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Ultrasound tomography (UST) image segmentation is fundamental in breast density estimation, medicine response analysis, and anatomical change quantification. Existing methods are time consuming and require massive manual interaction. To address these issues, an automatic algorithm based on GrabCut (AUGC) is proposed in this paper. The presented method designs automated GrabCut initialization for incomplete labeling and is sped up with multicore parallel programming. To verify performance, AUGC is applied to segment thirty-two in vivo UST volumetric images. The performance of AUGC is validated with breast overlapping metrics (Dice coefficient (D), Jaccard (J), and False positive (FP)) and time cost (TC). Furthermore, AUGC is compared to other methods, including Confidence Connected Region Growing (CCRG), watershed, and Active Contour based Curve Delineation (ACCD). Experimental results indicate that AUGC achieves the highest accuracy (D=0.9275 and J=0.8660 and FP=0.0077) and takes on average about 4 seconds to process a volumetric image. It was said that AUGC benefits large-scale studies by using UST images for breast cancer screening and pathological quantification. [ABSTRACT FROM AUTHOR]

Previous ultrasound tomography work conducted by our group showed a direct correlation between measured sound speed and physical density in vitro, and increased in vivo sound speed with increasing mammographic density, a known risk factor for breast cancer. Building on these empirical results, the purpose of this work was to explore a metric to quantify breast density using our ultrasound tomography sound speed images in a manner analogous to computer-assisted mammogram segmentation for breast density analysis. Therefore, volumetric ultrasound percent density (USPD) is determined by segmenting high sound speed areas from each tomogram using a k-means clustering routine, integrating these results over the entire volume of the breast, and dividing by whole-breast volume. First, a breast phantom comprised of fat inclusions embedded in fibroglandular tissue was scanned four times with both our ultrasound tomography clinical prototype (with 4 mm spatial resolution) and CT. The coronal transmission tomograms and CT images were analyzed using semiautomatic segmentation routines, and the integrated areas of the phantom’s fat inclusions were compared between the four repeated scans. The average variability for inclusion segmentation was ∼7% and ∼2%, respectively, and a close correlation was observed in the integrated areas between the two modalities. Next, a cohort of 93 patients was imaged, yielding volumetric coverage of the breast (45–75 sound speed tomograms/patient). The association of USPD with mammographic percent density (MPD) was evaluated using two measures: (1) qualitative, as determined by a radiologist’s visual assessment using BI-RADS Criteria and (2) quantitative, via digitization and semiautomatic segmentation of craniocaudal and mediolateral oblique mammograms. A strong positive association between BI-RADS category and USPD was demonstrated [Spearman ρ=0.69 (p<0.001)], with significant differences between all BI-RADS categories as assessed by one-way ANOVA and Scheffé posthoc analysis. Furthermore, comparing USPD to calculated mammographic density yielded moderate to strong positive associations for CC and MLO views (r2=0.75 and 0.59, respectively). These results support the hypothesis that utilizing USPD as an analogue to mammographic breast density is feasible, providing a nonionizing, whole-breast analysis. [ABSTRACT FROM AUTHOR]

Women with mammographic percent density >50% have a ∼three-fold increased risk of developing breast cancer, potentially making them screening candidates for breast MRI scanning. The purpose of this work is to introduce a new method to quantify mammographic percent density (MPD), and to compare the results with the current standard of care for breast density assessment. Craniocaudal (CC) and mediolateral oblique (MLO) mammograms for 104 patients were digitized and analyzed using an interactive computer-assisted segmentation routine implemented for two purposes: (1) to segment the breast area from background and radiographic markers, and (2) to segment dense from fatty portions of the breast. Our technique was evaluated by comparing the results to qualitative estimates determined by a certified breast radiologist using the BI-RADS Categorical Assessment (1 (fatty) to 4 (dense) scale). Statistically significant correlations (two-tailed, p<0.01) were observed between calculated MPD and BI-RADS for both CC (Spearman ρ=0.67) and MLO views (Spearman ρ=0.71). For the CC view, statistically significant differences were revealed between the mean MPD for each BI-RADS category except between fatty (BI-RADS 1) and scattered (BI-RADS 2). Finally, for the MLO views, statistically significant differences in the mean MPD between all BI-RADS categories were observed. Comparing the CC and MLO views revealed a strong positive correlation (Pearson r=0.8) in calculated MPD. In addition, an evaluation of the reproducibility of our segmentation demonstrated the average standard deviation of MPD for a subsample of eight patients, measured five times, was 1.9% (range: 0.03%–9.9%). Eliminating one misassignment reduced the average standard deviation to 0.75% (range: 0.03%–3.16%). Further analysis of ∼10% of the patient sample revealed strong agreement (ICC=0.80–0.85) in the reliability of MPD estimates for both mammographic views. Overall, these results demonstrate the feasibility of utilizing our approach for quantitative breast density segmentation, which may be useful for detecting small changes in MPD introduced through chemoprevention, diet, or other interventions. [ABSTRACT FROM AUTHOR]

Purpose: To investigate the use of the whole-breast sound speed measurement as a marker of breast density (BD), a known risk factor for breast cancer. Methods: As part of an ongoing study of breast cancer detection, 249 patients were scanned with a clinical prototype that operates on the principles of ultrasound tomography. Typically, 40-100 sound speed tomograms were reconstructed from the scan data, corresponding to the entire volume of the breast of each patient. The data were used to estimate the volume averaged sound speed (VASS) of the breast for each patient. The corresponding mammograms were used to calculate mammographic percent density (MPD) using CUMULUS software. Film mammograms were available for 164 patients while 85 digital mammograms were available for the remaining patients. Standard statistical techniques were used to determine associations of breast sound speed with a variety of mammographic measures such as percent density, area of dense tissue, and area of nondense tissue. Furthermore, associations of breast sound speed with continuous variables such as age and weight and dichotomous variables such as parity and menopausal status were also assessed. Results: VASS was found to be significantly associated with MPD. The Spearman correlation coefficient (rs) between VASS and MPD was found to be 0.77 and 0.71 for film and digital mammography, respectively. VASS was positively correlated with dense areas by mammography, both digital (rs = 0.46) and film (rs = 0.56). VASS was negatively associated with nondense area by mammography, both digital (rs = -0.58) and film (rs = -0.63). BD by all methods was less in postmenopausal than in premenopausal women. The MPD was lower in the postmenopausal group (by 6.6%, p < 0.08, for the digital group and 7.73%, p < 0.007, for the film group). The VASS was also lower in the postmenopausal group (by 15 m/s, p < 0.001 for the digital group and 8 m/s, p < 0.08, for the film group). The association of MPD with age was characterized with rs = -0.06 (p < 0.6) for digital mammography and rs = -0.53 (p < 0.002) for film mammography. For weight, the MPD associations were characterized by rs = -0.53 (p < 0.0001) for digital mammography and -0.38 (p < 0.0001) for film mammography. The association of VASS with age was rs = -0.33 (p < 0.002) for the digital group and -0.17 (p < 0.03) for the film group. For weight, the relationship was characterized with rs = -0.45 (p < 0.001) for the digital group and -0.37 (p < 0.0001) for the film group. Conclusions: The association between VASS and MPD is strong for both film and digital mammography, suggesting that VASS is a viable measure of breast density. This result sets the stage for future work that will focus on directly testing the association of VASS with breast cancer risk. [ABSTRACT FROM AUTHOR]