Breast density measurements with ultrasound tomography: a comparison with non-contrast MRI

O’Flynn, Elizabeth; Fromageau, J.; Ledger, Minty; Messa, Alessandro; D'Aquino, A; Schoemaker, Minouk J.; Schmidt, María Auxiliadora; Duric, Nebojsa; Swerdlow, Anthony; Bamber, Jeffrey C.

doi:10.1186/bcr3759

Cited by 2 publications

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“…UST holds tremendous promise for breast cancer screening and examination and UST images are preferred in clinical applications, such as quantitative breast tissues analysis [ 5 , 9 , 10 ], breast mass growing monitoring [ 6 , 11 ], and clinical pathologic diagnosis [ 12 – 15 ]. In this paper, we presented a fully automated algorithm (AUGC) for breast UST image segmentation.…”

Section: Discussionmentioning

confidence: 99%

“…As such, it aids in tumor differentiation in cases of obscured tumors or tumors located within dense breasts [ 5 , 8 – 10 ]. In addition, UST volumetric images can be applied in breast density estimation [ 6 , 11 , 12 ], medicine response analysis [ 13 ], anatomical change, and breast tumor analysis [ 14 , 15 ]. In summary, the image acquisition of UST is safe, cost-effective, and highly efficient.…”

Section: Introductionmentioning

confidence: 99%

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Automatic Segmentation of Ultrasound Tomography Image

Zhuang

et al. 2017

BioMed Research International

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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.

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Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Automatic Segmentation of Ultrasound Tomography Image

Zhuang

et al. 2017

BioMed Research International

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“…Intuitively, image segmentation enhances the breast visualization, and accordingly, the observation of suspicious region and clinical diagnosis are easier to perform. Breast segmentation also improves quantitative tissue analysis [ 9 , 10 , 11 , 12 , 13 ] and other follow-up applications [ 14 , 15 ]. It has far reaching consequences in the longitudinal analysis of UST images to facilitate the quantification of breast tissue growth during treatment delivery [ 16 , 17 , 18 , 19 ], the characterization of physical breast density using intra-patient alignment of UST and MR images [ 20 ], and the rendering of breast tumors by co-registration of B-mode images with transmission, attenuation, MR or mammographic images [ 21 , 22 ].…”

Section: Introductionmentioning

confidence: 99%

“…The algorithm is feasible for automated B-mode images and achieves an average surface deviation from manual segmentation of 2.7 mm [ 31 ]. Under this situation, manual segmentation of the breast outline has been widely accepted in ongoing studies [ 10 , 11 , 12 , 13 , 14 ]. Apart from poor time consumption, unstable outcome and intensive user interaction, an additional assumption is imposed in [ 25 , 27 ] that the breast boundary in each coronal slice can be modeled with an ellipse.…”

Section: Introductionmentioning

confidence: 99%

Efficient Segmentation of a Breast in B-Mode Ultrasound Tomography Using Three-Dimensional GrabCut (GC3D)

Zhuang

et al. 2017

Sensors

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As an emerging modality for whole breast imaging, ultrasound tomography (UST), has been adopted for diagnostic purposes. Efficient segmentation of an entire breast in UST images plays an important role in quantitative tissue analysis and cancer diagnosis, while major existing methods suffer from considerable time consumption and intensive user interaction. This paper explores three-dimensional GrabCut (GC3D) for breast isolation in thirty reflection (B-mode) UST volumetric images. The algorithm can be conveniently initialized by localizing points to form a polygon, which covers the potential breast region. Moreover, two other variations of GrabCut and an active contour method were compared. Algorithm performance was evaluated from volume overlap ratios (TO, target overlap; MO, mean overlap; FP, false positive; FN, false negative) and time consumption. Experimental results indicate that GC3D considerably reduced the work load and achieved good performance (TO = 0.84; MO = 0.91; FP = 0.006; FN = 0.16) within an average of 1.2 min per volume. Furthermore, GC3D is not only user friendly, but also robust to various inputs, suggesting its great potential to facilitate clinical applications during whole-breast UST imaging. In the near future, the implemented GC3D can be easily automated to tackle B-mode UST volumetric images acquired from the updated imaging system.

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Breast density measurements with ultrasound tomography: a comparison with non-contrast MRI

Cited by 2 publications

References 4 publications

Automatic Segmentation of Ultrasound Tomography Image

Automatic Segmentation of Ultrasound Tomography Image

Efficient Segmentation of a Breast in B-Mode Ultrasound Tomography Using Three-Dimensional GrabCut (GC3D)

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