Visual Search in 3D Medical Imaging Modalities

3D 医学成像模式中的视觉搜索

• 批准号: 10186742
• 负责人: Miguel Patricio Eckstein
• 金额: $ 33.3万
• 依托单位: UNIVERSITY OF CALIFORNIA SANTA BARBARA
• 依托单位国家: 美国
• 财政年份: 2018
• 资助国家: 美国
• 起止时间: 2018-09-15 至 2024-05-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/10186742
• 关键词: 3-Dimensional; Adopted; Adoption; Area; Back; Benchmarking; Breast Microcalcification; Cancer Detection; Cancer Etiology; Categories; Cessation of life; Clinic; Clinical; Complement; Computer Models; Data; Death Rate; Detection; Diagnostic Errors; Digital Breast Tomosynthesis; Early Diagnosis; Ensure; Evaluation; Eye; Eye Movements; Goals; Human; Image; Imaging technology; Individual; Knowledge; Lead; Lesion; Malignant Neoplasms; Malignant neoplasm of lung; Mammographic screening; Mammography; Medical Imaging; Methodology; Modeling; Modernization; Noise; Pattern; Perception; Performance; Peripheral; Positioning Attribute; Procedures; Psychophysics; Radiology Specialty; Reading; Role; Scanning; Signal Transduction; Slice; Source; Specific qualifier value; Techniques; Technology; Three Dimensional Medical Imaging; Three-Dimensional Image; Three-Dimensional Imaging; Time; Training; Vision; Visual; Visual system structure; Woman; Work; breast imaging; breast lesion; clinical imaging; clinically relevant; computerized tools; design; digital; gaze; imaging modality; malignant breast neoplasm; new technology; programs; radiologist; real time monitoring; sample fixation; three-dimensional modeling; tool; vision science; visual processing; visual search; visual tracking

Project Summary Early detection through screening mammography has decreased death rates from breast cancer. There are approximately 39 million mammogram procedures conducted each year in US. However, there are still alarmingly high error rates in radiological interpretations, with missed cancer rates ranging from 10-18 percent and false positive rates as high as 67% over a 10-year period. In order to reduce errors rates, digital breast tomosynthesis, a new 3D imaging technology intended to make cancers more visible to the radiologist, is rapidly being introduced throughout clinics in the US. However, there is no thorough understanding of the potential impact of these new 3D imaging technologies on radiological errors, and no knowledge of what eye movement strategies should be used by radiologists to minimize errors when searching through these volumes, while keeping manageable reading times. The current proposal combines expertise in medical image perception and state of the art vision science to increase the theoretical and empirical understanding of 3D search. To achieve such goal we aim: a) To understand how the types of errors detecting masses and microcalcifications are impacted by 3D search in digital breast tomosynthesis images; b) To gain an understanding of the functional impact on errors of adopting different eye movement strategies to search through 3D volumes; c) To develop a computational model of 3D search that includes foveated visual processing, scanning and drilling. The model will be used to assess the adequacy and efficiency of different eye movement strategies and to identify potential suboptimalities associated with an individual’s eye movement strategies or visual capabilities in the visual periphery. The psychophysical studies, eye tracking and computational models will be initially developed with trained non-radiologists, filtered noise and digital breast tomosynthesis phantoms. Subsequently, the findings and model will be validated with radiologists and real clinical images. If successful, the proposed studies will provide a new theoretical understanding of the types of radiological errors that occur and the functional role of search patterns on 3D search with digital breast tomosynthesis images, and provide computational tools to assess whether a radiologist’s eye movement patterns are well matched to their detection capabilities in their visual peripheral. Together, these advances can potentially help reduce errors in cancer detection. Although the proposed methodology is in the context of breast cancer and digital breast tomosynthesis, the principles investigated are potentially applicable to other areas of 3D medical images in radiology.

项目摘要 通过筛查性乳房X光检查的早期发现降低了乳腺癌的死亡率。有 美国每年进行约3900万次乳房X光检查。但仍有 放射学解释的错误率高得惊人，癌症的漏诊率从10%到18%不等。 在10年的时间里，假阳性率高达67%。为了减少错误率，数字乳房 断层合成是一种新的3D成像技术，旨在使放射科医生更容易看到癌症， 在美国的诊所中迅速推广。然而，没有彻底了解 这些新的3D成像技术对放射学错误的潜在影响，并且不知道什么眼睛 放射科医生应使用移动策略，以最大限度地减少在搜索这些 卷，同时保持可控的阅读时间。目前的提案结合了医学影像方面的专业知识， 感知和最先进的视觉科学，以增加对3D的理论和经验理解 搜索为了实现这样的目标，我们的目标是：a）了解如何检测质量和 微钙化受到数字乳腺断层合成图像中的3D搜索的影响; B）为了获得 不同眼动策略对错误搜索的功能影响 c）开发包括中央凹视觉的3D搜索的计算模型; 加工、扫描和钻孔。该模型将用于评估不同的充分性和效率 眼动策略，并识别与个人眼睛相关的潜在次优性 视觉周边的运动策略或视觉能力。心理物理学研究，眼动追踪 和计算模型将最初开发与训练有素的非放射科医生，过滤噪音和数字 乳房断层合成体模。随后，研究结果和模型将与放射科医生进行验证， 真实的临床图像。如果成功，拟议的研究将提供一个新的理论认识， 发生的放射学错误类型以及搜索模式对数字乳腺3D搜索的功能作用 断层合成图像，并提供计算工具，以评估放射科医生的眼球运动是否 图案与其视觉外围的检测能力很好地匹配。总之，这些进步 可以潜在地帮助减少癌症检测中的错误。虽然拟议的方法是在 乳腺癌和数字乳腺断层合成摄影，研究的原则可能适用于其他 放射学中的3D医学图像领域。

项目成果

Evaluation of Convolutional Neural Networks for Search in 1/f 2.8 Filtered Noise and Digital Breast Tomosynthesis Phantoms.

用于 1/f 2.8 过滤噪声和数字乳房断层合成模型搜索的卷积神经网络评估。

• DOI: 10.1117/12.2549362
• 发表时间: 2020
• 期刊: Proceedings of SPIE--the International Society for Optical Engineering
• 作者: Jonnalagadda,Aditya;Lago,MiguelA;Barufaldi,Bruno;Bakic,PredragR;Abbey,CraigK;Maidment,AndrewD;Eckstein,MiguelP
• 通讯作者: Eckstein,MiguelP

A 2D Synthesized Image Improves the 3D Search for Foveated Visual Systems.

2D 合成图像改进了注视点视觉系统的 3D 搜索。

• DOI: 10.1109/tmi.2023.3246005
• 发表时间: 2023
• 期刊: IEEE transactions on medical imaging
• 影响因子: 10.6
• 作者: Klein,DeviS;Lago,MiguelA;Abbey,CraigK;Eckstein,MiguelP
• 通讯作者: Eckstein,MiguelP