Automated fibroglandular tissue segmentation and volumetric density estimation in breast MRI using an atlas‐aided fuzzy C‐means method. Issue 12 (13th November 2013)

• Record Type: Journal Article
• Authors: Wu, Shandong; Weinstein, Susan P.; Conant, Emily F.; Kontos, Despina
• Is Part Of: Medical physics. Volume 40:Issue 12(2013)
• Page Start: n/a
• Page End: n/a
• Publication Date: 2013-11-13
• Subjects: Clinical applications -- Segmentation -- Logic and set theory -- Cancer -- Probability theory, stochastic processes, and statistics; biological organs -- biological tissues -- biomedical MRI -- cancer -- fuzzy set theory -- image segmentation -- learning (artificial intelligence) -- medical image processing -- probability -- regression analysis; magnetic resonance imaging (MRI) -- breast -- fibroglandular tissue -- segmentation -- atlas -- fuzzy C‐means (FCM); Involving electronic [emr] or nuclear [nmr] magnetic resonance, e.g. magnetic resonance imaging -- Biological material, e.g. blood, urine; Haemocytometers -- In which a programme is changed according to experience gained by the computer itself during a complete run; Learning machines -- Digital computing or data processing equipment or methods, specially adapted for specific applications -- Image data processing or generation, in general -- Inference methods or devices; Medical imaging -- Magnetic resonance imaging -- Medical image segmentation -- Medical magnetic resonance imaging -- Tissues -- Cluster analysis -- Cancer -- Testing procedures -- Tissue engineering -- Mammography; Medical physics -- Periodicals; Medical physics; Geneeskunde; Natuurkunde; Toepassingen; Biophysics; Periodicals; Periodicals; Electronic journals; 610.153
• DOI: 10.1118/1.4829496
• Languages: English
• ISSNs: 0094-2405

Abstract

Abstract : 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 authorsˈ 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, Pearsonˈs correlation coefficients, Studentˈs paired t ‐test, and Diceˈs 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 authorsˈ 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 authorsˈ results also show that the proposed FCM‐Atlas method outperforms the commonly used two‐cluster FCM‐alone method. The authorsˈ method runs at ∼5 min for each 3D bilateral MR scan (56 slices) for computing the FGT% and |FGT|, compared to ∼55 min needed for manual segmentation for the same purpose. Conclusions: : The authorsˈ method achieves robust segmentation and can serve as an efficient tool for processing large clinical datasets for quantifying the fibroglandular tissue content in breast MRI. It holds a great potential to support clinical applications in the future including breast cancer risk assessment.