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Automated chest wall line detection for whole-breast segmentation in sagittal breast MR images
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10.1118/1.4793255
/content/aapm/journal/medphys/40/4/10.1118/1.4793255
http://aip.metastore.ingenta.com/content/aapm/journal/medphys/40/4/10.1118/1.4793255

Figures

Image of FIG. 1.
FIG. 1.

The problem of breast segmentation in MR imaging. (a) A breast MRI slice. (b) The segmentation consists of identifying the air-breast interface (left-side contour) and CWL (right-side contour). (c) The final segmented breast.

Image of FIG. 2.
FIG. 2.

Examples of breast MR images elaborating on different challenging aspects of the CWL detection. In these images the fibroglandular (e.g., dense) tissue appears darker than fat in the breast. The relevant areas of concern are highlighted by rectangles. The intensity contrast around the CWLs is low in (a) and (b). CWL discontinuity and highly dense tissue are present in (b) and (c).

Image of FIG. 3.
FIG. 3.

An example of the ROI selection for searching for the CWL. (a) The left and right lines represent the anterior and posterior separation lines, respectively. (b) The selected ROI is defined as the general chest wall region.

Image of FIG. 4.
FIG. 4.

Three representative examples of Canny edge maps and the extracted edges corresponding to the CWL from original images, the anisotropic diffusion and bilateral filtered images. The first column [(a), (d), (g)] shows the original MR image, the second column [(b), (e), (h)] shows the anisotropic diffusion filtered image, and the third column [(c), (f), (i)] shows the bilateral filtered image. Each row shows results for one of the three representative cases demonstrating the synergistic strengths of the three image inputs in finding a complete edge corresponding to the CWL; specifically, the second example shows the strength of the anisotropic diffusion filter, while the third shows the strength of the bilateral filter.

Image of FIG. 5.
FIG. 5.

Three examples demonstrating the edge linking algorithm on (a) an original image, (b) an anisotropic diffusion filtered image, and (c) a bilateral filtered image. Each example first shows the located intermittent partial edges and then the single-linked complete edge corresponding to the CWL.

Image of FIG. 6.
FIG. 6.

Generation of the CWL reference. (a)–(c) The three CWL maps of the original, anisotropic diffusion, and bilateral filtered image inputs, respectively. Each CWL map represents the superimposed segmentation of the CWL candidates from the nonboundary slices for that breast. (d) The shared CWL map. (e) The CWL reference (solid curve). Note that the CWL map and the shared CWL map are 2D images and the CWL reference is a corresponding 2D curve.

Image of FIG. 7.
FIG. 7.

CWL candidate (left-side curve of each plot) matching to the CWL reference (right-side curve of each plot) via the dynamic time warping algorithm. Dash lines depict nonlinear alignment of points. (a) CWL of the original image, w 1 = 0.24. (b) CWL of the anisotropic diffusion filtered image, w 2 = 0.10. (c) CWL of the bilateral filtered image, w 3 = 0.06. The CWL candidate in (c) is the final CWL segmentation. In these plots the CWL reference is horizontally shifted 35 pixels from its actual position for better visualization of the alignment.

Image of FIG. 8.
FIG. 8.

Final breast segmentation (closed contour) for the three examples shown in Fig. 2 .

Image of FIG. 9.
FIG. 9.

Examples of (a) manual segmentation contour and (b) automated segmentation contour. (c) The superimposition of the manual and automated CWL, including a zoomed-in local portion of the CWLs for better visualization.

Image of FIG. 10.
FIG. 10.

Representative results with 3D visualization for the manual and automated segmentation. Rows 1–4 show selected examples for each BI-RADS density category, with increasing fibroglandular tissue density. (a) Automated breast segmentation contour. (b) Manually segmented breast. (c) Manual segmentation volume. (d) Automated segmentation volume. (e) Union of the two volumes shown in (c) and (d).

Image of FIG. 11.
FIG. 11.

Linear regression of segmentation performance versus breast volume: (a) OP, (b) DP, (c) COP, and (d) CDD. A larger breast volume leads to higher OP and lower DP. The R 2 is 0.25, 0.25, 0.07, and 0.17 for OP, DP, COP, and CDD, respectively, suggesting that segmentation accuracy is not significantly accounted for by the variation of breast volume.

Image of FIG. 12.
FIG. 12.

Correlation of the volume between the segmented left and right breasts. The Pearson correlation coefficient (r) is 0.9996 for (a) the manual segmentation and 0.9995 for (b) the automated segmentation, indicating strong bilateral agreement.

Image of FIG. 13.
FIG. 13.

Robustness analysis of the segmentation performance variation for the four validation metrics, (a) OP, (b) DP, (c) COP, and (d) CDD, with respect to the varying range of the four parameters: relative breast area ratio threshold, iteration number, conduction coefficient, and bilateral radius. The legend is shown on top of the figure for all of the 4 plots and the X axis ticks in each of the plots refer to the 4 parameter values (P1, P2, P3, and P4) shown in the corresponding legend items.

Tables

Generic image for table
TABLE I.

Overall segmentation accuracy and corresponding averaged accuracies for each of the 4 ACR BI-RADS density categories. Data format: mean (std).

Generic image for table
TABLE II.

Segmentation performance comparison of the proposed synergistic use of multiple image inputs with the individual use of each single input averaged over all the 60 cases. Data format: mean (std).

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/content/aapm/journal/medphys/40/4/10.1118/1.4793255
2013-03-08
2014-04-18
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752b84549af89a08dbdd7fdb8b9568b5 journal.articlezxybnytfddd
Scitation: Automated chest wall line detection for whole-breast segmentation in sagittal breast MR images
http://aip.metastore.ingenta.com/content/aapm/journal/medphys/40/4/10.1118/1.4793255
10.1118/1.4793255
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