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房密度是乳腺癌的独立危险因素。目前，使用半自动化图像阈值技术从乳房X线照片中最常定量乳房密度来分割密集组织的面积。然而，乳房X线摄影是一种投射成像技术，可视化叠加乳腺组织的添加成分。因此，乳房X线照片不允许估计体积密度，而是从乳房的投影图像中测得的基于粗糙的区域估计值。数字乳房断层合成（DBT）是一种新兴的3D X射线成像方式，其中层析成像乳房图像是从多个低剂量X射线源预测中重建的。知道乳腺癌的风险与乳房中的纤维结构组织量有关（又称乳房密度），DBT图像的体积乳房密度的测量可以提供更准确的乳房密度衡量，并最终导致更准确的风险度量。该项目将基于一种新型算法，将图像纹理分析与基于规模的模糊连接度图像分割相结合，以开发出一种新的健壮且完全自动化的方法，用于DBT中的体积乳房密度估计。主要思想是通过在重建的DBT图像中执行纹理分析作为一个第一级图像分析步骤，将“纹理亲和力”的概念纳入模糊连接性分割，以生成实质模式的相应的“纹理场景”。基于量表的模糊连接算法将应用于获得的“纹理场景”图像，以确定均匀的局部乳腺组织结构的大小并分段密集的组织体素。通过将相应的密集组织划分为整个乳房的乳房密度度量。我们的初步数据表明，DBT中的纹理分析可用于将致密乳腺组织区域区分开，表明所提出的分割方法是可行的。我们建议使用i）使用经过验证的拟人化型乳房软件幻影验证算法，其中可以控制乳房密度的地面真相，ii）临床DBT，MRI和数字乳房摄影和数字乳房摄影（DM）图像从已在我们部门完成的临床试验中收集到的临床图像。该项目将结合宾夕法尼亚研究者在DBT图像纹理分析中的独特专业知识和模糊连接性分割，以开发一种新型算法，用于DBT中的体积乳腺密度估计。 DBT的快速发展的技术以及出色的临床性能的潜力将决定DBT在临床实践中的新兴作用。一种可用于测量DBT图像的体积乳房密度的健壮且完全自动化的方法，可以提供一种非侵入性的定量成像生物标志物，用于估算乳腺癌风险，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性策略，尤其是对乳腺癌高风险的女性。