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: 10503039
• 负责人: Ann M Gillenwater
• 金额: $ 68.71万
• 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/10503039
• 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; Fluorescence; Formalin; Freezing; Fresh Tissue; Frozen Sections; Goals; Gold; Hematoxylin and Eosin Staining Method; Histology; Histopathology; Human Resources; Image; Image Analysis; Image-Guided Surgery; Improve Access; Lateral; Learning; Learning Module; 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; 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; Specimen; Speed; Stains; Surface; Surgeon; Survival Rate; System; Technology; Testing; Thick; Thinness; Time; Tissue Sample; Tissue imaging; Tissues; Training; Ultraviolet Rays; Variant; algorithm training; automated algorithm; base; cancer surgery; cancer survival; cellular imaging; clinical infrastructure; contrast imaging; cryostat; deep learning; design; digital; disease diagnosis; experience; fluorescence microscope; graphical user interface; health care quality; histopathological examination; improved; innovation; instrument; learning ability; learning network; low and middle-income countries; machine learning algorithm; machine learning framework; malignant mouth neoplasm; 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.

项目总结/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， 提供优质的医疗保健和治疗。例如，在手术时，肿瘤边缘理想地 通过快速冷冻切片病理学进行评估，以帮助确保完整的肿瘤切除，同时保留正常组织。 不幸的是，时间和劳动密集型的载玻片制备过程需要昂贵的设备， 专业人员，所以它不是广泛提供在许多设置，包括农村美国;即使在设置与 临床基础设施进行冷冻切片，只有一小部分的边缘是手动检查。在 在资源有限的全球环境中，病理学家的严重短缺使得提供常规的 诊断病理学因此，迫切需要负担得起的工具来支持高质量的组织病理学 在世界各地的节目。该提案的目标是利用光学制造的最新进展， 人工智能开发一种新的、负担得起的工具，深度学习扩展景深（DeepDOF） 平台，快速检查新鲜组织切除，无需大量的载玻片准备，同时提供 计算机辅助图像分析在护理点。我们将证明和验证其用于肿瘤边缘 口腔鳞状细胞癌是全球第六大常见恶性肿瘤。 在目标1中，我们将开发DeepDOF平台的关键模块，用于新鲜切除的组织的快速亚细胞成像。 组织样本将开发深度学习网络来设计和优化DeepDOF显微镜， 无需机械重新聚焦即可以亚细胞分辨率对高度不规则的组织表面（高达200 µm）进行成像;我们 将联合收割机与快速活性染料和深紫外线照明相结合，以实现高对比度成像。在目标2中， 将对DeepDOF进行临床评估，以确定其评估口腔肿瘤边缘状态的能力 手术后立即。DeepDOF术中口腔肿瘤切缘的临床工作流程 将优化评估，并通过与金标准进行比较来评估其绩效 组织病理学在目标3中，我们将开发一个机器学习框架，以识别积极的利润，并帮助 口腔手术标本的大面积细胞分辨率DeepDOF图的注释。使用临床数据 在目标1和2中获得的，我们将训练算法来完成识别关键诊断的分割任务， 特征，如核扩大和异常聚类;结果将进一步用于注释和 量化护理点的阳性边缘。总之，我们将开发第一个显微镜平台， 人工智能驱动的光学器件和算法，用于完整组织样本的快速和无载玻片组织学，我们将提供 重要的临床证据表明，DeepDOF平台可以改善口腔癌手术期间的患者护理。 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 在包括低收入和中等收入国家在内的缺乏高质量组织病理学服务的环境中，