Deep-learning assisted photoacoustic histology for real-time intraoperative pathological diagnosis

深度学习辅助光声组织学实时术中病理诊断

• 批准号: 10642628
• 负责人: Rui Cao
• 金额: $ 9.32万
• 依托单位: CALIFORNIA INSTITUTE OF TECHNOLOGY
• 依托单位国家: 美国
• 财政年份: 2023
• 资助国家: 美国
• 起止时间: 2023-09-01 至 2025-08-31
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/10642628
• 关键词: 3-Dimensional; Address; Algorithms; Animal Organ; Cancer Patient; Cancerous; Cell Nucleus; Cessation of life; Classification; Clinic; Clinical; Computer Assisted; Computer-Assisted Diagnosis; Cytoplasmic Protein; DNA; Detection; Development; Diagnosis; Diagnostic; Dyes; Elements; Ensure; Equipment; Evolution; Excision; Freezing; Frozen Sections; Future; Hematoxylin and Eosin Staining Method; Histology; Hospitals; Human; Human Resources; Image; Image-Guided Surgery; Imaging Techniques; Label; Lasers; Lesion; Lighting; Maintenance; Malignant Neoplasms; Masks; Methods; Microscopy; Morphologic artifacts; Oncology; Operative Surgical Procedures; Optics; Pathologic; Pathologist; Pathology; Patient-Focused Outcomes; Patients; Pattern Recognition; Phase; Preparation; Publishing; Pulse Rates; RNA; Recurrent tumor; Research Proposals; Resected; Resolution; Sampling; Scanning; Sectioning technique; Series; Signal Transduction; Slide; Specimen; Speed; Stains; Structure; Subcellular structure; Surface; Surgeon; Surgical Oncology; Surgical wound; System; Techniques; Thick; Time; Tissues; Training; Transducers; Translations; Ultraviolet Rays; absorption; animal imaging; bone; bone imaging; cancer surgery; cancer therapy; cellular imaging; clinical practice; commercialization; convolutional neural network; cost; deep learning; deep neural network; design; histopathological examination; image reconstruction; improved; meter; microscopic imaging; neural network; photoacoustic imaging; tissue processing; tool; tumor; ultraviolet; virtual

Project Summary Despite the advances in cancer treatment, surgery remains the cornerstone, and more than 80% of cancer patients have a surgical procedure at some point in their cancer evolution. In oncology surgery, intraoperative pathology provides surgical guidance and identification of tumor margins, allowing confirmation of complete tumor resection before oncology surgeons close the surgical wound and helping patients avoid a second tumor resection surgery. Most localized tumors with negative margin resection show improved patient outcomes and a lower chance of tumor recurrence. However, the intraoperative frozen section technique suffers from a series of limitations: tissue loss, compromised quality due to freezing artifacts, suboptimal cutting of fatty specimens, and inability to diagnose bony lesions. In our preliminary results, we have developed the 3D contour scanning ultraviolet photoacoustic microscopy (UV- PAM) to acquire histology-like images of thick bone specimens, which addresses the long-standing challenge of intraoperative bone histology. The rapid photoacoustic histology images of bone specimens well match the conventional histology images stained by hematoxylin and eosin (H&E), allowing pathologists to identify the cancerous features following existing pattern recognition parameters readily. Although these results showed the feasibility of intraoperative photoacoustic histology of bone specimens, the system has a relatively slow imaging speed fundamentally limited by the low laser repetition rate of UV lasers and applies only to only bone specimens. This research proposal aims to develop a high-throughput photoacoustic histology platform for pathologists and surgeons to diagnose intraoperatively and remotely with an imaging speed at least 100 times faster than any published reflection-mode UV-PAM systems. Specific Aim 1: Develop a structured illumination UV-PAM for ultrafast histology imaging of slide-free specimens. Aim 1.1. We will develop an ultrafast reflection mode UV-PAM using multifocal illumination with a single element transducer. Aim 1.2. We will design and fabricate DOEs for structured illumination UV-PAM with an extended depth of focus for slide-free specimens with irregular surfaces to allow high-throughput imaging of slide-free specimens in clinical settings. Specific Aim 2: Implement neural networks for virtual staining of photoacoustic histology and real-time intraoperative diagnosis. Aim 2.1. We will implement neural networks and unsupervised deep learning techniques to virtually stain photoacoustic images in various tissue types. The utilization of virtual stained PAM images for intraoperative diagnostic will be evaluated by pathologists in clinical practices. Aim 2.2. We will develop and train a deep learning neural network to classify tumor types and stages in different tissues using photoacoustic histology to build a computer-aided platform for real-time intraoperative diagnosis.

项目摘要 尽管癌症治疗取得了进展，但手术仍然是基石，80%以上的癌症 患者在癌症发展的某个阶段接受了外科手术。在肿瘤手术中，术中 病理学提供了手术指导和肿瘤边缘的识别，允许确认完整的 在肿瘤外科医生闭合手术伤口之前进行肿瘤切除，帮助患者避免第二个肿瘤 切除手术大多数局限性肿瘤的阴性切缘切除显示患者的预后改善， 降低肿瘤复发的几率。然而，术中冷冻切片技术存在一系列问题， 局限性：组织缺损、冷冻伪影导致的质量受损、脂肪标本切割欠佳，以及 无法诊断骨病变。 在我们的初步结果中，我们已经开发了三维轮廓扫描紫外光声显微镜（UV- PAM）获取厚骨标本的组织学样图像，解决了 术中骨组织学检查。骨标本的快速光声组织学图像与 常规组织学图像由苏木精和伊红（H&E）染色，允许病理学家识别 癌症特征容易遵循现有的模式识别参数。虽然这些结果表明， 骨标本术中光声组织学的可行性，该系统具有相对较慢的成像 速度从根本上受到UV激光器的低激光重复率的限制，并且仅适用于骨样本。 该研究提案旨在为病理学家开发高通量光声组织学平台， 外科医生可以在术中和远程诊断，成像速度比任何 公开的反射模式UV-PAM系统。 具体目标1：开发用于无载玻片超快组织学成像的结构化照明UV-PAM 标本目标1.1。我们将开发一种超快反射模式的UV-PAM，使用多焦点照明， 单元件换能器目标1.2。我们将设计和制作用于结构照明UV-PAM的DOE， 对于具有不规则表面的无载玻片样本， 临床环境中的无载玻片标本。 具体目标2：实现用于光声组织学和实时虚拟染色的神经网络 术中诊断目标2.1。我们将实现神经网络和无监督深度学习 在各种组织类型中虚拟染色光声图像的技术。虚拟染色聚丙烯酰胺的应用 用于术中诊断的图像将由病理学家在临床实践中进行评价。目标2.2。我们将 开发和训练深度学习神经网络，以分类不同组织中的肿瘤类型和阶段 光声组织学，以建立一个计算机辅助平台，实时术中诊断。