Deep learning microscope for slide-free and digital histology

用于无载玻片和数字组织学的深度学习显微镜

• 批准号: 10664026
• 负责人: Ann M Gillenwater
• 金额: $ 70.11万
• 依托单位: RICE UNIVERSITY
• 依托单位国家: 美国
• 财政年份: 2022
• 资助国家: 美国
• 起止时间: 2022-07-12 至 2027-03-31
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/10664026
• 关键词: Address; Algorithms; Anatomy; Area; Artificial Intelligence; Cellular Morphology; Classification; Clinical; Clinical Data; Clinical Research; Computer Assisted; Computer-Assisted Diagnosis; Consumption; Diagnosis; Diagnostic; Dyes; Ensure; Equipment; Excision; Formalin; Freezing; Fresh Tissue; Frozen Sections; Goals; Hematoxylin and Eosin Staining Method; Histology; Histopathology; Human Resources; Image; Image Analysis; Image-Guided Surgery; Improve Access; Lateral; Light Microscope; Lighting; Malignant Neoplasms; Manuals; Maps; Masks; Mechanics; Methods; Microscope; Microscopy; Microtome - medical device; Monitor; Mouth Neoplasms; Normal tissue morphology; Nuclear; Operative Surgical Procedures; Optics; Oral Surgical Procedures; Paraffin; Pathologist; Pathology; Patient Care; Patient-Focused Outcomes; Patients; Performance; Play; Population; Preparation; Procedures; Process; Protocols documentation; Recurrence; Resected; Resolution; Resource-limited setting; Resources; Role; Rural; Sampling; Scanning; Services; Slice; Slide; Specific qualifier value; Specimen; Speed; Stains; Surface; Surgeon; Survival Rate; System; Technology; Testing; Thick; Thinness; Time; Tissue Sample; Tissue imaging; Tissues; Training; Ultraviolet Rays; Variant; absorption; algorithm training; automated algorithm; cancer surgery; cancer survival; cellular imaging; clinical infrastructure; contrast imaging; cryostat; deep learning; design; digital; disease diagnosis; experience; fabrication; fluorescence microscope; graphical user interface; health care quality; histopathological examination; improved; innovation; instrument; learning network; light emission; low and middle-income countries; machine learning algorithm; machine learning framework; malignant mouth neoplasm; meter; mouth squamous cell carcinoma; novel; optical imaging; point of care; programs; reconstruction; research clinical testing; scalpel; survival outcome; tissue processing; tool; tumor; ultraviolet; virtual

Project summary/abstract: Anatomic histopathology plays a central role in disease diagnosis and in surgical procedure guidance to ensure delivery of quality healthcare and treatment. At the time of surgery, for example, tumor margins are ideally assessed with fast frozen section pathology to help ensure complete tumor resection while sparing normal tissue. Unfortunately, the time- and labor-intensive slide preparation process requires expensive equipment and specialized personnel, so it is not widely available in many settings including the rural US; even in settings with the clinical infrastructure to perform frozen section, only a small fraction of the margin is manually examined. In resource-limited global settings, a dire shortage of pathologists makes it more challenging to provide routine diagnostic pathology. Therefore, there is a critical need for affordable tools to support quality histopathology programs throughout the world. The goal of this proposal is to use recent advances in optical fabrication and artificial intelligence to develop a new and affordable tool, the deep learning extended depth-of-field (DeepDOF) platform, to rapidly examine fresh tissue resections without extensive slide preparation, while providing computer-aided image analysis at the point of care. We will demonstrate and validate its use for tumor margin assessment in patients with oral squamous cell carcinoma, the sixth most common malignancy worldwide. In Aim 1, we will develop key modules of the DeepDOF platform for rapid, subcellular imaging of freshly resected tissue samples. A deep learning network will be developed to design and optimize the DeepDOF microscope to image highly irregular tissue surfaces (up to 200 µm) at subcellular resolution without mechanical refocusing; we will combine it with fast vital dyes and deep ultraviolet illumination to achieve high contrast imaging. In Aim 2, we will carry out a clinical evaluation of DeepDOF to determine its ability to assess oral tumor margin status immediately following surgery. The clinical workflow of DeepDOF for intraoperative oral tumor margin assessment will be optimized, and its performance will be evaluated by comparing to gold standard histopathology. In Aim 3, we will develop a machine learning framework to identify positive margins in and assist annotation of large-area, cellular-resolution DeepDOF maps of oral surgical specimens. Using clinical data acquired in Aims 1 and 2, we will train an algorithm to complete segmentation tasks for identifying key diagnostic features such as nuclear enlargement and abnormal clustering; the results will be further used to annotate and quantify positive margins at the point of care. Taken together, we will develop a first microscopy platform with AI-driven optics and algorithms for rapid and slide-free histology of intact tissue samples, and we will provide important clinical evidence to show the DeepDOF platform can improve patient care during oral cancer surgeries. Equipped with a computer-aided image analysis, the broader impact of the DeepDOF platform extends to global settings including low- and middle-income countries that lack access to high quality histopathology services.

项目摘要/摘要： 解剖组织病理学在疾病诊断和外科手术指导中起着核心作用，以确保 提供优质的医疗保健和治疗。例如，在手术时，肿瘤边缘理想情况下是 用快速冷冻的剖面病理评估，以帮助确保在放大正常组织的同时完全切除肿瘤。 不幸的是，时间和劳动密集型的幻灯片准备过程需要昂贵的设备，并且 专业人员，因此在包括美国农村在内的许多情况下都无法广泛使用；即使在与 执行冷冻部分的临床基础设施，仅手动检查了一小部分边缘。在 资源有限的全球设置，严重的病理学家短缺使提供常规的挑战更大 诊断病理学。因此，对负担得起的工具迫切需要支持优质的组织病理学 全世界的计划。该建议的目的是利用光学制造和 人工智能开发一种新的可负担得起的工具，深度学习扩展深度（DeepDof） 平台，在提供的同时，可以快速检查新鲜的组织切除术而无需大量幻灯片准备 计算机辅助图像分析在护理点。我们将证明并验证其用于肿瘤边缘的使用 口服鳞状细胞癌患者的评估，这是全球第六个最常见的恶性肿瘤。 在AIM 1中，我们将开发深dof平台的关键模块，以快速，细胞化的刚切除的亚细胞成像 组织样品。将开发一个深度学习网络，以设计和优化深色显微镜 图像在亚细胞分辨率下高度不规则的组织表面（高达200 µm），而无需机械重新聚焦； 将其与快速生命的染料和深层紫外线照明相结合，以实现高对比度成像。在AIM 2中，我们 将对DeepDoF进行临床评估，以确定其评估口腔肿瘤状态的能力 手术后立即。术中口腔肿瘤边缘的DeepDOF的临床工作流程 评估将得到优化，并且将通过与黄金标准进行比较来评估其性能 组织病理学。在AIM 3中，我们将开发一个机器学习框架，以确定积极的利润并协助 口服外科手术标本的大面积，细胞分辨率深dof图的注释。使用临床数据 在AIMS 1和2中获得，我们将训练一种算法以完成分段任务以识别关键诊断 核扩张和异常聚类等特征；结果将进一步用于注释和 在护理点量化正缘。综上所述，我们将开发一个第一个显微镜平台 AI驱动的光学和算法，用于完整组织样品的快速和无滑动组织学，我们将提供 表明DeepDof平台的重要临床证据可以在口腔癌手术期间改善患者护理。 配备了计算机辅助图像分析，DeepDof平台的更广泛影响扩展到全球 包括缺乏高质量组织病理学服务的低收入和中等收入国家的设置。