Improving Perception in Digital Breast Tomography

改善数字乳腺断层扫描的感知

• 批准号: 10441711
• 负责人: Jeremy M Wolfe
• 金额: $ 47.43万
• 依托单位: BRIGHAM AND WOMEN'S HOSPITAL
• 依托单位国家: 美国
• 财政年份: 2016
• 资助国家: 美国
• 起止时间: 2016-09-14 至 2027-08-31
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/10441711
• 关键词: 3-Dimensional; Address; Area; Attention; Basic Science; Breast; Breast Cancer Detection; Bypass; Consumption; Data; Data Collection; Digital Breast Tomosynthesis; Digital Mammography; Eye; Eye Movements; Goals; High Prevalence; Hybrids; Image; Intervention; Learning; Lifting; Location; Low Prevalence; Malignant Neoplasms; Mammography; Measures; Medical Imaging; Methodology; Methods; Perception; Performance; Prevalence; Protocols documentation; Reader; Reading; Recommendation; Research; Scanning; Screening for cancer; Series; Slice; Speed; Techniques; Technology; Testing; Time; Triage; Uncertainty; Visual; Work; base; calcification; clinical application; clinically significant; computer aided detection; coronavirus disease; cost; detection platform; digital; effective intervention; experimental study; improved; interest; novel strategies; oculomotor behavior; pandemic disease; preservation; programs; radiologist; screening; tomography; translational potential; virtual; visual search; visual tracking

Digital Breast Tomosynthesis (DBT) is a breast cancer screening methodology in which radiologists search for cancer in 3D volumes of virtual slices through the breast. DBT performs better than classic, 2D mammography but it takes more time. Our goal is to compare different methods that could reduce the time required while maintaining or improving performance. Beyond the specific goal of improving DBT, we will uncover general principles of attention and perception that can be applied whenever the volume of images threatens to overwhelm the ability of observers to consume those images. We are particularly interested in conditions of “low target prevalence” (low percentage of positive cases). In tasks like breast cancer screening, with many images and very few clinically significant targets, interventions that are effective when tested at high prevalence in the lab may fail in the field when prevalence is much lower. There are three projects: Project 1: Self-Triage by 2D Full-field digital mammography or synthetic images: In screening for breast cancer, there will be some cases that a reader could safely declare ‘normal’ on the basis of the 2D image, alone. Is it reasonable to develop a protocol where some DBT images would be acquired but would not be examined? This “self-triage” would require a very conservative triage criterion in order to avoid triage of any positive cases, but if proven to be safe, self-triage could save significant time and might reduce false negative errors. Project 2: DBT as “hybrid search”: Breast cancer screening involves search for more than one type of target (masses and calcifications, at minimum). Research shows instances where such ‘hybrid’ search for multiple targets leads to elevated errors. We will test the hypothesis that readers are more efficient and/or more accurate if they perform separate searches for each target type. We will measure eye movements to compare search for each target type alone to search for both types together. Experiments with non-experts will investigate basic principles of search in 3D volumes of image data. Project 3: AI Targeted ‘drilling’: Eye tracking has identified two modes of search in 3D stacks of images: “drilling”, where readers move rapidly through depth (Z) while the eyes stay relatively stable in the XY plane, and “scanning”, where readers search widely in XY while moving slowly in Z. We will use eye tracking to evaluate a CAD system developed by iCAD that marks specific locations in the 2D XY image and invites readers to drill in these specific areas. Does that improve CAD performance? Summary: This program of research will produce recommendations for increasing the efficiency of breast cancer screening. Moreover, each study will produce basic science that will be generalizable to other settings and will deepen our understanding of visual search through 3D volumes of image data.

数字乳腺断层合成(DBT)是一种乳腺癌筛查方法，放射科医生在其中 在通过乳房的虚拟切片的3D卷中搜索癌症。DBT的性能要好于经典的 2D乳房X光检查，但需要更多的时间。我们的目标是比较不同的方法可以减少 保持或提高性能所需的时间。超越了改进的具体目标 DBT，我们将揭示注意力和知觉的一般原则，这些原则可以在 图像的数量可能会压倒观察者消费这些图像的能力。我们是 对“低目标流行率”(低阳性病例百分比)的情况特别感兴趣。在……里面 像乳腺癌筛查这样的任务，有很多图像，但临床上有意义的目标很少， 在实验室进行高流行率测试时有效的干预措施可能在以下情况下在现场失败 患病率要低得多。有三个项目： 项目1：通过2D全视野数字乳房X光摄影或合成图像进行自我分类：筛查 乳腺癌，将会有一些情况下，读者可以安全地宣布“正常”的基础上 单独的2D图像。开发一种协议来获取一些DBT图像是否合理 但不会被检查吗？这种“自我分类”需要一个非常保守的分类标准。 避免对任何阳性病例进行分诊，但如果证明是安全的，自我分诊可以节省大量时间和 可能会减少漏报错误。 项目2：DBT作为“混合搜索”：乳腺癌筛查涉及搜索一种以上类型 靶区(最小为肿块和钙化)。研究表明，这种“混血儿” 搜索多个目标会导致错误增加。我们将检验这一假设，即读者比 如果他们对每个目标类型执行单独的搜索，则效率和/或更准确。我们将衡量 眼球运动对比搜索每种目标类型单独搜索两种类型。 与非专家进行的实验将研究在3D体积的图像数据中搜索的基本原理。 项目3：人工智能定向‘钻探’：眼球跟踪已经在3D堆栈中确定了两种搜索模式 图片：“钻孔”，读者在深度(Z)中快速移动，同时眼睛保持相对稳定。 XY平面和“扫描”，即读者在XY方向广泛搜索，而在Z方向缓慢移动。我们将使用 眼球跟踪以评估由iCAD开发的用于标记2D XY中特定位置的CAD系统 图像，并邀请读者在这些特定领域钻研。这是否提高了CAD性能？ 摘要：本研究计划将为提高工作效率提供建议 乳腺癌筛查。此外，每项研究都将产生可推广到 并将加深我们对通过3D体积的图像数据进行视觉搜索的理解。