Improving Perception in Digital Breast Tomography

数字乳腺断层合成摄影（DBT）是一种乳腺癌筛查方法， 通过乳房的虚拟切片的3D体积搜索癌症。DBT的性能优于经典， 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， 保持或提高性能所需的时间。超越改善的具体目标 DBT，我们将揭示注意力和感知的一般原则，这些原则可以在任何时候应用。 大量的图像威胁着压倒观察者消费这些图像的能力。我们 特别关注“低目标流行率”（阳性病例百分比低）的情况。在 像乳腺癌筛查这样的任务，有很多图像，很少有临床意义的目标， 在实验室高流行率测试时有效的干预措施可能在实地失败， 流行率要低得多。有三个项目： 项目1：通过2D全视野数字乳腺X射线摄影或合成图像进行自我分类： 乳腺癌，将有一些情况下，读者可以安全地宣布'正常'的基础上， 2D图像，单独。制定一个采集DBT图像的协议是否合理 却不接受检查这种“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， 可以减少假阴性错误。 项目2：DBT作为“混合搜索”：乳腺癌筛查涉及搜索一种以上的类型 目标（肿块和钙化，至少）。研究表明，这种“混合” 对多个目标的搜索导致更高的误差。我们将测试读者更多的假设 如果它们对每种目标类型执行单独的搜索，则更有效和/或更准确。我们将测量 眼球运动以比较单独搜索每种目标类型和同时搜索两种类型。 与非专家的实验将探讨在3D图像数据量搜索的基本原则。 项目3：人工智能有针对性的“钻孔”：眼动跟踪已经确定了3D堆栈中的两种搜索模式， 图像：“钻孔”，读者快速移动通过深度（Z），而眼睛保持相对稳定， XY平面和“扫描”，读者在XY中广泛搜索，而在Z中缓慢移动。我们将使用 眼动跟踪，以评估iCAD开发的CAD系统，该系统在2D XY中标记特定位置 图像，并邀请读者钻在这些特定领域。这会提高CAD性能吗？ 摘要：本研究计划将提出提高效率的建议， 乳腺癌筛查此外，每项研究都将产生可推广到 其他设置，并将通过3D图像数据量加深我们对视觉搜索的理解。

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.