Breast tomosynthesis texture-based segmentation for volumetric density estimation

用于体积密度估计的基于乳房断层合成纹理的分割

• 批准号: 8248953
• 负责人: Despina Kontos
• 金额: $ 17.4万
• 依托单位: UNIVERSITY OF PENNSYLVANIA
• 依托单位国家: 美国
• 财政年份: 2012
• 资助国家: 美国
• 起止时间: 2012-03-09 至 2013-12-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/8248953
• 关键词: 3-Dimensional; Affinity; Algorithms; Anatomy; Area; Automation; Bilateral; Biological Markers; Breast; Breast Cancer Detection; Breast Cancer Risk Factor; Clinical; Clinical Trials; Computer software; Computers; Coupled; Data; Data Set; Digital Mammography; Dose; General Population; Goals; High Risk Woman; Image; Image Analysis; Imagery; Imaging Techniques; Knowledge; Magnetic Resonance Imaging; Mammary Gland Parenchyma; Mammography; Measures; Methods; Patient Education; Pattern; Pennsylvania; Performance; Physicians; Prevention strategy; Recommendation; Research Personnel; Risk; Risk Assessment; Risk Estimate; Role; Screening procedure; Simulate; Source; Structure; Students; Techniques; Technology; Testing; Texture; Tissues; Translations; Universities; Woman; base; breast density; cancer risk; clinical decision-making; clinical practice; density; digital; empowered; high risk; imaging Segmentation; imaging modality; malignant breast neoplasm; novel; tool; two-dimensional

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.

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，乳房密度最常通过乳房X光照片进行量化，使用半自动图像阈值技术来分割致密组织的区域。然而，乳房X线照相术是一种投影成像技术，可将叠加的乳腺组织的混合物可视化。因此，乳房X线照片不允许估计体积密度，而是根据乳房的投影图像测量的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 3D X 射线成像方式，其中乳腺断层图像是根据多个低剂量 X 射线源投影重建的。了解乳腺癌风险与乳房中纤维腺组织的数量（又称乳房密度）相关，通过 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结果，并最终得出更准确的风险测量结果。该项目将开发一种新的稳健且全自动的 DBT 体积乳腺密度估计方法，该方法基于一种新颖的算法，该算法将图像纹理分析与基于尺度的模糊连通性图像分割相结合。主要思想是通过在重建的 DBT 图像中执行纹理分析，将“纹理亲和力”的概念纳入模糊连通性分割中，作为生成实质图案的相应“纹理场景”的第一级图像分析步骤。基于尺度的模糊连通性算法将应用于所获得的“纹理场景”图像，以确定均匀局部乳腺组织结构的尺寸并分割致密组织体素。通过将致密组织的相应体积除以整个乳房的体积，可以得出乳房体积密度测量值。我们的初步数据表明，DBT 中的纹理分析可用于区分致密和脂肪乳腺组织区域，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，该图像是使用我们经过验证的拟人化乳房软件模型生成的，其中可以控制乳房密度的基本事实，以及 ii) 回顾性收集的临床 DBT、MRI 和数字乳房 X 线摄影 (DM) 图像我们部门已完成的临床试验。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，开发一种用于 DBT 体积乳腺密度估计的新算法。 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作用。一种通过 DBT 图像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，从而提供定制的乳腺癌筛查建议和制定预防策略，尤其是对于乳腺癌高危女性。 公共卫生相关性：我们设想了一个独特的环境，将乳腺癌风险评估和患者教育结合起来，使女性了解其个人风险，并为转诊医生提供全自动风险评估工具。数字乳腺断层合成 (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作用。用于根据 DBT 图像估计体积乳腺密度的强大的全自动方法将为估计乳腺癌风险提供非侵入性定量成像生物标志物，可用于指导临床决策，以提供定制的筛查建议并制定预防策略，尤其是针对女性患乳腺癌的风险很高。