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

基本信息

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
项目类别：
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

项目摘要

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.

项目概要/摘要：解剖组织病理学在疾病诊断和手术指导中发挥着核心作用，以确保例如，在手术时，肿瘤边缘处于理想状态。通过快速冰冻切片病理学进行评估，以帮助确保完全切除肿瘤，同时保留正常组织。不幸的是，耗时和劳力密集的载玻片制备过程需要昂贵的设备和专业人员，因此在包括美国农村在内的许多环境中，即使在有条件的环境中，它也没有广泛使用；在执行冰冻切片的临床基础设施中，仅手动检查一小部分边缘。全球环境资源有限，病理学家的严重短缺使得提供常规治疗变得更具挑战性因此，迫切需要负担得起的工具来支持高质量的组织病理学。该提案的目标是利用光学制造和技术领域的最新进展。人工智能开发出一种新的、经济实惠的工具，即深度学习扩展景深 (DeepDOF) 平台，无需大量载玻片准备即可快速检查新鲜组织切除物，同时提供我们将演示并验证其在肿瘤边缘的应用。对口腔鳞状细胞癌（全球第六大常见恶性肿瘤）患者的评估。在目标 1 中，我们将开发 DeepDOF 平台的关键模块，用于对新鲜切除的细胞进行快速亚细胞成像将开发一个深度学习网络来设计和优化 DeepDOF 显微镜，以达到组织样本的目的。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，无需机械重新聚焦；在 Aim 2 中，我们将其与快速活性染料和深紫外照明相结合，以实现高对比度成像。将进行 DeepDOF 的临床评估，以确定其评估口腔肿瘤边缘状态的能力手术后立即使用 DeepDOF 检测口腔肿瘤边缘的临床工作流程。优化评估，通过与金标准对比来评估其性能在目标 3 中，我们将开发一个机器学习框架来识别阳性边缘并提供协助。使用临床数据对口腔手术标本的大面积、细胞分辨率 DeepDOF 图进行注释。目标 1 和目标 2 中获得的数据，我们将训练一种算法来完成识别关键诊断的分割任务核增大、异常聚类等特征；结果将进一步用于注释和量化护理点的积极利润总之，我们将开发第一个显微镜平台。人工智能驱动的光学和算法，可对完整组织样本进行快速、无滑动的组织学分析，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。配备计算机辅助图像分析，DeepDOF 平台的影响力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环境。