Table 1.
MRI protocols parameters.
Fig 1.
(A and B) show Extreme Slices (C) ROI was drawn on T2-W without fat saturation images for separating out the breast tissue. (D) ROI was drawn on tumor tissue. Dark green color shows the ROI contour on both images.
Fig 2.
General overview of the process for outer and inner segmentation in breast MR images.
(A) Original T2-W MRI Image. (B) Image shows points P1, P2 and P3. (C) Image after fractional Otsu thresholding. (D) Image after the morphological operation. (E) Image shows points P3 and P3’(new P3 point). (F) Image shows points P1, P2, P3, P3’, P4, P5 and fat thickness t, indicated by red arrow. (G) Image shows an overlay of the fitted B-spline curve (yellow) through P4, P3’ and P5. (H) Outer segmented mask image. (I) Outer segmented breast tissue overlaid on the original T2-W image using the proposed method. (I) and (J) show Non-Fat-Sat T2-W and Fat-Sat T2-W images after outer segmentation respectively. (K) Difference image. (L) Inner segmentation image in which FG indicated by green color, fatty tissue by orange and tumor by red color.
Fig 3.
Proposed method results for outer segmentation.
It shows outer breast segmentation results of different patients with different shapes and sizes with different positions. Segmented images of breast tissue were color overlaid on base gray scale T2-W images. Blue color shows outer segmentation.
Fig 4.
Example of overlapping results for outer segmentation.
(A) The region of difference images between manual-1 and automatic proposed method is shown by purple color whereas (B) manual-2 and automatic proposed method is shown by pink color. The region of difference between two manual segmentations is shown by cyan color(C).
Fig 5.
Example of different datasets after the morphological operation.
Segmented images show an example of leftover points (non-breast tissue part) which were removed after B-spline curve fitting. Arrows indicate non-breast tissue part.
Fig 6.
It shows inner segmentation of breast tissue into FG (green color), fatty (orange color) and tumor (red color) tissue for a single slice of different patients.
Fig 7.
Comparison between different structural MRI images.
(A), (B) and (C) show T1-W vs T2-W, PD-W vs T2-W and PD-W vs T1-W respectively. Yellow shades show overlapping between two mask whereas red and green color for present in one mask but not other mask respectively.
Fig 8.
Performance of proposed method for outer segmentation and inner (tumor) segmentation evaluated on the basis of similarity index obtained by one radiologist.
It shows the distribution of three indexes Dice, Jaccard and Sensitivity coefficient for outer breast (A) and tumor tissue (B).
Table 2.
Dice coefficient, Jaccard coefficient and sensitivity for outer segmentation.
Table 3.
Dice coefficient, Jaccard coefficient and sensitivity for tumor segmentation.
Fig 9.
Fig 9A, 9C and 9E show manual FG segmentation by a radiologist which is indicated by yellow. Fig 9B, 9D and 9F show automatic FG segmentation which is indicated by green color.
Table 4.
Dice coefficient, Jaccard coefficient and sensitivity for FG segmentation.
Fig 10.
Example for the whole breast as 3D visualization before and after outer segmentation.
(A) shows a 3D visualization of T2-W Image before outer segmentation whereas (B) shows after outer segmentation.
Table 5.
The FG tissue volume segmented within the breast region was obtained using the proposed method.
Total breast volume (cm3), FG tissue volume (cm3) and percentage of breast density were calculated. Tumor volume (cm3) was also calculated for 28 datasets.
Fig 11.
Bar graph of breast density vs age.
The figure shows bar graph plot of breast density in different age groups. Error bars represent SD of breast density within each group.
Fig 12.
Bland-Altman and correlation plot.
(A) and (B) show bland-Altman and correlation plots for left and right breast density of all the subjects.
Table 6.
Total breast volume (cm3), FG tissue volume (cm3) and percentage of breast density for both left and right separately were calculated for each 30 patient datasets.