Segmentation of breast masses on dedicated breast computed tomography and three-dimensional breast ultrasound images

Abstract

We present and evaluate a method for the three-dimensional (3-D) segmentation of breast masses on dedicated breast computed tomography (bCT) and automated 3-D breast ultrasound images. The segmentation method, refined from our previous segmentation method for masses on contrast-enhanced bCT, includes two steps: (1) initial contour estimation and (2) active contour-based segmentation to further evolve and refine the initial contour by adding a local energy term to the level-set equation. Segmentation performance was assessed in terms of Dice coefficients (DICE) for 129 lesions on noncontrast bCT, 38 lesions on contrast-enhanced bCT, and 98 lesions on 3-D breast ultrasound (US) images. For bCT, DICE values of 0.82 and 0.80 were obtained on contrast-enhanced and noncontrast images, respectively. The improvement in segmentation performance with respect to that of our previous method was statistically significant ( $p=0.002$ ). Moreover, segmentation appeared robust with respect to the presence of glandular tissue. For 3-D breast US, the DICE value was 0.71. Hence, our method obtained promising results for both 3-D imaging modalities, laying a solid foundation for further quantitative image analysis and potential future expansion to other 3-D imaging modalities.

Keywords: active contour model; breast computed tomography; computer-aided diagnosis; image analysis; segmentation; three-dimensional automated breast ultrasound.

Fig. 1

Flowchart of the 3-D automated breast lesion segmentation method.

Fig. 2

The previous model can fail to segment lesions that are embedded in fibroglandular tissue or lesions with a complex shape. The figure shows a contour generated by our previous segmentation model.

Fig. 3

(a–d) Noncontrast-enhanced images. (e–h) Contrast-enhanced images. (b and f) Research specialist’s outlines. (c and g) Segmentations by our previous model. (d and h) Segmentations by the proposed model. Note that these eight images are of the same patient (case), and they are displayed in the central coronal plane through the lesion seed point. For this case, the proportion of fibroglandular tissue is 12%.

Fig. 4

(a) The comparison of segmentation between the proposed and previous segmentation methods on the contrast bCT benign dataset ($N=13$). (b) The comparison of segmentation between the proposed and previous segmentations on the contrast bCT malignant dataset ($N=25$). (c) The comparison of segmentation between the proposed and previous segmentations on the noncontrast bCT benign dataset ($N=49$). (d) The comparison of segmentation between the proposed and previous segmentations on the noncontrast bCT malignant dataset ($N=80$).

Fig. 5

Segmentation performance in (a) 54 mammographically occult and (b) 44 mammographically positive 3-D breast US images.

Fig. 6

Comparison of lesion DICE value for the previous and proposed segmentation models for (a) the contrast-enhanced and (b) noncontrast bCT datasets across different fibroglandular density categories.

Fig. 7

A contrast-enhanced bCT image example. (a) Original volume of interest. (b) Research specialist’s outline. (c) Segmentation result by using previous procedure. (d) Segmentation result by using proposed procedure. The proportion of fibroglandular tissue in the lesion vicinity is 29%.

Fig. 8

Three different noncontrast bCT segmentation examples for each of the fibroglandular density classes. (a–d) 8% of fibroglandular proportion (low density). (e–h) 29% fibroglandular proportion (intermediate density; this lesion is also depicted in Fig. 7). (i–l) 46% fibroglandular proportion (high density). (b, f, and j) Research specialist’s outlines. (c, g, and k) Segmentation results by previous procedure. (d, h, and l) Segmentation results by proposed procedure.

Fig. 9

Two examples of 3-D breast ultrasound segmentation on two cancerous lesions. (a–d) Mammographically occult example. (e–h) Mammographically positive example. (b and f) Medical doctor’s outlines. (c and g) Segmentation results by previous procedure. (d and h) Segmentation results by proposed procedure. Unlike traditional ultrasound images displaying in axial or sagittal plane, the images shown in this figure are displayed in coronal plane.