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.

项目总结