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: 8442279
• 负责人: Despina Kontos
• 金额: $ 19.63万
• 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 2014-12-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/8442279
• 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; 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; screening; 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.

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一个独立风险因素。目前，乳房密度最常使用半自动图像阈值技术从乳房X线照片中量化，以分割致密组织的区域。然而，乳腺X线摄影是一种投影成像技术，它可以使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，而是允许从乳房的投影图像测量的相当粗略的基于面积的估计。数字乳腺断层合成（DBT）是一种新兴的3D X射线成像模式，其中断层乳腺图像是从多个低剂量X射线源投影重建的。知道乳腺癌的风险与乳房中纤维腺体组织的数量有关（又称乳腺癌）。乳腺密度），来自DBT图像的体积乳腺密度的测量可以提供更准确的乳腺密度测量，并最终导致更准确的风险测量。该项目将开发一种新的强大的和全自动的方法，体积乳腺密度估计DBT的基础上的一种新的算法，结合图像纹理分析与基于尺度的模糊连通性图像分割。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来将模糊连通性分割中的“纹理亲和力”的概念结合起来，以生成实质模式的相应“纹理场景”。一个基于尺度的模糊连通性算法将被应用到所获得的“纹理场景”图像，以确定均匀的局部乳腺组织结构的大小和分割的致密组织体素。通过将致密组织的相应体积除以整个乳房的体积来获得体积乳房密度测量。我们的初步数据表明，DBT中的纹理分析可以用来区分致密的脂肪乳腺组织区域，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（使用我们经验证的拟人乳房软件体模生成，其中可以控制乳房密度的真实值）和ii）从我们部门已完成的临床试验中回顾性收集的临床DBT、MRI和数字乳腺X射线摄影（DM）图像来验证我们的算法。该项目将联合收割机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接分割方面的独特专业知识，开发一种用于DBT中体积乳腺密度估计的新算法。DBT技术的快速发展和上级临床性能的潜力将决定DBT在临床实践中的新兴作用。用于从DBT图像测量体积乳腺密度的稳健且全自动化的方法可以提供用于估计乳腺癌风险的非侵入性定量成像生物标志物，其可以用于指导临床决策，以提供定制的乳腺癌筛查建议并形成预防策略，特别是对于乳腺癌高风险的女性。