Computer Assisted Identification and Volumetric Analysis of Enhancing Components

增强成分的计算机辅助识别和体积分析

• 批准号: 7661263
• 负责人: Mubarak Ali Shah
• 金额: $ 15.99万
• 依托单位: UNIVERSITY OF CENTRAL FLORIDA
• 依托单位国家: 美国
• 财政年份: 2009
• 资助国家: 美国
• 起止时间: 2009-05-01 至 2011-04-30
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/7661263
• 关键词: Archives; Area; Brain; Brain Neoplasms; Brain imaging; Clinical; Clinical Trials; Clinical Trials Cooperative Group; Computer Assisted; Computer Vision Systems; Computers; Decision Making; Development; Diagnosis; Diagnostic; Dimensions; Disease; Double-Blind Method; Evaluation; Exploratory/Developmental Grant; Gadolinium DTPA; Goals; Gold; Growth; Human; Image; Image Analysis; Knowledge; Label; Lead; Learning; Magnetic Resonance Imaging; Manuals; Measurement; Measures; Medical Imaging; Methodology; Methods; Monitor; Neoplasms; Neuraxis; Outcome; Patients; Performance; Pharmaceutical Preparations; Physicians; Pilot Projects; Postdoctoral Fellow; Randomized; Reporting; Reproducibility; Research; Research Project Grants; Resolution; Retrospective Studies; Risk; Scanning; Scientist; Services; Slice; Solid; Structure; System; Techniques; Technology; Testing; Time; Tissues; Training; Treatment Protocols; Tumor Volume; base; chemotherapeutic agent; cost; design; experience; follow-up; improved; indexing; knowledge base; method development; neoplastic; novel; patient population; public health relevance; radiologist; response; statistics; tool; treatment planning; tumor

DESCRIPTION (provided by applicant): The assessment of volumetric change of enhancing tissue is regarded as an important parameter used by clinicians when seeking to monitor the response of brain neoplasms to therapy. Unfortunately, direct computation of enhancing volume requires the manual tracing and segmentation of areas of enhancement typically extending over multiple images, which is time consuming, labor intensive, and therefore impractical. Substitute methods are widely utilized (such as bi-directional measurements). Such surrogate measurements for tumor volume become problematic. Our long-term goal is to seek an objective, computer aided diagnostic (CAD) methodology for automatic computation of tumor (enhancing tissue) volume relevant for clinical decision making. Our hypothesis behind this project is that volume of enhancing tissue can be accurately measured through the use of advanced computer vision techniques, which will lead to an effective CAD system able to assist radiologists analyzing MR brain images. The specific aims of the proposed project are 1) to improve the accuracy of the measurement of enhancing tissue by constructing high resolution 3D MR images and labeling enhancing tissue using learning based computer vision techniques, and 2) to develop a CAD system for enhancing tissue volume assessment using the designed techniques and evaluate the performance of the system on assisting radiologists for image interpretation. We believe this system will be well suited for use in patients undergoing treatment protocols/clinical trials who require short term serial imaging. It will better enable radiologists to give accurate quantitative clinical information. If the proposed research is completed successfully, the determination of enhancing tissue volume will be significantly advanced. It will enable the radiologists to rapidly provide objective, accurate, reproducible, and easily reported assessment of the tumor status. This will lead to a more rapid and reproducible assessment of neoplasm and therefore, hopefully influence patient outcomes in a positive way. The proposed research will also be applicable for usage on archived studies thereby enabling the volume of enhancing tissue to be calculated on these images as well. PUBLIC HEALTH RELEVANCE: The goal of this research is to develop a more accurate and reproducible way to measure the amount of disease present in patients suffering from brain tumors. Measurement methods currently being used are limited in accuracy, reproducibility and efficiency and hence we propose a method, if successful, will enable the computer to identify and measure brain tumors in a more automated fashion with improved accuracy. Improvements in tumor volume assessment are important for treatment planning as well as for assessing response during clinical drug trials.

描述（由申请人提供）：临床医生在监测脑肿瘤对治疗的反应时，将增强组织体积变化的评估视为一个重要参数。不幸的是，增强体积的直接计算需要手动跟踪和分割通常在多个图像上延伸的增强区域，这是耗时的、劳动密集的，因此是不切实际的。替代方法被广泛使用（如双向测量）。肿瘤体积的这种替代测量变得有问题。我们的长期目标是寻求一种客观的计算机辅助诊断（CAD）方法，用于自动计算与临床决策相关的肿瘤（增强组织）体积。我们的假设是，通过使用先进的计算机视觉技术，可以准确地测量增强组织的体积，这将导致一个有效的CAD系统，能够帮助放射科医生分析MR脑图像。 拟议项目的具体目标是：1）通过构建高分辨率3D MR图像并使用基于学习的计算机视觉技术标记增强组织来提高增强组织测量的准确性，以及2）使用设计的技术开发用于增强组织体积评估的CAD系统，并评估该系统在辅助放射科医生进行图像解释方面的性能。 我们相信该系统将非常适合用于需要短期连续成像的接受治疗方案/临床试验的患者。它将更好地使放射科医生提供准确的定量临床信息。如果拟议的研究成功完成，将大大推进增强组织体积的确定。它将使放射科医生能够快速提供客观，准确，可重复和易于报告的肿瘤状态评估。这将导致更快速和可重复的肿瘤评估，因此，希望以积极的方式影响患者的结果。拟议的研究也将适用于存档研究，从而使增强组织的体积也可以在这些图像上计算。 公共卫生关系：这项研究的目标是开发一种更准确和可重复的方法来测量患有脑肿瘤的患者中存在的疾病数量。目前使用的测量方法在准确性，可重复性和效率方面受到限制，因此我们提出了一种方法，如果成功，将使计算机能够以更自动化的方式识别和测量脑肿瘤，并提高准确性。肿瘤体积评估的改进对于治疗计划以及临床药物试验期间的反应评估是重要的。