Comparison of breast tissue measurements using magnetic resonance imaging, digital mammography and a mathematical algorithm

Lu, Lee Jane W.; Nishino, Thomas; Johnson, Raleigh F.; Nayeem, Fatima; Brunder, Donald G.; Ju, Huangxian; Leonard, Morton H.; Grady, James J.; Khamapirad, Tuenchit

doi:10.1088/0031-9155/57/21/6903

Cited by 28 publications

(38 citation statements)

References 45 publications

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“…In the robustness analysis, we tested the segmentation performance variation in this regard and the experiment results indicate that our method is stable with respect to random permutations of different cases and a varying range (i.e., [4][5][6][7][8][9][10][11][12][13][14][15][16][17][18][19][20] of case number for constructing the atlas. This is an encouraging merit as we demonstrate that our method is not very much sensitive to the selection of the cases for constructing the atlas.…”

Section: Discussionmentioning

confidence: 99%

“…In addition, they can be timeconsuming, therefore being less practical for processing large datasets and clinical translation. Some attempts have been made for automated segmentation of fibroglandular tissue in breast MRI such as the adaptive thresholding, 16 hierarchical support vector machines (SVM), 17 Gaussian distribution curve-fitting algorithm, 18 and multiorgan-atlas methods. 10 While these methods are useful, they are limited.…”

Section: Introductionmentioning

confidence: 99%

“…Only ten cases 16 and four cases 17 were tested for validation. Several methods still rely on manual or user-assisted delineation for segmenting the whole-breast, 16,18 which is an essential step for estimating the total volume of the breast, which is needed (i.e., the denominator) for computing the FGT%. Customized MR protocols (i.e., three-point Dixon images) (Refs.…”

Section: Introductionmentioning

confidence: 99%

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Automated fibroglandular tissue segmentation and volumetric density estimation in breast MRI using an atlas‐aided fuzzy C‐means method

Weinstein

Conant

et al. 2013

Medical Physics

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Purpose: Breast magnetic resonance imaging (MRI) plays an important role in the clinical management of breast cancer. Studies suggest that the relative amount of fibroglandular (i.e., dense) tissue in the breast as quantified in MR images can be predictive of the risk for developing breast cancer, especially for high-risk women. Automated segmentation of the fibroglandular tissue and volumetric density estimation in breast MRI could therefore be useful for breast cancer risk assessment. Methods: In this work the authors develop and validate a fully automated segmentation algorithm, namely, an atlas-aided fuzzy C-means (FCM-Atlas) method, to estimate the volumetric amount of fibroglandular tissue in breast MRI. The FCM-Atlas is a 2D segmentation method working on a slice-by-slice basis. FCM clustering is first applied to the intensity space of each 2D MR slice to produce an initial voxelwise likelihood map of fibroglandular tissue. Then a prior learned fibroglandular tissue likelihood atlas is incorporated to refine the initial FCM likelihood map to achieve enhanced segmentation, from which the absolute volume of the fibroglandular tissue (|FGT|) and the relative amount (i.e., percentage) of the |FGT| relative to the whole breast volume (FGT%) are computed. The authors' method is evaluated by a representative dataset of 60 3D bilateral breast MRI scans (120 breasts) that span the full breast density range of the American College of Radiology Breast Imaging Reporting and Data System. The automated segmentation is compared to manual segmentation obtained by two experienced breast imaging radiologists. Segmentation performance is assessed by linear regression, Pearson's correlation coefficients, Student's paired t-test, and Dice's similarity coefficients (DSC). Results: The inter-reader correlation is 0.97 for FGT% and 0.95 for |FGT|. When compared to the average of the two readers' manual segmentation, the proposed FCM-Atlas method achieves a correlation of r = 0.92 for FGT% and r = 0.93 for |FGT|, and the automated segmentation is not statistically significantly different (p = 0.46 for FGT% and p = 0.55 for |FGT|). The bilateral correlation between left breasts and right breasts for the FGT% is 0.94, 0.92, and 0.95 for reader 1, reader 2, and the FCM-Atlas, respectively; likewise, for the |FGT|, it is 0.92, 0.92, and 0.93, respectively. For the spatial segmentation agreement, the automated algorithm achieves a DSC of 0.69 ± 0.1 when compared to reader 1 and 0.61 ± 0.1 for reader 2, respectively, while the DSC between the two readers' manual segmentation is 0.67 ± 0.15. Additional robustness analysis shows that the segmentation performance of the authors' method is stable both with respect to selecting different cases and to varying the number of cases needed to construct the prior probability atlas. The authors' results also show that the proposed FCM-Atlas method outperforms the commonly used two-cluster FCM-alone method. The authors' method runs at ∼5 min for each 3D bilateral MR scan (56 slices) for computing t...

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Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Automated fibroglandular tissue segmentation and volumetric density estimation in breast MRI using an atlas‐aided fuzzy C‐means method

Weinstein

Conant

et al. 2013

Medical Physics

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“…The segmentation's accuracy can be easily increased if some mathematical algorithms are applied [29,30]. With the use of MR imaging and tissue segmentation, the accuracy can be increased further, although paying a high price in manipulation and computation time [31]. For some purposes, the four aforementioned categories can be enough in view of the variation of the material constants with the fat content.…”

Section: Discussionmentioning

confidence: 99%

A polynomial hyperelastic model for the mixture of fat and glandular tissue in female breast

Calvo-Gallego

Martínez-Reina

Domı́nguez

2015

Numer Methods Biomed Eng

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In the breast of adult women, glandular and fat tissues are intermingled and cannot be clearly distinguished. This work studies if this mixture can be treated as a homogenized tissue. A mechanical model is proposed for the mixture of tissues as a function of the fat content. Different distributions of individual tissues and geometries have been tried to verify the validity of the mixture model. A multiscale modelling approach was applied in a finite element model of a representative volume element (RVE) of tissue, formed by randomly assigning fat or glandular elements to the mesh. Both types of tissues have been assumed as isotropic, quasi-incompressible hyperelastic materials, modelled with a polynomial strain energy function, like the homogenized model. The RVE was subjected to several load cases from which the constants of the polynomial function of the homogenized tissue were fitted in the least squares sense. The results confirm that the fat volume ratio is a key factor in determining the properties of the homogenized tissue, but the spatial distribution of fat is not so important. Finally, a simplified model of a breast was developed to check the validity of the homogenized model in a geometry similar to the actual one.

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“…Lu et al [8] introduce Gaussian distribution curve-fitting algorithm to compare breast density measurement in MRI and digital mammography. Wang et al [9] employ hierarchical support vector machine to compute amount of fibroglandular tissue in breast volume.…”

Section: Introductionmentioning

confidence: 99%

Automatic segmentation of breast and fibroglandular tissue in breast MRI using local adaptive thresholding

Fooladivanda

Shokouhi

Ahmadinejad

et al. 2014

2014 21th Iranian Conference on Biomedical Engineering (ICBME)

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Breast density is considered as an important risk factor associated with the development of breast cancer. Breast and fibroglandular tissue segmentation is the main step to compute breast density in Magnetic Resonance Imaging (MRI). This study presents an automatic algorithm to segment breast and fibroglandular tissue in MRI. It is a difficult task due to bias field and similar signal intensity between fibroglandular tissue and pectoral muscle. Our proposed segmentation approach has been developed based on the local adaptive thresholding to dominate on intensity inhomogeneity due to bias field and the low contrast intensity of the boundary between breast and pectoral muscle. The presented approach is validated with a dataset of 2520 bilateral axial breast MR images from 45 women that include all of Breast Imaging Reporting and Data System (BI-RADS) breast density range. Five quantitative metrics as DiceSimilarity Coefficient (DSC), Jaccard Coefficient (JC), total overlap, False Negative (FN) and False Positive (FP) are employed to compare similarity between manual and automatic segmentations. For breast segmentation, the presented approach achieves DSC, JC, total overlap, FN and FP values of 0.90, 0.82, 0.89, 0.1 and 0.09, respectively. For fibroglandular tissue segmentation, we attain DSC, JC, total overlap, FN and FP values of 0.96, 0.94, 0.98, 0.02 and 0.04, respectively.

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Comparison of breast tissue measurements using magnetic resonance imaging, digital mammography and a mathematical algorithm

Cited by 28 publications

References 45 publications

Automated fibroglandular tissue segmentation and volumetric density estimation in breast MRI using an atlas‐aided fuzzy C‐means method

Automated fibroglandular tissue segmentation and volumetric density estimation in breast MRI using an atlas‐aided fuzzy C‐means method

A polynomial hyperelastic model for the mixture of fat and glandular tissue in female breast

Automatic segmentation of breast and fibroglandular tissue in breast MRI using local adaptive thresholding

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