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

项目简介：该项目的目标是，首次将数字病理图像生成和ASIS- 通过开发计算机辅助显微镜PathCAM无缝地融入标准临床工作流程 病理学家的系统。乍一看，数字病理学(DP)领域似乎将产生重大影响。 精准医学方面的协定。然而，位于DP核心的数字图像的生成目前还在外面 用于绝大多数临床幻灯片诊断的模拟临床工作流程。这是因为， 到目前为止，病理中的数字图像生成一直被降级为昂贵而缓慢的整体采集 标准临床工作流程之外的幻灯片成像(WSI)系统。因此，目前的模拟微处理器 作用域工作流将是目前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具有重要意义- 顽固地脱离实践。此外，无法访问WSI系统的医院和患者 常规赛不能从这些助攻中受益。我们提出的低成本解决方案基于一个简单的见解： 使用临床显微镜上常见的安装在机载摄像头上的视频馈送，摄像头和相关设备- ATE软件可以建立临床回顾的准确数字记录。通过我们的技术创新，这些 图像将被被动创建，不需要改变病理学家的行为。我们的方法将提供一个 病理学家可以实时预览他们在幻灯片上探索的内容，为他们提供关于整个画面的反馈 它们的审查和评价的背景。此外，它还允许直接集成AI辅助技术- OGES在临床回顾中。我们方法的一个变革性方面是，在审查期间收集的图像 是从不同分辨率的病理切片中选择性地捕获的，将被合并到单个 可变分辨率图像(VRI)，它包含组织切片的最佳采样，包含准确的 为了做出临床决定，需要在放大和分辨率之间取得平衡。这幅最佳图像非常适合 用于远程病理会诊的即时数据传输-无需等待单独的数字化系统 扫描幻灯片。PathCAM对实时数字帮助和远程第二意见的适用性将是 在这项工作中进行了研究。除了对临床医生的直接好处外，通过PathCAM方法产生的数据还可以 为DP技术的未来发展带来变革性。PathCAM可以将数百万张幻灯片中的图像数据降价 审查本来会丢失，并将通过广泛和立即的方式实现更公平的数据收集 通过我们与Kitware Inc.的合作进行开源部署。PathCAM还捕获了丰富的信息 关于病理学家如何做出临床决定(即诊断来源)。这些记录记录了病理学家如何 临床上跨分辨率尺度和时空注意之间的关系搜索和查看幻灯片 诊断可以支持新的自我监督人工智能算法的开发，这些算法可以学习，但不是 受人机交互数据以及新的人在环决策支持系统的限制。

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.