Breast tomosynthesis texture-based segmentation for volumetric density estimation

描述（由申请人提供）：越来越多的证据表明乳腺密度是乳腺癌的独立危险因素。目前，乳房密度最常用的量化方法是使用半自动图像阈值技术从乳房x光片中分割密集组织的区域。然而，乳房x线照相术是一种投影成像技术，可以将叠加的乳腺组织的混合物可视化。因此，乳房x光检查不能估计体积密度，只能从乳房投影图像中粗略估计基于面积的密度。数字乳房断层合成（DBT）是一种新兴的三维x射线成像方式，其中乳房断层图像是由多个低剂量x射线源投影重建的。我们知道乳腺癌的风险与乳腺纤维腺组织的数量（即乳腺密度）有关，通过DBT图像测量乳腺体积密度可以提供更准确的乳腺密度测量，并最终得出更准确的风险测量。本项目将基于一种结合图像纹理分析和基于尺度的模糊连通性图像分割的新算法，开发一种新的鲁棒和全自动的DBT体积乳房密度估计方法。主要思想是通过在重建的DBT图像中进行纹理分析，作为生成相应实质图案的“纹理场景”的第一级图像分析步骤，将“纹理亲和力”的概念纳入模糊连接分割中。将基于尺度的模糊连通性算法应用于获得的“纹理场景”图像，以确定均匀的局部乳房组织结构的大小并分割密集的组织体素。通过将致密组织的相应体积除以整个乳房的体积，可以得出乳腺体积密度测量。我们的初步数据表明，DBT中的纹理分析可以用于区分乳腺组织的致密区域和脂肪区域，表明所提出的分割方法是可行的。我们建议使用i)模拟DBT图像来验证我们的算法，这些图像是由我们经过验证的拟人化乳房软件phantom生成的，其中可以控制乳腺密度的基本真相，以及ii)从我们部门完成的临床试验中回顾性收集的临床DBT， MRI和数字乳房x线摄影（DM）图像。该项目将结合宾夕法尼亚大学研究人员在DBT图像纹理分析和模糊连通性分割方面的独特专业知识，开发一种新的DBT乳房体积密度估计算法。快速发展的DBT技术和潜在的卓越临床表现将决定DBT在临床实践中的新兴作用。从DBT图像中测量乳腺体积密度的强大且全自动的方法可以提供一种非侵入性的定量成像生物标志物，用于估计乳腺癌风险，可用于指导临床决策，提供定制的乳腺癌筛查建议和形成预防策略，特别是对于乳腺癌高风险女性。

DESCRIPTION (provided by applicant): Growing evidence suggests that breast density is an independent risk factor for breast cancer. Currently, breast density is most commonly quantified from mammograms using semi-automated image thresholding techniques to segment the area of the dense tissue. Mammography, however, is a projection imaging technique that visualizes the addmixture of superimposed breast tissues. Therefore, mammograms do not allow estimating volumetric density but a rather rough area-based estimate measured from the projection image of the breast. Digital breast tomosynthesis (DBT) is an emerging 3D x-ray imaging modality in which tomographic breast images are reconstructed from multiple low-dose x-ray source projections. Knowing that the risk of breast cancer is associated with the amount of fibroglandular tissue in the breast (a.k.a. breast density), measures of volumetric breast density from DBT images could provide more accurate measures of breast density and ultimately result in more accurate measures of risk. This project will develop a new robust and fully-automated method for volumetric breast density estimation in DBT based on a novel algorithm that combines image texture analysis with scale-based fuzzy connectedness image segmentation. The main idea is to incorporate the notion of "texture-affinity" in fuzzy-connectedness segmentation by performing texture analysis in the reconstructed DBT images as a first-level image analysis step for generating the corresponding "texture-scene" of the parenchymal pattern. A scale-based fuzzy-connectedness algorithm will be applied to the obtained "texture-scene" image to determine the size of homogeneous local breast tissue structures and segment the dense tissue voxels. A volumetric breast density measure will be derived by dividing the corresponding volume of dense tissue to that of the entire breast. Our preliminary data suggest that texture analysis in DBT can be used to distinguish the dense from the fatty breast tissue regions, indicating that the proposed segmentation approach is feasible. We propose to validate our algorithm using i) simulated DBT images, generated using our validated anthropomorphic breast software phantom, in which ground truth for breast density can be controlled, and ii) clinical DBT, MRI and digital mammography (DM) images collected retrospectively from clinical trials that have been completed in our department. This project will combine the unique expertise of Penn investigators in DBT image texture analysis and fuzzy-connectedness segmentation to develop a novel algorithm for volumetric breast density estimation in DBT. The rapidly evolving technology of DBT and the potential for superior clinical performance will determine the emerging role of DBT in clinical practice. A robust and fully-automated method for measuring volumetric breast density from DBT images could provide a non-invasive quantitative imaging biomarker for estimating breast cancer risk that could be used to guide clinical decision making for offering customized breast cancer screening recommendations and forming preventive strategies, especially for women at high risk of breast cancer.