PathCAM: connecting the digital data pipeline in diagnostic pathology with onboard-camera variable resolution slide imaging

PathCAM：将诊断病理学中的数字数据管道与机载相机可变分辨率幻灯片成像连接起来

• 批准号: 10539532
• 负责人: JONATHAN QUINCY BROWN
• 金额: $ 44.21万
• 依托单位: TULANE UNIVERSITY OF LOUISIANA
• 依托单位国家: 美国
• 财政年份: 2022
• 资助国家: 美国
• 起止时间: 2022-09-28 至 2026-06-30
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/10539532
• 关键词: Adopted; Adoption; Algorithms; Artificial Intelligence; Attention; Behavior; Clinical; Clinical Pathology; Collaborations; Collection; Color; Computer Analysis; Computer Assisted; Computer Vision Systems; Computer software; Computers; Consultations; Cytopathology; Data; Data Collection; Data Storage and Retrieval; Decision Support Systems; Detection; Development; Diagnosis; Diagnostic; Equilibrium; Evaluation; Feedback; Feeds; Foundations; Foxes; Future; Generations; Glass; Goals; Heart; Histopathology; Hospitals; Human; Human Resources; Image; Imagery; Imaging technology; Intelligence; Malignant neoplasm of prostate; Maps; Medicine; Methods; Microscope; Microscopy; Mosaicism; Outcome; Pathologist; Pathology; Patients; Performance; Process; Prognosis; Radiology Specialty; Records; Recovery; Remote Consultation; Research; Resolution; Sampling; Scanning; Second Opinions; Slide; Stream; Supervision; System; Technology; Testing; Time; Tissue Sample; Tissue imaging; Visualization; Work; analog; artificial intelligence algorithm; base; cancer diagnosis; clinical diagnosis; clinical implementation; clinical practice; cost; data exchange; data pipeline; digital; digital imaging; digital pathology; graph theory; human-in-the-loop; image reconstruction; image registration; imaging biomarker; imaging system; improved; innovation; insight; learning algorithm; lens; machine learning algorithm; multidisciplinary; novel; open source; pathology imaging; precision medicine; real-time images; shape analysis; signal processing; spatiotemporal; whole slide imaging

Project Summary: This project aims, for the first time, to bring digital pathology image generation and assis- tance seamlessly into the standard clinical workflow by developing PathCAM, a Computer Assisted Microscope system for pathologists. The field of digital pathology (DP) at first glance seems poised to make significant im- pacts in precision medicine. However, the generation of the digital images at the heart of DP is currently outside of the analog clinical workflow used for the overwhelming majority of clinical slide diagnoses. This is because, until now, digital image generation in pathology has been relegated to acquisition in expensive and slow whole slide imaging (WSI) systems, that are outside of the standard clinical workflow. Thus, the current analog micro- scope workflow will be the prevalent standard for now and the foreseeable future, meaning that any form of digital assistance for pathologists, via artificial intelligence (AI) or external second opinions, is currently signifi- cantly disconnected from practice. Moreover, hospitals and patients that do not have access to WSI systems on a routine basis cannot benefit from these assists. Our low-cost proposed solution is based on a simple insight: using video feeds from “onboard cameras” commonly installed on clinical microscopes, the camera and associ- ated software can build an exact digital record of the clinical review. Through our technical innovations, these images will be created passively, requiring no change in pathologist behavior. Our approach will provide a pathologist with a real-time preview of what they explored on a slide, giving them feedback on the “whole picture” of their review and context of their evaluation. Moreover, it allows the direct integration of AI assistance technol- ogies during clinical review. A transformative aspect to our approach is that images collected during review that are captured regioselectively from the pathology slide across resolution scales, will be combined into a single variable resolution image (VRI), which contains an optimal sampling of the tissue section, containing the exact balance of magnification and resolution needed to render the clinical decision. This optimal image is ideal for immediate data transfer for a remote pathology consultation – with no waiting for a separate digitization system to scan the slide. PathCAM’s applicability to both real-time digital assistance and remote second opinions will be studied in this work. Beyond the direct benefits to clinicians, the data produced by the PathCAM approach can be transformative for DP technologies going forward. PathCAM can cheaply image data from millions of slide reviews that would be otherwise be lost, and would enable more equitable data collection via wide and immediate open-source deployment through our collaboration with Kitware Inc. PathCAM also captures rich information about how pathologists reach clinical decisions (i.e., diagnostic provenance). These records of how pathologists clinically search and view slides across resolution scales and relationships between spatiotemporal attention and diagnosis can support the development of new self-supervised AI algorithms that learn from, but are not limited by, human-machine interaction data, as well as new human-in-the-loop decision support systems.

项目概述：该项目的目的是，第一次，把数字病理图像生成和重建， 通过开发PathCAM（一种计算机辅助显微镜）， 病理学家的系统。乍一看，数字病理学（DP）领域似乎准备做出重大改进， 精准医学协议。然而，作为DP核心的数字图像的生成目前在外部 用于绝大多数临床载玻片诊断的模拟临床工作流程。这是因为， 到目前为止，病理学中的数字图像生成一直被降级为昂贵且缓慢的整体采集， 在标准临床工作流程之外的载玻片成像（WSI）系统。因此，目前的模拟微- 范围工作流将是现在和可预见的未来的流行标准，这意味着任何形式的 通过人工智能（AI）或外部第二意见为病理学家提供数字辅助，目前意义重大， 与实践脱节。此外，无法访问WSI系统的医院和患者 日常工作不能从这些援助中受益。我们提出的低成本解决方案基于一个简单的见解： 使用来自通常安装在临床显微镜上的"机载摄像机"的视频馈送，摄像机和相关的 自动化软件可以建立临床审查的精确数字记录。通过我们的技术创新， 将被动地创建图像，不需要改变病理学家的行为。我们的方法将提供一个 病理学家，实时预览他们在幻灯片上探索的内容，为他们提供"全貌"反馈 他们的审查和他们的评价背景。此外，它还允许直接集成AI辅助技术， 在临床审查期间进行检查。我们方法的一个变革性方面是，在审查期间收集的图像， 从病理学载玻片跨分辨率尺度区域选择性地捕获，将被组合成单个 可变分辨率图像（VRI），其包含组织切片的最佳采样，包含精确的 放大率和分辨率的平衡需要做出临床决策。这一最佳图像非常适合 远程病理咨询的即时数据传输-无需等待单独的数字化系统 扫描载玻片。PathCAM对实时数字援助和远程第二意见的适用性将 在这项工作中学习。除了对临床医生的直接好处外，PathCAM方法产生的数据还可以 为未来的DP技术带来变革。PathCAM可以廉价地从数百万张幻灯片中获取图像数据 否则将丢失的审查，并将通过广泛和直接的数据收集， 通过我们与Kitware Inc.的合作，PathCAM还捕获了丰富的信息 关于病理学家如何达成临床决策（即，诊断来源）。这些记录了病理学家 临床搜索和查看幻灯片的分辨率尺度和时空注意力之间的关系 和诊断可以支持新的自监督人工智能算法的开发，这些算法可以学习，但不能 受限于人机交互数据以及新的人在回路决策支持系统。