-axis coordinates representing the A/P direction. The posterior boundary along the sternum of the woman used in the initial cut for breast segmentation is defined as the z = 0 plane. The centroid of the breast is marked by an asterisk. After the transformation, the nipple-centroid line becomes perpendicular to the z = 0 axis. Similar transformation is performed in the y-axis shown in Figure 2.

Mentions: The whole breast and the fibroglandular tissue within each breast were segmented using a computer-based algorithm. Detailed step-by-step procedures and illustration examples, as well as segmentation reproducibility and error analysis results based on radiologists’ correction, were given in four methodology papers published before [11–14]. Because the breast was connected with the body without a clear boundary, one major task was to determine the superior, inferior, and the posterior-lateral boundaries so the breast could be separated from the body using a standardized criterion. The superior and inferior boundaries of the breast (that is, where the breast begins and ends) were determined by comparing the thickness of breast fat layer with the body fat layer. For the posterior-lateral boundary, in this study, we used a horizontal line through the sternum to perform the initial cut, and the fatty tissue above this line was considered as breast fat (procedures and examples shown in [13]). Because a flat-bed breast coil was used, and the subjects were small Asian women, the posterior breast laid flat on the coil and a horizontal line determined on the basis of the slice of the sternum could be applied to all imaging slices of this data set and defined as the z = 0 plane shown in Figure 1. After the three boundaries were determined, the key steps for breast segmentation were given as follows: 1) identify the chest wall muscle by applying the Fuzzy C-means clustering and b-spline curve fitting (examples are shown in [11–14]); 2) separate the left and the right breasts by using a vertical line perpendicular to the sternum in the middle of the bilateral breasts [13]; 3) exclude the skin along the breast boundary by applying dynamic searching [11]; 4) correct the bias field and intensity nonuniformity (since a surface breast coil was used) by applying the nonparametric nonuniformity normalization plus Fuzzy C-means algorithm for segmentation of fibroglandular tissue and fatty tissue (detailed procedures and examples are shown in [12]). After the breast was segmented and the intensity correction procedures were completed, the next job was to separate the fibroglandular and fatty tissues using the k-means clustering. When non–fat-suppressed images were analyzed, we have tested different settings and found that by using a total cluster number of 6, the lower three clusters for the fibroglandular tissue and the higher three clusters for the fatty tissue, this setting worked very well for most breast MRI data sets. A detailed description about the testing of the setting was given in [11], and many examples of the segmentation quality were shown in [12–14] and other breast density publications of our group.


Quantification of Regional Breast Density in Four Quadrants Using 3D MRI-A Pilot Study.

Fwu PT, Chen JH, Li Y, Chan S, Su MY - Transl Oncol (2015)

A diagram illustrating the volume-preserving affine transformation to reorient a 3D breast to align with the nipple-centroid line in the L/R direction. An axial projection view is shown, with the x-axis coordinates representing the L/R direction, and the z-axis coordinates representing the A/P direction. The posterior boundary along the sternum of the woman used in the initial cut for breast segmentation is defined as the z = 0 plane. The centroid of the breast is marked by an asterisk. After the transformation, the nipple-centroid line becomes perpendicular to the z = 0 axis. Similar transformation is performed in the y-axis shown in Figure 2.
© Copyright Policy - CC BY-NC-ND
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC4562976&req=5

f0005: A diagram illustrating the volume-preserving affine transformation to reorient a 3D breast to align with the nipple-centroid line in the L/R direction. An axial projection view is shown, with the x-axis coordinates representing the L/R direction, and the z-axis coordinates representing the A/P direction. The posterior boundary along the sternum of the woman used in the initial cut for breast segmentation is defined as the z = 0 plane. The centroid of the breast is marked by an asterisk. After the transformation, the nipple-centroid line becomes perpendicular to the z = 0 axis. Similar transformation is performed in the y-axis shown in Figure 2.
Mentions: The whole breast and the fibroglandular tissue within each breast were segmented using a computer-based algorithm. Detailed step-by-step procedures and illustration examples, as well as segmentation reproducibility and error analysis results based on radiologists’ correction, were given in four methodology papers published before [11–14]. Because the breast was connected with the body without a clear boundary, one major task was to determine the superior, inferior, and the posterior-lateral boundaries so the breast could be separated from the body using a standardized criterion. The superior and inferior boundaries of the breast (that is, where the breast begins and ends) were determined by comparing the thickness of breast fat layer with the body fat layer. For the posterior-lateral boundary, in this study, we used a horizontal line through the sternum to perform the initial cut, and the fatty tissue above this line was considered as breast fat (procedures and examples shown in [13]). Because a flat-bed breast coil was used, and the subjects were small Asian women, the posterior breast laid flat on the coil and a horizontal line determined on the basis of the slice of the sternum could be applied to all imaging slices of this data set and defined as the z = 0 plane shown in Figure 1. After the three boundaries were determined, the key steps for breast segmentation were given as follows: 1) identify the chest wall muscle by applying the Fuzzy C-means clustering and b-spline curve fitting (examples are shown in [11–14]); 2) separate the left and the right breasts by using a vertical line perpendicular to the sternum in the middle of the bilateral breasts [13]; 3) exclude the skin along the breast boundary by applying dynamic searching [11]; 4) correct the bias field and intensity nonuniformity (since a surface breast coil was used) by applying the nonparametric nonuniformity normalization plus Fuzzy C-means algorithm for segmentation of fibroglandular tissue and fatty tissue (detailed procedures and examples are shown in [12]). After the breast was segmented and the intensity correction procedures were completed, the next job was to separate the fibroglandular and fatty tissues using the k-means clustering. When non–fat-suppressed images were analyzed, we have tested different settings and found that by using a total cluster number of 6, the lower three clusters for the fibroglandular tissue and the higher three clusters for the fatty tissue, this setting worked very well for most breast MRI data sets. A detailed description about the testing of the setting was given in [11], and many examples of the segmentation quality were shown in [12–14] and other breast density publications of our group.

Bottom Line: The symmetry of the quadrant BV in the left and right breasts separated by using the nipple alone, or the nipple-centroid line, was compared.Among the four quadrants, PD was the highest in the lower outer and the lowest in the upper outer (significant than the other three) quadrants (P < .05).The reorientation based on the nipple-centroid line improved the left to right quadrant symmetry, and this may provide a better standardized method to measure quantitative quadrant density.

View Article: PubMed Central - PubMed

Affiliation: Center for Functional Onco-Imaging, Department of Radiological Sciences, University of California, Irvine, CA, USA.

No MeSH data available.


Related in: MedlinePlus