AI-empowered 3D Computer Vision and Image-Omics Integration for Digital Kidney Histopathology

AI 赋能的 3D 计算机视觉和图像组学集成用于数字肾脏组织病理学

• 批准号: 10635439
• 负责人: Yuankai Huo
• 金额: $ 61.49万
• 依托单位: VANDERBILT UNIVERSITY
• 依托单位国家: 美国
• 财政年份: 2023
• 资助国家: 美国
• 起止时间: 2023-03-15 至 2027-12-31
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/10635439
• 关键词: 3-Dimensional; Adult; Algorithms; Allografting; Anatomy; Appearance; Biological Markers; Biopsy; Caring; Cell Communication; Characteristics; Chronic Kidney Failure; Classification; Clinical; Computer Assisted; Computer Vision Systems; Computer software; Decision Making; Diagnosis; Elements; Focal and Segmental Glomerulosclerosis; Future; Gene Expression Profile; Goals; Grain; Healthcare; Heterogeneity; Histopathology; Human BioMolecular Atlas Program; Image; Individual; International; Kidney; Kidney Diseases; Kidney Transplantation; Label; Lesion; Methods; Modeling; Molecular; Morphologic artifacts; Morphology; Needle biopsy procedure; Nephrotic Syndrome; Pathogenesis; Pathologist; Pathology; Patient Care; Patients; Phenotype; Prognosis; Renal Tissue; Reproducibility; Research; Scientist; Stains; Standardization; Structure; Techniques; Technology; Three-Dimensional Imaging; Tissues; Transplant Recipients; United States National Institutes of Health; Visualization; allograft rejection; analytical tool; cell type; clinical phenotype; cost; deep learning; design; digital; digital imaging; digital pathology; empowerment; glomerulosclerosis; imaging biomarker; imaging modality; kidney allograft; molecular marker; novel; personalized diagnostics; routine practice; success; supervised learning; three-dimensional visualization; tool; transcriptomics; two-dimensional; whole slide imaging

PROJECT SUMMARY Our overarching goal is to enable the AI-empowered 3D histopathological interpretation on routine digitized renal tissue biopsies, so as to (1) allow renal pathologists to perform a reproducible 3D phenotyping on serial 2D whole slide images (WSI), (2) advance the characterization of kidney-allograft rejection phenotypes on kidney transplant patients with cutting-edge 3D computer vision, and (3) equip clinical scientists with an advanced 3D spatial transcriptomics analytics tool to investigate the anatomical-molecular associated causes of chronic kidney disease (CKD). Our novel 3D histopathological interpretation, with 3D computer vision (Map3D toolkit) and 3D spatial transcriptomics, will open a new door for performing reproducible clinical phenotyping (Pheno3D toolkit), identifying and validating new 3D imaging and molecular biomarkers (GPS3D toolkit), and ultimately advancing the patient care with personalized diagnosis and prognosis options for a wide range of CKD. Despite more than 25 years of exploitation of digital pathology, the presence, significance and characteristics of 3D contextual information in renal histopathological assessment have been largely overlooked. The current 2D interpretation on renal histopathology is error-prone and less reproducible due to the heterogeneity of tissue morphologies (e.g., glomeruli, tubules, vessels) across 3D serial sections. For example, our previous study on segmental glomerulosclerosis (GS) in patients with nephrotic syndrome and idiopathic FSGS, the percent of GS increased from 31.5 +/- 6.8% to 48.0 +/- 6.6% (P < 0.025) in adults by replacing a 2D single section analysis with 3D serial section analysis. Moreover, 2D based phenotyping can also hinder the discovery of new biomarkers via state-of-the-art spatial transcriptomic techniques. As an example, a glomerulus with focal segmental glomerulosclerosis (FSGS) can have a normal appearance on a specific 2D section, which might lead to an opposite molecular finding using 2D spatial transcriptomics The core tenant of this proposal is NOT developing a new 3D imaging modality, but rather, to develop technologies that enable reproducible 3D characterization on routine 2D renal histopathological biopsies (with trivial added cost), so as to advance the care of future patients with renal diseases. To this end, we will: Aim 1. Develop novel 3D computer vision tools (Map3D) to facilitate renal pathologists in modeling, quantifying, and visualizing 3D renal histopathological tissues from routine 2D digital histopathology. Impact: Allow renal pathologists to perform a reproducible 3D phenotyping on serial 2D whole slide images (WSI). Aim 2. Develop 3D phenotyping tools (Pheno3D) to advance the characterization of kidney-allograft rejection for kidney transplant patients via 3D computer vision and self-supervised deep learning. Impact: Advance the characterization of kidney-allograft rejection phenotypes for kidney transplant patients. Aim 3. Develop 3D computer vision algorithms for 2D and 3D spatial transcriptomics (GPS3D toolkit). Impact: Equip clinical scientists an 3D spatial transcriptomics analytics tool to investigate image-omics interaction.

项目摘要 我们的总体目标是在常规数字化肾脏上启用AI授权的3D组织病理学解释 组织活检，以便（1）允许肾脏病理学家对串行2D整体进行可重现的3D表型 幻灯片图像（WSI），（2）提高肾脏肾移植表型在肾脏上的表征 具有尖端3D计算机视觉的移植患者，（3）为临床科学家配备了高级3D 空间转录组学分析工具，用于研究慢性肾脏的解剖分子相关原因 疾病（CKD）。我们的小说3D组织病理学解释，具有3D计算机视觉（MAP3D工具包）和 3D空间转录组学，将为执行可重复的临床表型打开新的门（PENO3D 工具包），识别和验证新的3D成像和分子生物标志物（GPS3D工具包），最终 通过个性化的诊断和预后选择，以推进患者护理。 尽管有超过25年的数字病理学剥削，但存在，意义和 肾脏组织病理学评估中3D上下文信息的特征在很大程度上是 被忽视。当前关于肾脏组织病理学的2D解释容易出错，并且由于 组织形态的异质性（例如，肾小球，小管，血管）跨3D串行切片。为了 例如，我们先前关于肾病综合征患者和 特发性FSG，GS的百分比从31.5 +/- 6.8％增加到48.0 +/- 6.6％（P <0.025）（P <0.025）（p <0.025） 用3D串行部分分析替换2D单部分分析。而且，基于2D的表型也可以 通过最新的空间转录组技术阻碍了新生物标志物的发现。例如， 肾小球具有局灶性节段性肾小球硬化（FSG）可以在特定2D上具有正常外观 部分，可能会导致使用2D空间转录组学的分子发现 该提案的核心租户不是开发新的3D成像方式，而是要开发 在常规2D肾脏组织病理活检中启用可重现3D表征的技术（与 琐碎的成本），以促进未来肾脏疾病患者的护理。为此，我们将： 目标1。开发新颖的3D计算机视觉工具（MAP3D），以促进肾脏病理学家建模， 从常规2D数字组织病理学中量化和可视化3D肾脏组织病理组织。 影响：允许肾脏病理学家对串行2D整个幻灯片图像（WSI）进行可再现的3D表型。 目标2。开发3D表型工具（PHENO3D）以推动肾脏同种植的表征 通过3D计算机视觉和自我监督的深度学习对肾脏移植患者的拒绝。 影响：推进肾移植患者的肾脏同向拒绝表型的表征。 目标3。为2D和3D空间转录组学（GPS3D Toolkit）开发3D计算机视觉算法。 影响：为临床科学家配备了3D空间转录组学分析工具，以研究图像词的相互作用。