Improving Virtual Gross Anatomy: Enhancing the Information Content of Cadaveric CT Scans

改善虚拟大体解剖：增强尸体 CT 扫描的信息内容

• 批准号: 10377915
• 负责人: Steven Lewis
• 金额: $ 3.15万
• 依托单位: STATE UNIVERSITY OF NEW YORK AT BUFFALO
• 依托单位国家: 美国
• 财政年份: 2021
• 资助国家: 美国
• 起止时间: 2021-04-01 至 2023-03-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/10377915
• 关键词: 3-Dimensional; Address; Agreement; Anatomic Models; Anatomy; Artificial Intelligence; Blood coagulation; Buffaloes; Cadaver; Characteristics; Clinical; Computer Models; Contrast Media; Coupled; Cranial Nerves; Data; Development; Diagnostic; Disease; Dissection; Education; Exposure to; Gifts; Goals; Image; Image Analysis; Image Enhancement; Imaging Techniques; Individual; Information Retrieval; Infrastructure; Institution; Kidney; Lead; Libraries; Liver; Machine Learning; Mandible; Manuals; Medical; Medical Students; Methods; Modeling; Modernization; Morphology; Nature; Noise; Organ; Outcome; Pathologic; Pathology; Patients; Population; Preservation Technique; Process; Reading; Research; Research Personnel; Sample Size; Sampling; Scanning; Shapes; Signal Transduction; Site; Specimen; Statistical Data Interpretation; Structure; Students; Testing; Time; Tissues; Training; Universities; Variant; Work; X-Ray Computed Tomography; automated segmentation; base; biomedical imaging; clinically relevant; cohort; contrast enhanced; cost; deep learning; demographics; education research; feature extraction; human imaging; image processing; image registration; improved; innovation; interest; non-invasive imaging; posters; preservation; programs; quantitative imaging; retention rate; segmentation algorithm; shape analysis; soft tissue; success; virtual

Project Summary The long-term goal of this research is to establish a pipeline for automated image processing that enhances cadaveric non-contrast enhanced (NCE) CT data and extracts meaningful models and metrics to improve anatomy research and education. The objective is to develop the necessary toolset for this image processing, and feature extraction. The central hypothesis is that it is possible to enrich the information content of biomedical imaging data, particularly that of cadaveric NCE CT imaging, for use in gross anatomy education and research. The rationale behind this project is that cadaveric dissection, while an important part of anatomy education, is limited due to sample size, infrastructure, cost, and time. Biomedical imaging can preserve specimens for posterity and be used to supplement this material by providing statistical and quantitative information from anatomical structures. This research will attempt to establish a working pipeline for efficient information extraction through the following specific aims: (1) Improving inter-observer anatomical agreement in cadaveric CT scans; (2) Develop an approach to automatically segment anatomical structures from non-contrast enhanced CT images; and (3) Establish normal variation of anatomical structures and its relationship to pathologies. This project is innovative because it applies artificial intelligence to efficiently extract anatomical information from cadaveric NCE CT imaging, which has only been performed with traditional registration- dependent methods that often fail and are domain specific, acting on a single organ at a time. In addition, this project works with multi-species data to enhance human image data. This project is significant because it will allow students to understand anatomical variation better by both expanding student exposure to more samples, while also extracting useful representations and analytics from these samples for education and research. The expected outcome of this project is a toolset that is capable of enhancing anatomy education and research by increasing soft-tissue contrast, automatically segmenting the kidneys, liver, mandible, and intraosseus sites of the cranial nerves, and performing statistical analysis on these organs, including but not limited to statistical shape modelling and shape analysis. This will have a positive impact on anatomical education and student retention because it will provide students with a broader range of sample variability information which will decrease pervading biases in medical training that result from small, limited sample sizes, and improve medical training.

项目概要 这项研究的长期目标是建立一个自动化图像处理管道，以增强 尸体非对比增强 (NCE) CT 数据并提取有意义的模型和指标以改进 解剖学研究和教育。目标是开发图像处理所需的工具集， 和特征提取。中心假设是可以丰富生物医学的信息内容 成像数据，特别是尸体 NCE CT 成像数据，用于大体解剖学教育和研究。 该项目背后的基本原理是尸体解剖虽然是解剖学教育的重要组成部分，但 由于样本大小、基础设施、成本和时间的限制。生物医学成像可以保存样本 后人并通过提供来自以下方面的统计和定量信息来补充本材料 解剖结构。这项研究将尝试建立一个有效信息提取的工作管道 通过以下具体目标： (1) 提高尸体 CT 观察者之间的解剖一致性 扫描； (2) 开发一种从非对比中自动分割解剖结构的方法 增强 CT 图像； (3) 建立解剖结构的正常变异及其与 病理学。该项目具有创新性，因为它应用人工智能来有效提取解剖结构 来自尸体 NCE CT 成像的信息，该成像仅通过传统配准进行 依赖的方法经常失败并且是特定领域的，一次作用于单个器官。此外，这 该项目使用多物种数据来增强人类图像数据。 这个项目很重要，因为它将让学生更好地理解解剖学变异 扩大学生接触更多样本的机会，同时也从中提取有用的表示和分析 这些样本用于教育和研究。该项目的预期成果是一个能够 通过增加软组织对比度、自动分割软组织来加强解剖学教育和研究 肾脏、肝脏、下颌骨和颅神经的骨内部位，并对这些部位进行统计分析 器官，包括但不限于统计形状建模和形状分析。这将产生积极的 对解剖学教育和学生保留率的影响，因为它将为学生提供更广泛的知识 样本变异性信息将减少医疗培训中因小、 限制样本量，并改善医疗培训。