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
复制标题
DOI：
10.1118/1.4829496
复制
发表时间：
2013-12-01
期刊：
影响因子：
3.8
通讯作者：
Kontos, Despina
中科院分区：
文献类型：
--
作者：
Wu, Shandong;Weinstein, Susan P.;Kontos, Despina
目的：乳腺磁共振成像（MRI）在乳腺癌的临床治疗中起着重要作用。研究表明，纤维腺的相对量（即，在MR图像中量化的乳房中的（致密）组织可以预测患乳腺癌的风险，特别是对于高危女性。因此，乳腺MRI中的纤维腺体组织和体积密度估计的自动分割可能是有用的乳腺癌risk assessment.Methods：在这项工作中，作者开发和验证了一个完全自动化的分割算法，即，一个图集辅助模糊C-均值（FCM-Atlas）方法，估计乳腺MRI中的纤维腺体组织的体积量。FCM-Atlas是一种在逐切片基础上工作的2D分割方法。首先将FCM聚类应用于每个2D MR切片的强度空间，以产生纤维腺体组织的初始体素似然图。然后，结合先前学习的纤维腺体组织似然图谱来细化初始FCM似然图以实现增强的分割，从该增强的分割中，纤维腺体组织的绝对体积（|FGT|）和相对量（即，百分比）|FGT|计算相对于整个乳房体积（FGT%）。作者的方法通过60个3D双侧乳房MRI扫描（120个乳房）的代表性数据集进行评估，这些数据集涵盖了美国放射学会乳房成像报告和数据系统的全乳房密度范围。将自动分割与由两位经验丰富的乳腺成像放射科医师获得的手动分割进行比较。分割性能通过线性回归、Pearson相关系数、Student配对t检验和Dice相似系数（DSC）进行评估。|FGT|.当与两位读者的手动分割的平均值相比时，所提出的FCM-图谱方法实现了FGT%的r = 0.92和FGT%的r = 0.93的相关性。|FGT|自动分割在统计学上无显著差异（FGT% p = 0.46，|FGT|).对于读取器1、读取器2和FCM-Atlas，左侧乳房和右侧乳房之间的FGT%的双侧相关性分别为0.94、0.92和0.95;同样，对于FGT%，左侧乳房和右侧乳房之间的双侧相关性分别为0.94、0.92和0.95。|FGT|，分别为0.92、0.92和0.93。对于空间分割一致性，与阅片员1相比，自动算法实现的DSC分别为0.69 +/- 0.1，阅片员2为0.61 +/- 0.1，而两名阅片员手动分割之间的DSC为0.67 +/- 0.15。额外的鲁棒性分析表明，作者的方法的分割性能是稳定的，无论是在选择不同的情况下，并改变的情况下，需要构建先验概率图谱的数量。作者的研究结果还表明，提出的FCM-Atlas方法优于常用的两类FCM-alone方法。作者的方法对于每个3D双侧MR扫描（56个切片）运行类似于5分钟，用于计算FGT%，|FGT|结论：作者的方法实现了稳健的分割，可以作为一种有效的工具，用于处理大型临床数据集，以量化乳腺MRI中的纤维腺体组织含量。它具有很大的潜力，以支持临床应用在未来，包括乳腺癌风险评估。(C)2013年美国医学物理学家协会。
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 similar to 5 min for each 3D bilateralMR scan (56 slices) for computing the FGT% and | FGT|, compared to similar to 55 min needed for manual segmentation for the same purpose.Conclusions: The authors' 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. (C) 2013 American Association of Physicists in Medicine.