Artificial Intelligence-Based Approaches for Renal Structure Characterization in Computed Tomography Images

基于人工智能的计算机断层扫描图像中肾脏结构表征方法

• 批准号: 10040835
• 负责人: Timothy Lee Kline
• 金额: $ 11.34万
• 依托单位: MAYO CLINIC ROCHESTER
• 依托单位国家: 美国
• 财政年份: 2020
• 资助国家: 美国
• 起止时间: 2020-08-01 至 2022-04-30
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/10040835
• 关键词: Abdomen; Affect; Aging; Albuminuria; Anatomy; Area; Arteries; Artificial Intelligence; Autosomal Dominant Polycystic Kidney; Award; Biopsy; Chronic Kidney Failure; Clinic; Clinical Research; Collaborations; Communities; Computer Vision Systems; Data; Data Set; Databases; Detection; Development; Disease; Early Diagnosis; Environment; Fibrosis; Funding; Generations; Goals; Gold; Grant; Health; Hepatic Cyst; Hour; Hypertension; Image; Image Analysis; Imaging technology; Individual; K-Series Research Career Programs; Kidney; Kidney Diseases; Machine Learning; Magnetic Resonance Imaging; Measurement; Measures; Methods; Microscopic; National Institute of Diabetes and Digestive and Kidney Diseases; Nephrons; Organ; Outcome; Pathology; Patient Care; Patient imaging; Patients; Polycystic Kidney Diseases; Radiologic Finding; Renal Blood Flow; Reproducibility; Research; Research Personnel; Research Project Grants; Research Proposals; Resources; Risk; Scanning; Semantics; Services; Stenosis; Structure; Surveys; Techniques; Technology; Time; Transplantation; Tubular formation; Visit; Work; X-Ray Computed Tomography; automated analysis; automated image analysis; automated segmentation; base; clinical decision-making; clinical practice; deep learning; density; early detection biomarkers; graft failure; image processing; imaging biomarker; imaging modality; improved; interest; interstitial; kidney biopsy; learning strategy; living kidney donor; member; microscopic imaging; non-invasive imaging; novel; novel imaging technology; personalized decision; precision medicine; prognostic value; programs; radiological imaging; research clinical testing; tool

ABSTRACT The goal of this R03 Small Grant Program for NIDDK is to provide additional funding for Dr. Kline to expand upon his work on his K award and apply his expertise to new image acquisitions and problems related to renal imaging. Dr. Kline’s work has piqued the interest of many internal and external investigators and has led to recent collaborations with Drs. Rule, Denic, and Kim. Together with Dr. Erickson, this new research team has prepared this R03 proposal which takes advantage of the unique expertise of each team member. The focus of this proposal is to bridge the gap between microscopic observations and those assessable non-invasively by radiological imaging. To do this, we have established a unique dataset of renal CT imaging data and corresponding biopsy measured nephron densities. We have also generated a large database of gold-standard segmentation data of kidneys, cortical regions, and medullary pyramids. Using this existing data, we propose to: (i) develop tools for segmentation of kidneys, segmentation of individual medullary pyramids, and imputing missing parts of the kidneys outside of the imaged field-of-view in the CT image, and (ii) to establish imaging biomarkers of early CKD, and correlate macroscopic imaging findings to underlying microscopic structure. This research will be facilitated by Mayo Clinic’s outstanding clinical and research environment dedicated to improving patient care, as well as the Aging Kidney Anatomy Study (PI: Rule), which led to the generation of this unique and well characterized dataset. Dr. Kline’s background in imaging technologies and image processing makes him particularly well suited to perform this research. In addition to the above aims, near the end of this research project Dr. Kline will submit a highly competitive R01 application expanding upon the findings from this research proposal. This proposal will lead to vast improvements to current analysis workflows, as well as an improved understanding of the prognostic power of renal imaging biomarkers. Obtaining this R03 Award will greatly facilitate Dr. Kline’s transition into a prosperous independent researcher focused on developing novel imaging technologies and image analysis techniques for abdominal organ pathologies.

抽象的 NIDDK 的 R03 小额赠款计划的目标是为 Kline 博士扩大规模提供额外资金 基于他在 K 奖上的工作，并将他的专业知识应用于新的图像采集和与肾脏相关的问题 克莱恩博士的工作引起了许多内部和外部研究人员的兴趣，并导致了 最近与 Rule、Denic 和 Kim 博士以及 Erickson 博士的合作 准备了这个 R03 提案，该提案利用了每个团队成员的独特专业知识。 该提案旨在弥合微观观察与可通过非侵入性方法评估的观察之间的差距 为此，我们建立了一个独特的肾脏 CT 成像数据数据集和 我们还生成了一个大型金标准数据库。 我们提出使用现有数据的肾脏、皮质区域和髓质金字塔的分割数据。 目的：（i）开发肾脏分割、个体髓质锥体分割和插补的工具 CT 图像中成像视野之外的肾脏缺失部分，以及 (ii) 建立成像 早期 CKD 的生物标志物，并将宏观成像结果与潜在的微观结构相关联。 梅奥诊所杰出的临床和研究环境将促进研究的发展 改善患者护理以及老化肾脏解剖研究（PI：规则），这导致了 这个独特且特征明确的数据集。克莱恩博士在成像技术和图像方面的背景。 除了上述目标之外，处理使他特别适合进行这项研究。 该研究项目结束时，Kline 博士将提交一份极具竞争力的 R01 申请，该申请扩展了 该研究提案的结果将极大地改进当前的分析。 工作流程，以及更好地了解肾脏成像生物标志物的预后能力。 获得这个R03奖将极大地促进Kline博士向一​​名成功的独立研究员的转变 专注于开发腹部器官的新型成像技术和图像分析技术 病理学。