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：本项目旨在首次实现数字化病理图像生成和辅助诊断