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.

项目摘要 这项研究的长期目标是建立一个自动图像处理的管道，以增强 尸体非对比度增强（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。目的是为此图像处理开发必要的工具集， 和特征提取。中心假设是可以丰富生物医学的信息含量 成像数据，尤其是尸体NCE CT成像的数据，用于用于总体解剖学教育和研究。 该项目背后的理由是，尸体解剖虽然是解剖学教育的重要组成部分，却是 由于样本量，基础设施，成本和时间而限制。生物医学成像可以保留标本 后代，并通过提供统计和定量信息来补充该材料 解剖结构。这项研究将尝试建立有效信息提取的工作管道 通过以下特定目的：（1）改善尸体CT中观察者间的解剖一致性 扫描； （2）开发一种方法来自动从非对比度分割解剖结构 增强的CT图像； （3）建立解剖结构的正常变化及其与之的关系 病理。该项目具有创新性，因为它适用人工智能来有效提取解剖学 来自Cadaveric NCE CT成像的信息，仅通过传统的注册来执行 - 经常失败且特定领域的依赖方法，一次作用于单个器官。此外，这 项目可以使用多物种数据来增强人类图像数据。 该项目很重要，因为它将允许学生通过两者更好地理解解剖学变化 扩大学生对更多样本的接触，同时还从中提取有用的表示和分析。 这些用于教育和研究的样本。该项目的预期结果是一种能够 通过增加软组织对比来增强解剖学教育和研究，自动细分 肾脏神经的肾脏，肝脏，下颌骨和内部部位，并对这些部门进行统计分析 器官，包括但不限于统计形状建模和形状分析。这将有一个积极的 对解剖学教育和学生保留的影响，因为它将为学生提供更广泛的范围 样本可变性信息将减少小规模的医学训练中的偏见 样本量有限，并改善医疗培训。