Breast tomosynthesis texture-based segmentation for volumetric density estimation

描述(申请人提供)：越来越多的证据表明，乳房密度是乳腺癌的一个独立风险因素。目前，乳房密度通常是通过使用半自动图像阈值技术来分割致密组织区域的乳房X光照片来量化的。然而，乳房X光摄影是一种投影成像技术，它可以显示叠加的乳腺组织的混合情况。因此，乳房X光检查不允许估计体积密度，而是根据乳房的投影图像测量相当粗略的基于区域的估计。数字乳腺断层合成(DBT)是一种新兴的三维X射线成像方式，它是从多个低剂量X射线源投影重建乳腺断层图像。知道乳腺癌的风险与乳房中纤维腺组织的数量有关(也就是乳房密度)，从DBT图像测量乳房体积密度可以提供更准确的乳房密度测量，并最终导致更准确的风险测量。该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，开发一种新的稳健且全自动的DBT乳腺体积密度估计方法。其主要思想是通过在重建的DBT图像中执行纹理分析作为第一级图像分析步骤，在模糊连通性分割中融入纹理亲和力的概念，以生成对应的实质图案的纹理场景。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，以确定均匀局部乳腺组织结构的大小，并分割密集的组织体素。通过将相应的致密组织体积除以整个乳房的体积，将得出体积乳房密度测量。我们的初步数据表明，DBT中的纹理分析可以用于区分致密和肥胖的乳房组织区域，这表明所提出的分割方法是可行的。我们建议使用i)使用我们经过验证的拟人化乳腺软件phantom生成的模拟DBT图像，其中可以控制乳房密度的基本事实，以及ii)从我科已完成的临床试验中回溯收集的临床DBT、MRI和数字乳房X光摄影(DM)图像来验证我们的算法。该项目将结合宾夕法尼亚州立大学研究人员在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体积乳腺密度估计算法。DBT技术的快速发展和卓越临床表现的潜力将决定DBT在临床实践中的新兴角色。一种从DBT图像测量乳腺体积密度的稳健和全自动化方法可以提供一种非侵入性定量成像生物标志物来估计乳腺癌风险，该生物标志物可用于指导临床决策，以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危女性。 与公共健康相关：我们设想了一种独特的环境，在这种环境中，乳腺癌风险评估和患者教育可以结合起来，使女性了解自己的个人风险，并为转诊医生提供全自动化的风险评估工具。快速发展的数字乳房断层合成(DBT)技术和卓越临床表现的潜力将决定DBT在临床实践中的新兴作用。一种强大的从DBT图像估计乳腺体积密度的全自动方法将提供一种非侵入性定量成像生物标记物，用于估计乳腺癌风险，可用于指导临床决策，提供定制的筛查建议和形成预防策略，特别是对于乳腺癌高危女性。

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. PUBLIC HEALTH RELEVANCE: We envision a unique setting in which breast cancer risk assessment and patient education can be combined to empower women with knowledge about their personal risk and provide a fully-automated risk assessment tool for referring physicians. The rapidly evolving technology of digital breast tomosynthesis (DBT) and the potential for superior clinical performance will determine the emerging role of DBT in clinical practice. A robust fully-automated method for estimating volumetric breast density from DBT images will provide a non-invasive quantitative imaging biomarker for estimating breast cancer risk that can be used to guide clinical decision making for offering customized screening recommendations and forming preventive strategies, especially for women at a high risk of breast cancer.